Master Thesis Electrical Engineering April, 2012

Evaluation of Smartphones Network Performance

Chane, Mekides Abebe Tekelmariam, Hailay Mezgebo

School of Computing Blekinge Institute of Technology 371 79 Karlskrona Sweden i

This thesis is submitted to the School of Computing at Blekinge Institute of Technology in partial fulfillment of the requirements for the degree of Master of Science in Electrical Engineering. The thesis is equivalent to 20 weeks of full time studies.

Contact Information: Authors: Chane, Mekides Abebe E-mail: [email protected]

Tekelmariam, Hailay Mezgebo E-mail: [email protected]

University Advisor: Dr. Patrik Arlos School of Computing, BTH E-mail: [email protected]

School of Computing Internet : www.bth.se/com Blekinge Institute of Technology Phone : +46 455 38 50 00 371 79 Karlskrona Fax : +46 455 38 50 57 Sweden ii

ABSTRACT

Nowadays most Desktop based softwares, operating system (OS) and applications features are being adapted to Smartphones (SMPs). The simplicity and mobility of SMPs are some of the qualities which make them interesting to run different network applications. In order to develop mobile applications with efficient functionality and competitive in marketability, application developers need to have knowledge on network performance of SMPs.

In this thesis, an experimental based methodology is provided to evaluate the effect of transmission patterns on One Way Delay (OWD), throughput and packet loss across designed setup for different SMPs. Based on these metrics the SMPs have been compared with each other under the same experimental setting with relatively higher accuracy of measurement techniques. For accurate measurement DAG 3.6E card together with GPS for synchronization is used to capture traffic for further analysis. To avoid undeterministic inputs in the network, the experiment is made to be in a controlled and continuously monitored wireless local area network. Moreover the nodes or sub networks constituting the entire network are evaluated to conceive the effects on the estimated output. From this work application developers will have the opportunity to design their application according to the network performance of SMPs and users are also able to select suitable applications and SMPs.

Keywords: Smartphone, WLAN, DPMI, Network performance

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ACKNOWLEDGMENT

Many thanks to our supervisor Dr. Patrik Arlos for his continuous guidance throughout the thesis work. We would also like to thank Dr. David Erman for his feedback and all the people who participate in the thesis work by sharing their knowledge and experience.

Thank you families and friends for your support

Hailay and Mekides

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CONTENTS ABSTRACT ...... I ACKNOWLEDGMENTS...... II CONTENTS ...... III LIST OF FIGURES ...... IV LIST OF TABLES ...... V LIST OF ACRONYMS...... VI 1 INTRODUCTION ...... 8 1.1 Smartphone Overview...... 9 1.2 Related Works ...... 10 1.3 Motivation ...... 11 1.4 Contributions ...... 12 1.5 Research Questions ...... 12 1.6 Research Methodology ...... 13 1.6 Outline of the Thesis ...... 13 2 EXPERIMENTAL SETUP AND IMPLEMENTATION ...... 14

2.1 Experimental Setup ...... 14 2.1.1 Sender and Receiver Workstation ...... 15 2.1.2 GW Workstation ...... 15 2.1.3 Access Point ...... 15 2.1.4 Measurement Point ...... 15 2.2 Method ...... 16 2.2.1 OWD Estimation...... 16 2.2.2 Throughput Estimation ...... 19 2.2.3 Packet Loss Estimation ...... 20 2.2.4 OWD of GW...... 21 2.2.5 OWD of AP ...... 21 2.2.6 OWD of USB Link ...... 21 2.2.7 Throughput measurement using DPMI and Iperf ...... 23 2.2.8 Experimental Configuration ...... 24 2.2.9 Controlling the Experimental Environment ...... 26 3 RESULTS AND ANALYSIS ...... 28 3.1 Effect of GW on OWD ...... 28 3.2 Effect of AP on the OWD...... 32 3.3 Effect of USB Link on the OWD ...... 32 3.4 Evaluation and Comparisionof OWD of SMPs...... 32 3.4.1 Packet Trace Analysis of OWD...... 45 3.4.2 Comparison of OWD Distribution of the Network using the SMPs...... 47 3.5 Throughput Analysis ...... 48 3.6 Packet Loss Ratio Analysis ...... 58 3.7 Throughput difference between DPMI and Iperf...... 64 4 CONCLUSTION AND FUTURE WORK ...... 68 4.1 Conclusion...... 68 4.2 Future Work ...... 68 BIBLIOGRAPHY ...... 70 APPENDIX A ...... 74 APPENDIX B ...... 75 APPENDIX C ...... 80

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LIST OF FIGURES

Figure 2.1 Experimental setup for network performance measurement of SMP...... 14 Figure 2.2 MP special wiring scheme ...... 16 Figure 2.3 Network trace excerpt inside Mp ...... 16 Figure 2.4 OWD estimation of GW...... 21 Figure 2.5 OWD estimation of AP...... 22 Figure 2.6 OWD estimation of USB Link...... 14 Figure 2.7 Experimental setup for DPMI vs. Iperf comparison...... 23 Figure 2.8 Flow chart for statistical analysis process...... 23 Figure 3.1a OWD distribution for R=2Mbps at PL=1400 bytes (Zoomed) ...... 32 Figure 3.1b OWD distrubution exc. initial spike as a function of sequence number ...... 32 Figure 3.2 Minimum OWD of the network at R=8kbps ...... 37 Figure 3.3 Minimum OWD of SMPs in a network at R=1Mbps...... 38 Figure 3.4 Minimum OWD of SMPs in a network at R=2Mbps...... 43 Figure 3.5 OWD vs. Seq. No. at PL= 1472 bytes and R= 1Mbps...... 46 Figure 3.6 ECDF of OWD of the network at R=1Mbps and PL= 1472byte...... 48 Figure 3.7 Sender and Receiver average Throughput at R=1Mbps ...... 49 Figure 3.8 Sender and Receiver average Throughput at R=2Mbps ...... 49 Figure 3.9 Sender and Receiver average Throughput at R=5.5Mbps ...... 53 Figure 3.10 Throughput vs Time at PL=1472 and R=5.2Mbps ...... 56 Figure 3.11 CDF graph for PL=1472 byte at R=5.2Mbps...... 57 Figure 3.12 Packet Loss Ratio at the R=1Mbps...... 58 Figure 3.13 Packet Loss ratio at R=2Mbps ...... 61 Figure 3.14 Packet loss ratio at R=5.5Mbps...... 62 Figure 3.15 Throughput variation for DPMI vs. Iperf on SMP1...... 65 Figure 3.16 Throughput variations for DPMI vs. Iperf on SMP2...... 66 Figure 3.17 Throughput variation for DPMI vs. Iperf on SMP3...... 67 Figure B.1 Minimum OWD of a Network at IFG=1s ...... 75 Figure B.2 Minimum OWD of a Network at IFG=2s ...... 75 Figure B.3 Minimum OWD of a Network at IFG=8ms...... 76 Figure B.4 Histogram distribution of OWD for SMPs, GW and AP...... 76

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LIST OF TABLES

Table 2.1 Experimental parameter setting ...... 25 Table 3.1 Effect of GW on OWD of the network at R 8kbps, 1Mbps and 2Mbps...... 30 Table 3.2 Effect of AP on OWD of the network at R 8kbps,1Mbps and 2Mbps ...... 33 Table 3.3 Effect of USB Link on OWD of the network at R 8kbps ...... 33 Table 3.4 Statistical results of OWD at R=8kbps for PL=400bytes to PL=1150bytes...... 39 Table 3.5 Statistical results of OWD at R=8kbps for PL=1200bytes to PL=1450bytes ...... 40 Table 3.6 Statistical OWD delay at R=1Mbps for PL=400 bytes to PL=900 bytes ...... 41 Table 3.7 Statistical OWD delay at R=1Mbps for PL=900 bytes to PL=1450 bytes ...... 44 Table 3.8 Statistical OWD delay at R=2Mbps for PL=400 bytes to PL=1450 bytes ...... 44 Table 3.9 Sender and Receiver average Throughput at R=1Mbps ΔT=1S ...... 50 Table 3.10 Sender and Receiver average Throughput at R=2Mbps ΔT=1S...... 52 Table 3.11 Sender and Receiver average Throughput at R=5.5Mbps ΔT=1S...... 55 Table 3.12 Packet Loss Ratio at R=1Mbps ...... 59 Table 3.13 Packet Loss Ratio at R=2Mbps ...... 60 Table 3.14 Packet loss ratio at R=5.5Mbps...... 59 Table 3.15 DPMI vs Iperf average throughput output at R=1Mbps ...... 64 Table 3.16 Standard deviation DPMI vs. Iperf at R=1Mbps...... 65 Table A.1 SMPs Specification overview ...... 74 Table B.1 Stastistical results of OWD at IFG=1s for PL=400bytes to PL=1450bytes ...... 77 Table B.2 Stastistical results of OWD at IFG=2s for PL=400bytes to PL=1450bytes...... 78 Table B.3 Stastistical results of OWD at IFG=8ms for PL=400 bytes to 1150 bytes...... 79

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LIST OF ACRONYMS

1. A.P: Access Point 2. ACK: Acknowledgment 3. API : Application Programming Interface 4. Corr. Coef : Correlation Coefficient 5. CPU : Central Processor Unit 6. DAG : Data Acquisition and Generation 7. DHCP : Dynamic Host Configuration Protocol 8. DIFS : DCF Interframe Space 9. DPMI : Distributed Passive Measurement Infrastructure 10. Exp. No. : Experiment Number 11. GPS : Global Positioning System 12. GUI : Graphical User Interface 13. GW : Gateway 14. IFG : Inter Frame Gap 15. IP : Internet Protocol 16. Mar : Measurement Area 17. MArC : Measurement Area Controller 18. NTP : Network Time Protocol 19. OS : Operating System 20. PDA : Personal Digital Assistant 21. PDU : Packet Datagram Unit 22. pps : packets per second 23. QoE : Quality of Experience 24. RTT : Round Trip Time 25. Seq. No. : Sequence Number 26. SIFS : Short Interframe Space 27. SMP : Smartphone 28. SMP1 : HTC Windows 29. SMP2 : HTC Android 30. SMP3 : Xperia X1 Windows 31. SMP4 : XPERIA Neo android 32. SOF : Start of a Frame 33. Std.dev : Standard deviation

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34. TCP : Transport Control Protocol 35. UDP : User Datagram Protocol 36. UMTS : Universal Mobile Telecommunication System 37. USB : Universal Serial Bus 38. WLAN : Wireless Local Area Network

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CHAPTER 1

INTRODUCTION

The demand for Smartphone (SMPs) in the market is increasing from day to day due to addition of new features and improvements in hardware, software and applications. To cope up with the market demand, SMP manufactures are continuously improving the hardware capacity and OS efficiencies to match the processing capacities of personal computers, laptops and other advance computing devices. Currently numerous mobile applications are being developed for different purposes. In order to develop competent mobile applications with good user experience, application developers need to have an insight into the network performance of the SMPs.

The survey conducted by AdMob Mobile Metrics show that 46% of mobile Internet traffic worldwide is generated by SMPs compared to feature phones and mobile Internet data cards [1]. And most applications which require internet access are dependent on the metrics OWD, throughput and packet loss ratio. Hence, it is important for the application developers to understand how these metrics affect SMPs performance on networks.

So far, there are no comparisons and findings in measuring the performance of SMPs that employ measurement setups with a relatively high accuracy as used in [4, 5, 6 and 15]; hence in this thesis we will compare the network performance of SMPs in a controlled experimental environment specifically studying the effect of transmission patterns on OWD, throughput and packet loss in wireless local area network. From this thesis, application developers may obtain a better knowledge about network performance of SMP in order to design competent application.

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1.1 Smartphone Overview A special mobile phone that combines the features of Personal Digital Assistant (PDA) is called SMP. A PDA is a computer based small mobile hand held device that provides computing, personal organizer tool, information storage and retrieval capacity for personal and business purpose. SMP are called “Smart” for the reason that they have the functionality of a computer. There functionalities and easy to carry along in our small pocket or purse make them important in our day to day life.

Mobile phones support the features such as organizers, camera, games, and browsers. Beside the features that mobile phones have, SMP has functionalities to act as a mobile camera with high resolution, GPS navigation, GB of mass storage, radio and wireless interface i.e. UMTS and WLAN. They also have their own mobile OSs, some of the most common and well known ones are, Google Android, Nokia Symbian, Microsoft Windows phone 7 and Apple iOS [33].

The first mobile device, that somehow fulfils the term SMP definition, was IBM SIMON manufactured by IBM and BellSouth, which was shown in trade in 1992 [35]. Most currently available SMPs such as Blackberry, iPhone and Android have similar featured applications with IBM SIMON. The common used applications today in these SMPs are email, fax, world clock, calendar, calculator, address book and even a touch screen. The next notable chronologically known SMP was Nokia Communicator 9210 which was introduced by Nokia in 1996. It was the first SMP to have open OS unlike the previous predecessors. Inspired by the fact that third party applications was becoming more popular, in 2001 Handspring released Palm OS Treo. Some of the popular applications on this SMP introduced are wireless web browsing, email and contact organizer.

When Research in Motion (RIM) launched Blackberry in 2002, the market acceptance of SMPs interesting started to increase. Blackberry used an optimized wireless email, which most SMPs use this application feature in their OSs these days. The next SMP that revolutionized the course of web browsing was iPhone by APPLE in 2007.

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IPHONE was easy to use, aesthetically attractive and very good touch screen feature; it changes the course of market acceptance for the SMPs. Moreover in 2008 APPLE has established apps store which we can download different apps freely or fee based. This trend was followed by different SMP manufactures and OSs developers such as Google apps store, Nokia Ovi store, Microsoft windows mobile apps store and so on.

The full version release of an open source Android OS by Google and Open Handset Alliance in 2008 took into the highest level in market competition of OS, apps and other web applications. Currently different version of operating OS, hardware releases, and millions of apps are available on the market. Vendors and applications developers are striving to bring about new products and feature to satisfy their customers. Hence evaluation of network performance of SMPs from the user’s perspective keeps them in the open market game.

1.2 Related Works Many network performance comparisons of SMPs have been reported in different papers, their center of attention is mainly based variety of application based measurement softwares. Micheal et al. performances a study on some well known mobile OSs (Android, iPhone OS, Symbian, Windows mobile, palm OS and Blackberry) to determine which OS are the most efficient and convenient for users, developers, mobile gaming and business applications [18]. Jonathan et al. [19] compares two mobile OSs windows and symbian focusing on CPU management. The researchers from Rysavy research perform a test on the efficiency comparison of blackberry 6.0 versus Apple iPhone iOS3 and Android 2.1, the test was made on applications including e-mail, social networking, instant messaging and web browsing. From the test the researchers concluded that blackberry phones consume far less data than iPhone iOS and Android [7].

In [2], the authors compared different SMP platforms, operator networks, network protocols and application types by considering application, transport and network layer performance metrics. They used application software measurement tools to verify their 10 comparison. As reported in [12], application level measurement tools are less accurate in describing network behaviors. The author further showed that hardware measurement tools offer more accuracy in time measurement than software tools. In [4, 5] papers, One Way Delay (OWD) measurement was undertaken using measurement setup that give timestamp accuracy of less 100ns for uplink and downlink on 3G networks connecting data card as a modem. The authors pointed out that payload size and data rate affects OWD. In [6], the authors studied the processing delay of IP routers on best-effort Internet traffic generated in their experimental setup based on specification of IETF RFC2679 for OWD metric. They have also shown that the processing delay in IP routers is influenced by traffic characteristics, link status and hardware specification. The variables payload size, protocol and inter-packet time collectively described as transmission pattern in [2, 4, 5, 6, 12 and 14], are important input parameters to be considered in our thesis work.

The authors in [2] have compared cross SMP and cellular carrier of TCP and UDP throughput. Their experimental result shows that there is a difference in throughput between different SMPs platforms. In [8] two SMP platforms are studied by monitoring the traffic using passive sniffers. After measuring TCP throughput they conclude that, connection throughput is not only a function of Round Trip Time (RTT) and loss rate but also a function of application lever factors such as amount of data.

1.3 Motivation Different studies have evaluated network performance of SMPs mainly based on the application software’s that use internal hardware and OS clock timestamp [2, 18, and 19]. However the biggest challenge in such research is the accuracy and efficiency of those tools in capturing the timestamp of packets. As time is an important parameter to describe other network metrics such as throughput, OWD, RTT and so on. In this paper hardware based experimental setup which can deliver timestamp accuracy of less than 100 ns is implemented. Hence this thesis will provide good understanding on the network performance of SMPs for application developers and service providers to increases their users satisfaction.

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1.4 Contributions Some of the basic contributions of the thesis are,  To provide knowledge for application developers on OWD, throughput and packet loss ratio of a SMPs network performance for UDP protocol.  An experimental methodology to evaluate network performance of SMPs in a designed network in uplink direction.  Comparison of the network performance of SMP based on OWD, throughput and Packet Loss with respect to sending rate and payload size.  Estimation of throughput difference between the Distributed Passive Measurement Infrastructure (DPMI) and existing network performance measurement tool (Iperf) for SMPs.

1.5 Research Questions RQ1: How does transmission pattern affect OWD of SMPs and what are the factors for OWD variation? RQ1.1: How can we model the comparison of SMPs based on OWD? RQ2: How does transmission pattern affect the throughput of SMPs? RQ2.1: What is the difference in throughput of different SMPs and what factors affect this difference? RQ3: How does transmission pattern affect packet loss ratio of SMPs? RQ3.1: What is the difference in packet loss ratio among different SMPs and what factors affect the difference? RQ4: What is the difference in throughput between SMPs using DPMI and Iperf?

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1.6 Research Methodology

In this section the research methodology and approach used to address the research questions will be discussed. To answer the research questions, we studied previous research papers related to network performance of SMPs that give us good perceptive for our thesis work. In addition we studied software tools and hardware equipments that are required in commencing our experimental method. The choice of experimental methodology in this thesis instead of other methodologies such as Simulation, mathematical modeling, and others, is due the fact that the experimental results represent true network performance in real networks. To do the experiment we designed experimental setup in the laboratory where the setup was verified and validated by executing pre-experimental tests comparing with theoretical results. Our methodology is intended to be replicable for similar experimental tests. To conduct the experiment we clearly indentified and controlled the variables in the experimental process; these variables are sending rate, payload size, Inter Frame Gap (IFG), time of the day (TOD) and distance of the SMP from the directly connected wireless media. The transmission patterns sending rate and payload size are varied independently to see the effect on OWD, throughput and packet loss ratio. The experimental runs are repeated for reducing the randomization behavior of the networks and measurement processes. Moreover for external random and interference effects, the networks are controlled and monitored throughout the experimental process. Using the above experimental approaches it was possible to answer the challenges raised in our research questions.

1.7 Outline of the Thesis The thesis is organized as follows. Chapter 2 discusses the experimental setup, methods and experimental configurations. Chapter 3 explains and discuses the results found from the experiments. Finally, chapter 4 concludes the thesis by discussing the basic contributions of the thesis and proposing future works.

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CHAPTER 2

Experimental Setup and Implementations

In this chapter, the hardware and software tools used in the experimental setup will be discussed in section 2.1. In section 2.2 the methods used to approximate OWD, throughput, packet loss and experimental configuration setting will be discussed.

2.1 Experimental Setup

To observe the effect of transmission pattern on OWD, throughput and packet loss in network performance of SMPs, we designed experimental setup shown in Figure 2.1. Using existing UDP traffic generator tool, traffic is sent in uplink direction from sender to receiver. Since it’s not possible to wiretap USB cable to capture data at the MP, Gateway (GW) workstation is connected to the SMP via its USB interface. Using tethering software, the SMP was made to share wireless network of the Access Point (AP) to the GW to transfer traffic at the receive workstation.

Wiretap A Wiretap B GW SMP Receiver Sender AP

MP

Figure 2.1 Experimental setup for network performance measurement of SMP.

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Traffic sent from the sender is captured and time stamped near the sender workstation at Wiretap A and upon arrival at the receiver their copy is capture and time stamped at Wiretap B using MP.

2.1.1 Sender and Receiver Workstation

Two workstations installed with Linux OS were used as sender and receiver end points as shown in Figure 2.1. Each of them has CPU processor of Dual core 2.6GHz AMD Athlon. A UDP Traffic generator tool at the sender and UDP traffic sink at the receiver written in C++ program was installed at both ends. The sender is connected to the GW by 10Mbps Ethernet via Wiretap A. On the other side the receiver is connected to the AP through 10Mbps Ethernet via Wiretap B. Moreover at the receiver a DHCP server was configured to assign IP address to the SMPs through the AP.

The transmission pattern variables which are used by the UDP generator parameters at the sender and receiver are IFG and payload size (PL) for different experimental configuration settings it will be discussed in section 2.2.8. Additional parameters used by the UDP generator to differentiate the experiments are IP address, experimental number (e), run number (r), key id (k) and port (p) which will be varied accordingly for each experiment independent of the transmission patterns.

2.1.2 Gateway workstation

The GW is Intel Core 2 Duo CPU P8600 2.40GHZ with 4GBytes RAM and Ubuntu 10.10 version OS. Since it is not possible to wiretap USB cable of the SMP to capture packets in the MP, the sender and the GW workstations are made to be connected through the Ethernet link. Hence by doing so it is possible to wiretap the link to send copy of packets to the MP. The GW forwards traffic from the sender to the SMP using Internet Connection Sharing (ICS) through the USB cable.

2.1.3 Access Point The AP with model of DAP-1522 XTREME supports IEEE 802.11a/b/g/n modes at a speed of 11/54/65 Mbps [24]. The operating frequency is 2.4GHz (unlicensed Industrial and Scientific Medical) and 5.7GHz frequency bands. The AP was configured with IEEE 802.11b at speed of 11Mbps and 2.4GHz in which each SMP 15

WLAN interface supports this mode. It operates at four half duplex data rates which are 1Mbps, 2Mbps, 5.5Mbps and 11Mbps [17, 26, and 27]. The AP passes traffic from the SMP to receiver through its Ethernet interface.

2.1.4 Measurement Point

The MP is a Linux workstation with two Endace network monitoring DAG 3.6E cards [15]. It collects the copy of the original data near the sender and receiver wiretaps Wiretap A and Wiretap B respectively as shown in Figure 2.1 [12, 34].

2.2 Method

2.2.1 OWD Estimation OWD is the time it takes for a packet to travel from sender to receiver across a given network [4, 5 and 6]. For the packet designate as index i captured and time stamped at th the sender (Tαi) and receiver (Tβi), the OWD is estimated by subtracting the i packet th at the sender (Tαi) from i packet at the receiver (Tβi) [4, 5].

OWDi= Tβi – Tαi 2.1

In order to calculate OWD from the given parameters at the sender and receiver, the accuracy of the absolute time should be achieved. The location of the sender and receiver could be in two different places geographically. Hence in this thesis, DAG 3.6E card is used which captures packet with timestamp resolution of 60 ns at both sender and receiver. Moreover the DAG cards clocks are synchronized to a common GPS clocking system.

To avoid clock variation in data capturing a special one to one wiring scheme as in Figure 2.2 is used. This wiring scheme allows capturing data traffic and take timestamp on the same DAG 3.6E card. From Figure 2.2, as data goes from sender to receiver in uplink direction as shown as a dark line, when it pass wiretap A copy of the original data will be sent to Dag00 interface. When the same data reaches at the wiretap B it will be copied and sent to Dag01 interface.

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Figure 2.2 MP special wiring scheme

The ith indexed single packet is indentified by combination of network trace parameters captured inside the MP. These parameters comprised of traffic input parameters feed to the traffic generator as depicted in section 2.1.1, unique sequence number, IP headers, PDU length, timestamp (TS), MP ID, DAG ID, and so son. Excerpt of the network trace parameters at sender and receiver are shown in Figure 2.3.

Pkt DAG MP SRC DST EXP RUN KEY SEQ TS PL ………………. No ID ID IP IP ID ID ID No

Figure 2.3 Network Trace Excerpt inside MP

SRC IP, DST IP, EXP Id, DAG ID, RUN ID and KEY ID are important selected parameters to match the specific packet SEQ No and TS at the sender and receiver. Hence PERL Script is employed to extract and estimate OWD of each packet. Ultimately by repeating the process the statistical mean, maximum (max), minimum (min) and standard deviation (Std.dev) are estimated. The procedure is illustrated in flow chart format in Figure 2.7.

For single packet the OWD of the network is estimated using equation 2.1. For N number of samples the min, max, mean and std.dev values for the ith packet inside the MP is formulated as,

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OWDmean = 2.2

N OWDmin= i=1 2.3

N OWDmax= i=1 2.4

OWD standard deviation describes how the OWD values varies from the mean and is estimated as equation 2.5,

2 OWDStd.dev= 2.5

Using linear regression fitness of curve we can formulate the dependency of payload size (x) and the minimum OWD (y) of the network for each SMP as,

= a + bx 2.6

‘a’ is computed as and ‘b’ is given by b = (s/byte)

Where: = average of y’s and = average of x’s. Q = total number of selected payload size ranges.

Hence the OWD ( ) function can be approximately dependent on payload size variable ( ). The correlation coefficient (Corr.Coeff) is the relative fitness factor between the measured OWD versus payload size plot and the theoretical linear regression formula.

Corr.Coeff = 2.7

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2.2.2 Throughput Estimation Throughput is defined as the ratio of an amount of data passing a point of reference and the total elapsed time [13]. Throughput serves as Quality of Service (Qos) indicator for the network [21]. The measure of network performance QoS as view by the end user termed as Quality of Experience (QoE), covers the all the communication layers [13, 16 and 21].

For the given kth interval, bitrates for sampling interval time of (ΔT) at a reference point S is estimated as,

DK = 2.8

th Where: DK = the bitrate at (k ) interval, th dk- = number of bits started arriving in prior k interval th dk+= number of bits started arriving in k interval but not completed th dj=bits of M packets completely inside k interval

Each (DK) throughput value at position A is calculated as the average bitrate of duration of kth interval captured at MP in the interval [(k-1)ΔT, kΔT] specifies one sampling interval which includes one or more packets [21]. The combination arrival time series (timestamp) and payload size are the parameters collected from the captured data at the MP to estimate the throughput. The average throughput during the time window (W) is given as,

Dave= 2.9

Where Z= , defines the total number Z throughput values during W time Window. The standard deviation of the average throughput is given by:

2.10

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The Coefficient of Throughput Variation (CoTV) for a network at point ‘M’ is given by:

CoTVM = 2.11

The CoTV comparison at the sender and receiver indicates the extent of the burstiness in the network [13].

From the above equations estimation of the throughput depends on selection of sampling interval (ΔT), time window (W) and number of samples. For accuracy in throughput results the accuracy of timestamp and consideration of fractional packets (PDU) in the sampling processing are determinant factors [12].

Bitrate C++ program tool is used to convert the binary cap file to textual stream of samples. This program handles timestamp resolution of picoseconds. The input parameters for the program are sampling frequency (m), speed of the link (l), layer of interest (q), source IP address, destination IP address, and port number. The sampling frequency is the inverse of sampling interval. The throughput of the network including the SMPs is estimated from the average bitrate in the streams of samples [12]. For most of the throughput analysis section the sampling frequency is selected 1Hz which is equivalent to 1s sampling time interval.

2.2.3 Packet Loss Ratio Estimation Packet loss can be due to corruption in the IP fields of the packet after it is received on the receiver or completely lost in the network. The designated packets captured at the sender and receiver of the MPs is compared for each packet to analyze the packet loss in the network.

Hence the packet loss ratio is estimated by calculating the difference between sent packets and total received packets and dividing the difference by the total sent packets. It can be simply estimated as shown in equation 2.12.

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λ = 2.12

Where: λ= packet loss ratio Sp = Total number of sent packets Rp = successfully received packets

2.2.4 OWD of Gateway To evaluate the effect of GW on OWD estimation of the network; the GW connects the sender and receiver workstations using internet connection sharing (ICS) the same as the complete setup for SMPs. The SMP and AP from Figure 2.1 were replaced with D- link DUB-E100 fast Ethernet USB adaptor to connect the GW and receiver workstation as show in the setup Figure 2.4. Selecting USB-Ethernet adapter in this case would assure the same treatment of packets as SMP USB interfaces as in Figure 2.1 with a difference in driver. The two ends of the GW are wiretapped to the MP DAG 3.6E interfaces for capturing the traffic at the sender and receiver workstations.

Wiretap A Wiretap B

usb Ethernet DUB-E100. GW Receiver Sender

MP

Figure 2.4 OWD estimation of the GW.

2.2.5 OWD of Access Point To illustrate the effect of AP in the network direct OWD estimation is not easy. This is due to WLAN interface can’t be wiretapped to be used in DPMI. Hence an alternative path from one Ethernet interface to another Ethernet interface is selected, as show in Figure 2.5. The AP was configured to work as bridge mode where the functionality is transparent for layer 3 and above [24].

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Figure 2.5 OWD estimation of AP. 2.2.6 OWD of USB Link To estimate the effect of the USB link and interface on OWD, experimental setup shown in Figure 2.6 is used. The SMP is connected to GW through USB 2.0 cable which has interfaces speed of 480Mbps. The transfer of packets to and from SMP is captured and timestamp recorded using a special hardware USB protocol analyzer called Beagle USB 480 Protocol Analyzer [23, 40]. The packets that appear on the bus are copied to the analysis port. The captured ‘TDC’ format is converted to a readable ‘csv’ file in the datacenter software. PERL script is employed to separate payload sizes and estimate the duration of time accordingly.

Mostly for all SMPs every packet there is 86 bytes of header appended to application packet during transition which is greater than USB-Ethernet adapter i.e. 46bytes. Packets greater than or equal to (450+86) bytes are fragmented. The maximum MSS is 512bytes on the bus and as payload size increase the packets are divided in more segments.

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Figure 2.6: OWD estimation of USB Link.

2.2.7 Throughput Measurement using DPMI and Iperf The main aim of this section is to compare the throughput measured using available network performance measurement tools for SMPs and DPMI system. In this thesis Iperf is selected because it is commonly used tool in different type of devices and platforms. Iperf estimates throughput of a network between two end point devices [10].

Wiretap B

Receiver Sender Access point

MP

Figure 2.7 Experimental setup for DPMI vs. Iperf comparison.

Using the experimental setup shown in Figure 2.7, Iperf tool is installed at the SMP and receiver workstation. Using Iperf default UDP payload size, 1470 bytes of UDP traffic at the data rate of 1Mbps for consecutive 100 s are sent to the receiver side 23 through the AP. While the data pass through wiretap B the copy of the data is captured at the MP before reaching the receiver. The Iperf tool installed at the SMP and receiver side captures and calculates the throughput for each sampling interval of 1 s. With the experimental trace found at the MP, the throughput for DPMI case is analyzed using existing bit rate evaluation tool at the sampling interval of 1 s. The statistical results are discussed in section 3.7.

2.2.8 Experimental Configuration In this experimental configuration three SMPs, HTC HD2 Microsoft Windows Mobile 6.5 Professional OS (SMP1), HTC Desire HD Android OS v2.2 (SMP2) and Sony Ericsson Xperia X1a Microsoft Windows Mobile 6.1 Professional (SMP3) [22, 23], have been connected in the experimental setup. In addition to the above mentioned SMPs, Nokia N8 (Symbian^3 OS) and Apple iPhone iOS3 experimental runs were tried, but due to the limitation on tethering software they are not dealt here.

The AP operates at four data rates i.e. 1Mbps, 2Mbps, 5.5Mbps and 11Mbps. In our experiment the sending rates (R) in UDP traffic generator were tuned to 8kbps, 1Mbps, 2Mbps and 5.5Mbps where data rate of AP link was fixed at R=11Mbps as shown in

Table 2.1. The payload sizes (PL) are made to vary starting from 400 bytes to 1450 bytes with step size of 50 bytes at different R. The UDP traffic generator uses the IFG and PL to calculate R using equation 2.13.

R = 2.13

The same experimental setting has been used for each OWD, throughput and packet loss ratio analysis during the whole experiment. The experimental runs are repeated for more than 20 times to see the effect of the randomization behavior of the networks and measurement processes. After observing the confidence interval we used the data collected from 15 run for analysis purpose. After the experiment run, the trace data captured at the MP in binary .cap format is converted into human readable format using trace analyzer tool for OWD and packet loss ratio analysis as shown in Figure 2.8. To estimate the throughput the network traces collected at the MP are analyzed

24

using bitrate estimation tool with averaging interval of 1 s for a given sample size and total observation time as shown in Figure 2.8. By using Perl script and MATLAB program, the textual stream file is used to estimate the statistical average, min, max and CoTV for each metrics.

Start capturing traffic inside MP

Binary file (.cap)

Bitrate or OWD

OWD Bitrate

Conversion text format (.txt) Conversation to text format (.txt)

Network Traces Stream of avg. bitrate samples

Bitrate Analysis Tool (PERL OWD Analysis Tool (PERL and and MATLAB) MATLAB)

Report Throughput, loss, Report OWD, packet loss statistics statistics

Figure 2.8: Flow chart for statistical analysis process.

25

Table 2.1 Experimental Parameter setting.

Number of Exp. Duration Metrics P [Byte] R sample No. L (s) packets (N)

1 8kbps 10,000 400 -1450 2 with step 1Mbps - OWD size of 50 3 2Mbps

4 1472 1Mbps - 100,000 5 Throughput 1Mbps 320-1160 and 400 -1450 6 with step 2Mbps 160-580 Packet size of 50 7 Loss Ratio 5.5Mbps 64-232

8 Throughput 1472 5.2Mbps 80,000 226

Throughput 9 for DPMI 1470 1Mbps - 100 vs. Iperf

2.2.9 Controlling the Experimental Environment Controlling the input variables and environmental effects for WLAN based network is imperative especially for comparison of different SMPs. As network processes inside SMP demands high consumption power all the SMPs were plugged-in power to the gateway during the experiments. This also keeps from network performance deterioration using internal batteries below certain power level. Frequency analyzer software inSSIDer version 1.2.6.0115 [25] is used to scan the status of the WLAN access points that emerge in the vicinity of the experimental setup. Using the analyzer software the Radio Frequency (RF) power level of the WLAN devices found in the environment can be scanned to monitor the external wireless signals. After surveying the best available radio frequency level spot for the SMP from AP both the devices are

26 fixed at the same positions for all the experimental runs [26]. Running the frequency analyzer in the test bed environment before we start the experiment, the transmitting and receiving power variation was between -27db and -32db in the AP, on other wireless devices found in the environment the range was between -50db to -72db. Hence their low power interference effect is very low. To avoid additional interference from Wi-Fi users in the surrounding, the experiments are made at the night time between the times 18:00 up to 6:00 in the laboratory where the number of Wi-Fi users are very low i.e. Bluetooth device, micro oven etc. Moreover, to minimize the interference from other AP in the area the AP was configured to work on channel 1 i.e. non overlapping channel. In the next chapter, the results that are found from the experiment will be discussed.

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CHAPTER 3

Results and Analysis

In this chapter the results found from the experimental processes will be discussed in detail. The OWD analysis will be discussed in section 3.1, section 3.2 and section 3.3, the average throughput and packet loss ratio will be discussed in section 3.4 and section 3.5. Finally comparison of SMPs throughput with other available network performance evaluation tool will be discussed in section 3.6.

3.1 Effect of GW on the OWD

Exp. No. 1-3 configuration setting is employed to see the effect of the GW on OWD of the network as shown in Figure 2.4. For R 8kbps, 1Mbps and 2Mbps the estimated OWD statistical min, mean, max and standard deviations are tabulated as shown in Table 3.1.

From the table as the payload sizes increases the minimum and mean OWD of the GW increase for each increase in payload size as expected. Moreover the standard deviation value ranges from 46µs to 65µs for the specified range of payload size and the maximum values for the entire payload sizes are also at certain limit; increase in payload size increases the maximum value also in non strict sense. The typical maximum OWD occurrence was identified to be 5.77ms at PL=1400 byte and R=2Mbps by probing the network trace the maximum delay exhibited was at the initial state. This OWD spike might be due to premature simulation warm-up. Hence the initial value can be ignored and instead the steady state of this distribution can be taken to consideration.

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Table 3.1 Effect of GW on OWD of the Network at R 8kbps, 1Mbps and 2Mbps

R=8kbps R=1Mbps R=2Mbps PL No. of No. of Mean Min Max Std.dev Mean Min Max Std.dev Mean Min Max Std.dev [Bytes] lost lost [ms] [ms] [ms] [ms] [ms] [ms] [ms] [ms] [ms] [ms] [ms] [ms] Packet Packet 400 0.677 0.461 1.334 0.045 0.549 0.434 1.476 0.104 6 0.495 0.433 1.371 0.084 10 450 0.719 0.492 1.483 0.049 0.584 0.477 1.406 0.1 2 0.548 0.476 1.402 0.091 7 500 0.765 0.557 1.396 0.047 0.637 0.509 1.459 0.101 5 0.605 0.517 1.561 0.098 4 550 0.806 0.591 1.024 0.045 0.725 0.558 1.676 0.1 0 0.655 0.557 1.55 0.101 1 600 0.847 0.615 1.505 0.05 0.793 0.602 1.644 0.091 0 0.682 0.594 1.525 0.094 0 650 0.886 0.672 1.516 0.05 0.84 0.638 2.69 0.088 0 0.746 0.64 1.631 0.103 0 700 0.927 0.704 0.987 0.049 0.883 0.687 1.733 0.086 0 0.782 0.677 1.765 0.101 3 750 0.969 0.741 1.606 0.055 0.938 0.728 1.691 0.079 2 0.832 0.716 1.74 0.103 0 800 1.012 0.806 1.758 0.054 0.999 0.772 1.826 0.077 0 0.873 0.767 1.793 0.101 1 850 1.055 0.835 1.119 0.048 1.045 0.815 1.873 0.076 0 0.922 0.808 1.818 0.102 0 900 1.097 0.888 1.527 0.049 1.09 0.847 2.462 0.074 0 0.964 0.851 1.753 0.101 0 950 1.139 0.934 1.905 0.051 1.122 0.901 1.969 0.081 0 1.021 0.884 1.968 0.102 0 1000 1.18 0.964 1.858 0.053 1.16 0.934 1.993 0.08 0 1.036 0.935 1.944 0.095 0 1050 1.22 0.996 1.846 0.054 1.192 0.978 2.033 0.083 0 1.113 0.974 1.886 0.102 0 1100 1.266 1.058 1.926 0.053 1.228 1.025 2.073 0.089 0 1.184 1.013 1.981 0.099 2 1150 1.307 1.104 1.94 0.051 1.273 1.054 2.109 0.085 1 1.243 1.061 2.026 0.092 0 1200 1.348 1.151 1.906 0.052 1.318 1.11 2.167 0.082 0 1.292 1.103 2.007 0.089 1 1250 1.389 1.181 2.044 0.053 1.36 1.146 2.48 0.083 0 1.338 1.145 2.109 0.087 0 1300 1.433 1.236 2.092 0.052 1.398 1.191 2.212 0.082 0 1.383 1.189 2.15 0.086 0 1350 1.475 1.273 2.164 0.054 1.439 1.231 2.216 0.082 0 1.418 1.228 2.255 0.09 0 1400 1.518 1.314 2.135 0.053 1.483 1.276 2.351 0.081 0 1.467 1.273 5.77 0.087 0 1450 1.561 1.359 2.238 0.057 1.527 1.31 2.369 0.08 0 1.516 1.308 2.342 0.082 0

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This incident is plotted in Figure 3.1a initial version (above) and zoomed version (below) as function of sequence number. The scaled version of randomly selected sample distribution for sequence number from 5000 up to 5010 in the plot resembles flatted top and sharp bottom edge (trapezoid) structure which repeats every 28ms interval. Further enlarging the traces might also resemble triangular seesaw at some parts. This pattern appears in most of the payload size ranges. This effect might be partly contributed due to USB-Ethernet driver. The full original Distribution plot without the initial OWD spike is as show in Figure 3.2b. Hence the GW effect contributes a small OWD variation to the overall network as shown for all PL ranges.

Moreover the packet losses were also investigated. There were no packet losses observed for lower R i.e. R=8kbps but at R=1Mbps and R=2Mbps total packet losses from one million packets is not more than 6 and 10 packets at PL=400byte as shown in

Table 3.1. The losses are mostly at shorter PL’s. Packet losses have been observed randomly and they have no particular pattern. Repetition of the experiment has shown the numbers of packet losses are not that frequent and they are considered insignificant. Besides the packet loss, there was duplication of packets seen in the network trace. This phenomenon is discussed in [14]. The main reason for duplication in this experimental setting might be due to the misfits in driver for USB-Ethernet in the Ubuntu operating system. This type of incidence needs more study but since we are interested in packet losses only, they are not dealt in detail here.

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Figure 3.1a: OWD distribution of the first 30 packets (above) and 100 packets zoomed randomly selected packets from the middle of the same trace (below) for the same transmission pattern.

OWD Distribution For Gateway R=2Mbps PL=1400byte 2.6

2.4

2.2

2

1.8 OWD(ms)

1.6 - 1.4

0 1 2 3 4 5 6 7 8 9 10 4 Sequence Number x 10 Fig.3.1b OWD Distribution excluding the initial spike as a function of sequence number. 31

3.2 Effect of Access Point on the OWD

To evaluate the effect of AP on OWD of the network similar to the GW, experimental setup shown in Figure 2.5 is used. The effect of AP on OWD of the network at R=8kbps, 1Mbps and 2Mbps are estimated in Table 3.2. The results confirm that there is no large deviation in the observed OWD of the AP between measured sample packets. The maximum and minimum are almost equal to the mean in effect the standard deviation has very small values from 1µs to 12.3µs. As payload size increase the mean and minimum OWD increase linearly and so does the standard deviation.

This is expected because of longer packets experience more delay than shorter packets. But estimating the AP OWD in this setup would designate how the device treats packets with small difference in the executed results. From the tabular statistical results there are no significant OWD values which can contribute to the OWD variation of the network. There is insignificance difference between each R’s minimum OWD in their respective payload sizes. Hence the OWD of the AP is less dependent on R. Furthermore by looking into the network traces, the packets loss is very insignificant where they are ignored under normal condition. The maximum number of packets loss observed is not less than three out of million packets.

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Table 3.2 Effect of AP on OWD of the network at R 8kbps, 1Mbps and 2Mbps

R=8kbps R=1Mbps R=2Mbps No. of No. of PL Mean Min Max Std.dev Mean Min Max Std.dev lost Mean Min Max Std.dev lost [Bytes] [ms] [ms] [ms] [ms] [ms] [ms] [ms] [ms] Packet [ms] [ms] [ms] [ms] Packet 400 0.375 0.374 0.376 0.0059 0.375 0.374 0.376 0.0012 1 0.375 0.374 0.376 0.0012 3 450 0.413 0.412 0.414 0.0065 0.413 0.412 0.414 0.0014 2 0.413 0.412 0.522 0.0014 2 500 0.455 0.454 0.456 0.0072 0.455 0.454 0.567 0.0015 2 0.455 0.454 0.456 0.0015 2 550 0.493 0.492 0.494 0.0078 0.493 0.492 0.494 0.0016 0 0.493 0.492 0.494 0.0016 0 600 0.535 0.534 0.536 0.0085 0.535 0.534 0.615 0.0018 0 0.535 0.534 0.536 0.0017 0 650 0.573 0.572 0.574 0.0091 0.573 0.572 0.657 0.0019 0 0.573 0.572 0.584 0.0018 0 700 0.615 0.614 0.616 0.0097 0.615 0.614 0.616 0.002 0 0.615 0.614 0.696 0.002 0 750 0.653 0.652 0.69 0.0103 0.653 0.652 0.654 0.0021 0 0.653 0.652 0.654 0.0021 0 800 0.695 0.694 0.695 0.011 0.695 0.694 0.696 0.0022 0 0.695 0.694 0.766 0.0022 0 850 0.733 0.732 0.734 0.0116 0.733 0.732 0.783 0.0024 0 0.733 0.732 0.734 0.0023 0 900 0.775 0.774 0.776 0.0123 0.775 0.774 0.805 0.0025 1 0.775 0.774 0.869 0.0025 0 950 0.855 0.854 0.856 0.0135 0.813 0.812 0.855 0.0026 0 0.813 0.812 0.814 0.0026 0 1000 0.893 0.892 0.894 0.0141 0.855 0.854 0.919 0.0027 0 0.855 0.854 0.856 0.0027 0 1050 0.935 0.934 0.936 0.0148 0.893 0.892 0.894 0.0028 0 0.893 0.892 0.894 0.0028 1 1100 0.935 0.934 0.936 0.0094 0.935 0.934 1.035 0.003 0 0.935 0.934 0.961 0.003 0 1150 0.973 0.972 0.974 0.0097 0.973 0.972 0.974 0.0031 0 0.973 0.972 0.974 0.0031 0 1200 1.015 1.014 1.016 0.0102 1.015 1.014 1.016 0.0032 0 1.015 1.014 1.016 0.0032 0 1250 1.053 1.052 1.054 0.0105 1.053 1.052 1.156 0.0034 0 1.053 1.052 1.106 0.0034 0 1300 1.095 1.094 1.096 0.011 1.095 1.094 1.193 0.0035 0 1.095 1.094 1.096 0.0035 0 1350 1.133 1.132 1.134 0.0113 1.133 1.132 1.134 0.0036 0 1.133 1.132 1.267 0.0036 0 1400 1.175 1.174 1.176 0.0118 1.175 1.174 1.28 0.0037 0 1.175 1.174 1.176 0.0037 0 1450 1.213 1.212 1.214 0.0121 1.213 1.212 1.214 0.0039 0 1.213 1.212 1.214 0.0039 0

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3.3 Effect of USB link on OWD

The effects of USB link on OWD for each SMP is shown in the Table 3.3. From the experimental results we can see that as payload size increases the duration of packets in the USB bus increases. When data transferred over the USB bus to or from SMP there is an extra delay besides propagation delay and transmission delay. These delays are due the USB protocol which is periodically generated synchronization frames (signals) to control data communication and power consumption on the bus.

From the results we can also observe that the OWD occurred in the USB cable is very small compared to the effect of AP and GW as discussed in section 3.1 and 3.2. Here we have tried to show that the effect of USB link on the performance of the SMPs is very small but still it needs further study.

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Table 3.3: Effect of USB Link on OWD of the network at R 8kbps

PL SMP1 [us] SMP2 [us] SMP3 [us] [Bytes] Mean Min Max Std.dev Mean Min Max Std.dev Mean Min Max Std.dev 400 15.41 14.40 30.20 2.35 15.52 14.25 32.10 2.57 15.51 14.35 29.73 2.61 450 22.58 19.87 37.52 4.11 22.30 20.37 38.97 3.45 17.62 16.45 34.08 2.69 500 23.37 20.73 37.37 4.03 23.24 21.32 39.87 3.48 18.38 17.22 35.12 2.52 550 24.58 21.98 38.87 3.95 24.55 22.40 39.97 3.49 19.27 18.05 36.33 2.63 600 25.40 22.63 39.35 3.90 25.47 23.35 40.33 3.47 20.09 18.78 35.43 2.61 650 26.23 23.58 40.70 3.84 26.32 24.20 42.80 3.40 20.99 19.75 38.05 2.76 700 27.29 24.68 41.47 3.59 27.56 25.72 43.23 3.44 21.77 20.57 38.78 2.66 750 28.19 25.70 43.15 3.63 28.48 26.55 44.72 3.44 22.64 21.40 37.48 2.70 800 29.59 26.95 43.88 3.76 29.93 27.88 46.15 3.66 23.55 22.25 38.47 2.87 850 30.75 27.80 45.22 4.11 30.74 28.72 47.87 3.52 24.35 23.10 39.40 2.81 900 31.54 28.75 46.37 4.00 31.72 29.52 54.98 3.70 25.17 23.85 40.27 2.76 950 38.14 34.65 52.03 4.47 38.11 35.13 54.83 3.87 30.54 25.88 48.37 2.88 1000 38.96 35.52 55.12 4.45 39.03 30.60 64.02 3.97 31.54 26.73 49.07 3.08 1050 39.87 36.38 53.73 4.52 39.91 36.93 57.27 3.78 32.42 27.48 50.82 2.97 1100 40.94 37.73 55.42 4.34 41.35 38.20 60.87 3.96 33.78 28.33 51.27 3.20 1150 41.76 38.52 57.67 4.30 42.24 34.25 68.17 4.12 34.81 29.10 50.83 3.45 1200 42.92 39.82 59.37 4.15 43.50 40.12 59.50 4.06 35.91 29.93 53.53 3.34 1250 44.11 40.68 59.62 4.43 44.49 40.92 69.63 4.59 36.89 30.90 54.10 3.35 1300 44.92 41.62 59.98 4.34 45.38 41.83 73.48 4.39 37.89 31.57 54.47 3.68 1350 46.21 42.95 62.52 4.38 46.63 43.32 75.53 4.30 39.23 32.53 56.17 3.81 1400 47.02 43.82 63.15 4.33 47.77 44.00 74.82 4.68 40.19 33.28 57.68 3.95 1450 48.43 45.00 65.60 4.65 48.95 40.20 76.63 4.57 41.58 34.07 58.35 4.21

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3.4 Evaluation and Comparison of OWD of SMPs

The aim of this section is to see how transmission patterns affect the OWD of the network and based on that to compare the network performance of SMPs. To observe the effect we have done experiments using experimental configuration as shown in Table 2.1. From all the experiments Exp. No. 1 up to Exp. No. 3, we observe that as payload size increase the minimum OWD of the network increases across all SMPs. This is expected since longer payload size takes longer time to transmit.

From the experimental results we also observe that, the minimum OWD of the network at low R i.e. 8kbps has higher minimum OWD value than at relatively higher data rates i.e. 1Mbps, 2Mbps especially for higher payload sizes. In this section the results for R=8kbps, R=1Mbps and R=2Mbps are presented in Figure 0.3, 0.5 and 0.7, additional low data rate results can be found in Appendix B.

The linear regression function and Corr.Coeff are evaluated for network with each SMP by using equation 2.6 and 2.7. The constant values and function are shown at Table 3.4, Table 3.6 and Table 3.7 for data rates 8kbps, 1Mbps and 2Mbps. The intercept constant discussed in section 2.2.1 equation 2.6 may be used as comparator for each SMP, which has empirically inversely proportional to the size of RAM and processing speed where other internal software and applications processes have been assumed relatively smaller effect. The intercept constant can be defined the initial OWD value at the initial PL value. In this thesis consideration the initial PL value is 50byte for each SMP’s. The initial PL=50bytes value is chosen close to the minimum possible PL=40bytes set by the traffic generator. The intercept constant is considered as the OWD at relatively zero PL coordinate. From the Tables 3.4 the intercept constant value for SMP1, SMP2 and SMP3 are 692.2µs, 496.2µs and 1602.7µs respectively. Hence these values represent indirect relationship to the capacity of their respective CPU (RAM size) as shown in Table A.1, SMP1 and SMP2 are from the same hardware vendor but their CPU (RAM

36 size) capability are different. Since they have same processing speed their difference in the intercept constant is very small as compared with SMP3. In contrast SMP3 has lower RAM size and processing speed compared to SMP1 and SMP2, in effect the intercept constant is comparatively higher both of them. The Corr.Coeff for minimum OWD versus payload size shows mostly greater than 99% as shown in Table 3.5, Table 3.6 and Table 3.7 for each SMP. Hence for low R the minimum OWD and payload sizes have linearity relationship in loose sense. But the OWD versus payload size graph for higher R might deviate from linearity behavior due to loss of packets and excessive queuing in the network.

Figure 3.2 Minimum OWD of the network at R=8kbps

Based on OWD and PL dependency criteria, OWD of the network using SMP2 has mostly more smooth linearity (Corr.Coeff = 99.99%) than the other SMP1 and SMP3. For SMP3 the MTU has been estimated to be 1366 byte where as for SMP1 and SMP3 they have MTU of 1500 bytes each. Hence for SMP3 at PL’s of 1400 byte and 1450byte, the correlation coefficient becomes smaller and the plots are

37 deviated from the linear curve as shown in the Figure 3.2, 3.3 and 3.4 at the respective R’s.

Figure 3.3 Minimum OWD of SMPs in a network at R=1Mbps.

The network with SMP2 has the lowest OWD as compared to the other SMPs at most payload size whereas using SMP3 in the network has highest minimum OWD. The qualitative plot of the Figure 3.2, 3.3 and 3.4, SMP2 is relatively more strictly linear than SMP1 and SMP2. In addition its plots are mostly below the two SMPs. This behavior might be an indication for faster packet forwarding capability or low processing delay. This network performance efficiency might be mainly for two different reasons. Firstly the CPU capacity namely processing speed and RAM size are higher than or equal to either of the two SMP’s. Secondly the anatomy of the tethering software (packet forwarding software) is implemented in the kernel level of the Android OS Froyo 2.2 platform. Hence due to most probable reason SMP2 would make faster decision in routing the packets from USB interface to WLAN interface which eventually is visible on the overall OWD of the network.

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Table 3.4 Statistical results of OWD at R=8kbps for PL=400bytes to PL=1150bytes.

SMP1 [ms] SMP2 [ms] SMP3 [ms] PL [Bytes] Mean Min Max Std.dev Mean Min Max Std.dev Mean Min Max Std.dev 400 3.14 1.68 14.12 1.55 1.71 1.36 7.42 0.42 2.94 2.41 75.11 1.51 450 2.71 1.7 11.6 1.31 1.88 1.49 31.19 0.67 2.98 2.55 10.48 0.55 500 2.95 1.82 14.88 1.37 1.96 1.56 13.95 0.53 3.2 2.49 119.8 1.725 550 3.09 1.97 17.06 1.43 2.12 1.67 69.88 1.9 3.23 2.72 293.59 1.463 600 3.31 2.09 17.86 1.49 2.13 1.74 12.08 0.52 3.26 2.74 13.73 0.65 650 3.59 2.13 18.03 1.57 2.25 1.82 46.41 0.89 3.38 2.89 73.8 1.31 700 4.77 2.25 18.85 1.56 2.36 1.91 69.28 1.51 3.63 2.97 23.53 1.25 750 4.87 2.35 16.04 1.53 2.42 2 50.79 0.95 3.43 3 13.82 0.62 800 4.72 2.42 19.02 1.63 2.49 2.09 16.27 0.58 3.5 3 15.86 0.55 850 3.19 2.43 11.14 1.03 3.49 2.19 75.95 1.33 3.55 3.19 5.91 0.48 900 4.12 2.57 22.32 1.88 3.05 2.25 73.45 1.226 3.66 3.19 9.76 0.49 950 4.09 2.71 37.21 1.83 2.9 2.34 19.59 0.98 3.68 3.33 11.17 0.54 1000 4.53 2.69 27.36 1.43 2.86 2.44 53.33 0.99 3.88 3.44 144.15 1.67 1050 4.68 2.9 23.95 1.23 2.91 2.48 68.66 1.12 3.98 3.39 140 2.25 1100 4.65 2.98 18.16 1.96 2.98 2.56 31.08 0.66 4.18 3.72 125.96 1.41 1150 5.23 3.07 24.97 1.25 3.05 2.67 36.94 0.7 4.2 3.72 10.23 0.55

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Table 3.5 Statistical results of OWD at R=8kbps for PL=1200bytes to PL=1450bytes

SMP1 [ms] SMP2 [ms] SMP3 [ms] PL [Bytes] Mean Min Max Std.dev Mean Min Max Std.dev Mean Min Max Std.dev

1200 4.85 3.13 30.96 2.08 3.12 2.72 10.72 0.41 4.4 3.82 459.82 7.23 1250 4.27 3.16 16.96 1.41 3.25 2.85 64.88 1.11 4.46 3.83 307.54 4.83 1300 4.46 3.28 20.33 1.5 3.3 2.93 9.69 0.39 4.82 3.96 193.75 3.52 1350 4.51 3.4 21.11 1.47 3.61 3.01 16.62 1.09 4.66 4.06 36.55 1.12 1400 4.76 3.48 19.05 1.55 3.51 3.08 25.97 0.73 4.66 4.26 11.69 0.59 1450 5.01 3.58 20.46 1.8 3.58 3.18 7.92 0.45 4.94 4.52 43.42 0.87 Slope (µs/byte) 1.794 1.724 1.801 Intercept (µs) 692.2 496.2 1602.7 Corr.

Coff (%) 99.98 99.99 99.9

+692.2

Regression

Y=1.794X Y=1.724X+496.2

Y=1.801X+1602.7

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Table 3.6 Statistical OWD delay at R=1Mbps for PL=400 bytes to PL=900 bytes.

PL SMP1[ms] SMP2[ms] SMP3[ms] [bytes] Mean Min Max Std.dev Mean Min Max Std.dev Mean Min Max Std.dev 400 8.78 1.36 217.39 1.55 2.12 1.18 64.72 1.73 2.85 2.29 49.91 0.99 450 10.17 1.45 73.54 1.21 1.81 1.31 31.01 0.9 2.95 2.4 43.58 0.98 500 6.42 1.58 158.43 1.93 1.84 1.39 22.45 0.77 3.02 2.49 63.01 1.01 550 6.01 1.67 172.24 1.82 1.88 1.48 30.12 0.56 3.1 2.58 47.72 0.98 600 3.13 1.77 110.55 1.39 1.96 1.55 24.5 0.54 3.18 2.67 63.81 1 650 2.54 1.86 44.55 0.99 2.07 1.64 37.74 0.62 3.28 2.76 60.83 1.07 700 2.57 1.94 98.56 1.33 2.36 1.72 73.65 1.35 3.78 2.84 59.97 1.27 750 2.69 2.03 42.06 1.02 2.23 1.82 72.46 0.75 3.44 2.92 48.95 0.95 800 2.82 2.11 55.58 1.13 2.3 1.9 19.58 0.5 3.55 3.02 47.37 0.98 850 2.95 2.21 54.68 1.11 2.43 1.99 90.47 1.67 3.64 3.1 70.9 1.04 900 3.01 2.3 65.68 1.13 2.5 2.07 35.43 0.52 3.73 3.14 49.29 0.95

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Table 3.7 Statistical OWD delay at R=1Mbps for PL=950 bytes to PL=1450 bytes. SMP1 [ms] SMP2 [ms] SMP3 [ms] PL [Bytes] Mean Min Max Std.dev Mean Min Max Std.dev Mean Min Max Std.dev 950 4.23 2.39 51.82 1.64 2.6 2.17 53.63 0.57 4.16 3.3 62.66 1.5 1000 3.43 2.54 85.74 2.92 2.71 2.26 45.01 0.66 3.97 3.38 79.68 1.39 1050 3.43 2.61 74.55 1.44 2.77 2.33 28.76 0.54 4.04 3.47 52.11 1.07 1100 3.47 2.72 60.4 1.22 2.86 2.41 99.41 0.86 4.17 3.55 110.01 1.92 1150 3.54 2.8 67.23 1.34 2.93 2.51 50.91 0.65 4.37 3.61 54.33 1.851 1200 3.55 2.84 43.46 1 2.99 2.59 30.4 0.57 4.22 3.74 45.26 1.21 1250 3.65 2.92 52.28 1.11 3.05 2.68 45.61 0.57 4.32 3.81 300.79 1.37 1300 4.88 3.02 60.77 1.772 3.13 2.77 25.16 0.5 4.35 3.87 53.74 0.83 1350 3.9 3.1 91.69 1.54 3.23 2.84 36.5 0.55 4.43 3.94 45.33 0.73 1400 5.14 3.19 39.48 1.28 3.36 2.93 93.55 1.18 4.69 4.03 55.14 0.78 1450 4.12 3.29 87.94 1.82 3.43 3.03 80.32 1.11 4.85 4.29 58.82 0.88 Slope 1.826 1.731 1.77 (µs/byte) Intercept 661.9 515.6 1601 (µs) Corr. 99.92 99.99 99.8

Coef (%)

Regression

Y=1.826X+661.9 Y=1.731X+515.6 Y=1.774X+11601.1

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Figure 3.4 Minimum OWD of SMPs in a network at R=2Mbps.

As R increase the minimum OWD of the network decrease slightly mostly for longer

PL’S. But for shorter PL the minimum OWD decrease. This can be seen from Table 3.6, 3.7 and 3.8 especially for SMP2. As R increase the standard deviation increase in the network, this might be due to the effect of queuing and losses. The packet losses for the experimental configurations dealt in this section will be discussed in detail on section 3.5.

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Table 3.8 Statistical OWD delay at R=2Mbps for PL=400bytes to PL=1450byte

SMP1(ms) SMP2 (ms) SMP3 (ms) Std. Std. P (byte) Mean Min Max Mean Min Max Mean Min Max Std.dev L dev Dev 400 2.28 1.36 82.14 2.76 1.64 1.19 31.33 1.06 6.60 2.41 66.93 1.82 450 2.52 1.46 93.29 3.78 1.78 1.31 52.39 0.97 5.09 2.48 37.32 1.47 500 2.51 1.59 68.00 2.93 1.86 1.40 67.95 1.24 3.64 2.58 30.13 1.25 550 2.65 1.68 82.20 3.25 1.90 1.48 25.51 0.65 3.78 2.66 68.31 1.63 600 2.73 1.76 83.98 3.37 2.06 1.56 138.92 2.51 3.88 2.73 44.84 1.07 650 2.90 1.85 93.15 3.46 2.07 1.64 40.93 0.77 3.95 2.79 45.04 1.17 700 2.88 1.94 80.02 2.63 2.17 1.73 24.55 0.63 5.22 2.88 63.90 1.24 750 2.99 2.02 81.60 3.16 2.24 1.81 27.68 0.69 4.07 2.94 68.17 1.57 800 3.09 2.11 94.74 2.93 2.32 1.89 26.40 0.57 3.87 3.02 54.69 1.15 850 3.14 2.21 87.69 2.93 2.64 1.98 102.32 1.38 3.76 3.10 53.82 1.25 900 3.27 2.28 92.97 3.09 2.51 2.06 76.28 1.19 3.80 3.19 69.69 1.38 950 3.39 2.38 96.81 3.03 2.93 2.17 103.49 1.442 3.89 3.30 50.05 1.19 1000 5.13 2.47 163.77 9.72 2.68 2.25 45.66 0.68 3.98 3.38 57.35 1.44 1050 4.50 2.67 129.17 6.76 2.76 2.33 41.49 0.65 4.07 3.47 65.47 1.47 1100 4.19 2.65 94.58 5.02 2.85 2.42 33.84 0.61 4.16 3.56 97.06 1.62 1150 3.87 2.73 93.74 3.81 2.97 2.49 35.03 0.77 4.23 3.65 61.28 1.42 1200 3.82 2.83 92.34 3.28 3.19 2.59 95.28 1.78 4.30 3.73 53.73 1.22 1250 3.96 2.92 74.93 3.24 3.31 2.67 77.16 1.86 4.98 3.82 63.41 1.56 1300 4.05 3.00 105.58 3.38 3.19 2.74 47.37 1.13 4.47 3.91 63.42 1.28 1350 4.94 3.10 240.52 8.81 3.30 2.84 83.02 1.50 4.57 4.00 57.10 1.17 1400 4.32 3.18 222.23 4.49 3.35 2.92 59.78 1.09 4.65 4.09 56.23 1.33 1450 4.32 3.28 76.66 3.04 3.50 3.02 137.90 2.12 4.96 4.38 69.37 1.52 Slope(µs/byte) 1.801 1.710 1.744 Intercept (µs) 672.7 532.2 1624.9 Corr. Coef (%) 99.90 99.99 99.78

641.96

468.18

1624.86

44

+

+ +

Y=1.870X Y=1.745X Y=1.744X Regression

Based on the regression equation the slope of each line denotes an inverse of the bandwidth of the network. This slope varies between each SMP and selected R’s due to loss, queuing, total number of samples (N), PHY overhead, MAC overhead, the nature of each SMP and other factors. From the above tables SMP2 has lowest slope especially for lower R at 8kbps and SMP1 has lower slope than SMP3. For relatively higher R i.e. 1Mbps and 2Mbps, SMP2 still has lower slope than both SMPs but SMP3 has lower slope than SMP1. Moreover there is a slight tendency in increment of the intercept constant as R increase especially from 1Mbps to 2Mbps. This can be interpreted for lower R the minimum OWD of the network has relatively less values than higher R’s especially for shorter PL.

3.4.1 Packet Trace Analysis of OWD distributions

To examine how the network is performing as function of sequence number, the network traces collected from Exp. No 4 are plotted for PL 1472 byte at R=1Mbps. OWD of the network for the three SMPs connected cases as a function of sequence numbers are shown Figure 0.5. The OWD across the network experience loss of packets, variations and delay spikes. The effect of OWD variations might be due to the combination or independent actions of underlying MAC and PHY parameters or higher layer protocols involving in the transmission of data packets across the 802.11b link.

Firstly Since UDP doesn’t guarantee delivery of data, the only packet transmission control for packets is two way handshake acknowledgments (ACK) generated by the AP after successful delivery of MAC Service Data Unit (MSDU). For every packet the sender (SMP) has to pause for Short Interframe Space (SFIS) duration of time until the ACK signal issued by the AP. If the ACK frame is not issued back or timeout duration expires, the Automatic Repeat reQuest (ARQ) commands retransmission of the packet according to exponentially random back off algorithm. The ARQ for 802.11b is commonly termed as “stop and

45

wait”. The packet can be retransmitted without discarded until the maximum seven threshold number of attempts is repeated. Due to this random retransmission or loss of packet the variation of OWD across the network increases in effect some packets might experience huge delay spikes [26, 29, 31, and 32].

Figure 3.5 OWD vs. Seq. No. at PL= 1472 bytes and R= 1Mbps.

This concept can be extended for multiple base stations (STA’s) in the media where complex form of CSMA/CD back off exponential algorithms are employed to avoid collision on the wireless link hence it would contribute to the delay variation but in this controlled experimental scenario the only STA nodes are always the SMP and AP [31,32]. Hence the OWD variation effect due to involvement of multiple STA’s are considered minimal. Secondly most probable reason for OWD variation and delay spikes might be due to tethering software, hardware capability and OS of

46

the SMPs. From the traces SMP1 has highest max and std.dev OWD at most packets while SMP2 has lowest max and std.dev OWD. SMP1 and SMP3 have higher max OWD and std.dev compared to SMP2 at most packets. This might be because of the reason that they are using the same tethering software and windows OS version. The tethering software might be responsible for keeping the packets in the queue in the presence of congestion in the network. This in effect might contribute to nominal loss of packets and increase in throughput as it will be discussed in sections 3.5 and 3.6.

The effects of OWD variation and spikes due to retransmission and other effects mentioned above are clearly noticeable in the zoomed Figure 3.5 for the three SMPs. The maximum OWD of the network for SMP1, SMP2 and SMP3 are 298ms, 55ms and 169ms respectively. The delay spikes are randomly distributed throughput the total sequence number ranges.

3.4.2 Comparison of OWD Distribution The OWD Empirical Cumulative Distribution Function (ECDF) of network with three SMPs is plotted in Figure 3.6. The OWD probability distribution of the network using SMP2 is greater than SMP1 and SMP3 but the distribution for SMP1 lies in between SMP2 and SMP3. This comparison based on the OWD distribution of the network holds mostly true for relatively smaller and lossless R values at given PL’s.

The ECDF plots illustrate for given total sent packets more than 95% of the packets SMP2 offers s the least OWD in the network, as compared between SMP1 and SMP3. Comparing the network using SMP1 and SMP3, the network using SMP1 has around 67% of samples less OWD than SMP3. But around 23% of the samples the network using SMP3 has less OWD than SMP3. Due its better hardware and software capability, SMP2 contributes less delay to the end to end OWD of the network. Even though SMP1 has higher CPU capability than SMP3 but they have similar Windows OS and tethering software. This might be the reason for comparable OWD distribution of the network using either of them.

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Figure 3.6 log scaled ECDF OWD of the network at using the three SMP’s

R=1Mbps and PL= 1472bytet

3.5 Throughput Analysis

To study how the transmission patterns affect the average throughput of a

network having SMP, We perform experiments by varying R’s and PL‘s using the same experimental configurations as previous section 3.4. The experimental configuration of Exp. No. 5, 6 and 7 are applied to the network to observe the throughput of the network.

Table 3.9, Table 3.10 and Table 3.11, shows the average throughput at the sender and receiver side as a function of payload size, from the tables it can be seen that for each increase in data rate there is an increase in average throughput. The sender side throughput is less than the ideal traffic generator sending rate 1Mbps, 2Mbps and 5.5Mbps this might be because of the accuracy of the traffic generator as estimated inside the MP [12] and

48

the small distance between the generated traffic and the wiretap. From these tables we can also observe that as the length of payload size increase the sender and receiver side throughput also increases slightly except at some payload sizes, this is due to the random loss of packets in the network [39]. The CoTV at the receiver is greater than the sender side for the respective

PL. As PL increase the CoTV at the receiver decrease mostly. The CoTV describes the burstiness in the network [16]. The packets loss ratio for the respective R and PL can be seen from Table 3.9, Table 3.10 and Table 3.11 but it will be discussed in section 3.6 in more detail.

Figure 3.7 Sender and Receiver average throughput at R=1Mbps.

At 1Mbps, from Figure 3.7 and Table 3.9, the average received throughput of the network for SMP1 and SMP3 is higher than SMP2 except at some payload sizes. Surprisingly for payload size above its MTU value i.e. 1366 byte as estimated in section 3.4, the network with SMP3, have higher throughput than the network with SMP1 and SMP2.

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Table 3.9 Sender and Receiver average Throughput at R=1Mbps ΔT=1s. SMP1 SMP2 SMP3 PL Sender [Mbps] Receiver [Mbps] Sender [Mbps] Receiver [Mbps] Sender [Mbps] Receiver [Mbps] [Bytes] λ [%] λ [%] λ [%] Mean CoTV Mean CoTV Mean CoTV Mean CoTV Mean CoTV Mean CoTV 400 0.99672 0.00158 0.99650 0.00426 0.02160 0.99846 0.00060 0.99640 0.00189 0.20658 0.99671 0.00159 0.99660 0.12799 0.01071 450 0.99710 0.00057 0.99680 0.00831 0.03009 0.99865 0.00040 0.99710 0.00247 0.15491 0.99706 0.00055 0.99700 0.05328 0.00602 500 0.99736 0.00176 0.99730 0.00668 0.00602 0.99870 0.00035 0.99730 0.00218 0.14001 0.99739 0.00178 0.99737 0.00327 0.00201 550 0.99761 0.00181 0.99760 0.00704 0.00100 0.99760 0.00179 0.99760 0.00271 0.00000 0.99758 0.00179 0.99750 0.00825 0.00802 600 0.99777 0.00136 0.99770 0.00479 0.00739 0.99779 0.00138 0.99770 0.01460 0.00902 0.99779 0.00136 0.99770 0.11193 0.00902 650 0.99803 0.00099 0.99790 0.00655 0.01303 0.99790 0.00134 0.99790 0.10351 0.00000 0.99799 0.00106 0.99270 0.06865 0.53007 700 0.99813 0.00219 0.99810 0.00905 0.00301 0.99811 0.00224 0.99810 0.01976 0.00100 0.99817 0.00217 0.99790 0.00335 0.02705 750 0.99824 0.00271 0.99820 0.00600 0.00401 0.99827 0.00272 0.99820 0.09656 0.00739 0.99823 0.00269 0.99810 0.00340 0.01302 800 0.99842 0.00071 0.99830 0.00312 0.01202 0.99835 0.00078 0.99420 0.04899 0.41569 0.99839 0.00066 0.99820 0.00444 0.01903 850 0.99843 0.00227 0.99840 0.68328 0.00300 0.99846 0.00222 0.99740 0.01990 0.10616 0.99843 0.00227 0.99690 0.00388 0.15324 900 0.99853 0.00309 0.99850 0.35680 0.00300 0.99854 0.00310 0.99760 0.00842 0.09414 0.99857 0.00304 0.99840 0.02470 0.01702 950 0.99861 0.00349 0.99860 0.29394 0.00130 0.99863 0.00349 0.99780 0.00695 0.08354 0.99861 0.00347 0.99850 0.00386 0.01102 1000 0.99874 0.00252 0.99740 0.05097 0.13420 0.99871 0.00255 0.99660 0.02403 0.21127 0.99870 0.00258 0.99870 0.00293 0.00000 1050 0.99879 0.00194 0.99870 0.07855 0.00901 0.99904 0.00007 0.99670 0.00814 0.23453 0.99880 0.00194 0.99870 0.00307 0.00991 1100 0.99884 0.00410 0.99880 0.00960 0.00400 0.99885 0.00409 0.99810 0.00743 0.07509 0.99884 0.00413 0.99880 0.00494 0.00400 1150 0.99885 0.00423 0.99880 0.39987 0.00501 0.99884 0.00426 0.99820 0.10097 0.06407 0.99886 0.00424 0.99870 0.03142 0.01629 1200 0.99898 0.00177 0.99890 0.11316 0.00767 0.99895 0.00172 0.99820 0.00296 0.07520 0.99893 0.00172 0.99890 0.20193 0.00300 1250 0.99895 0.00255 0.99890 0.12064 0.00544 0.99897 0.00244 0.99840 0.00338 0.05706 0.99900 0.00240 0.99850 0.00965 0.05033 1300 0.99902 0.00187 0.99890 0.02482 0.01201 0.99900 0.00199 0.99840 0.00246 0.06000 0.99900 0.00195 0.99810 0.00302 0.09009 1350 0.99907 0.00498 0.99720 0.06157 0.18690 0.99908 0.00504 0.99840 0.02271 0.06846 0.99903 0.00502 0.99903 0.00525 0.00000 1400 0.99911 0.00410 0.99890 0.03884 0.02102 0.99911 0.00406 0.99850 0.00413 0.06105 0.99911 0.00414 0.99900 0.00699 0.01101 1450 0.99913 0.00344 0.99910 0.01947 0.00300 0.99914 0.00347 0.99860 0.00361 0.05387 0.99912 0.00345 0.99910 0.00386 0.00200

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As we increase the sending data rate to R=2Mbps the average throughput of network with SMP1 attains better throughput than the network with SMP2 and SMP3 as shown in Table 3.10 and Figure 3.8. From experimental results at 1Mbps and 2Mbps, the average throughput at the sender side is comparable with the receiver side throughput since the packet loss is relatively small. And the standard deviation at the receiver side is higher than the sender side, this is expected because of the bottleneck in the network might create random arrival of packets at the receiver [13].

Figure 3.8 Sender and Receiver average throughput at R=2Mbps

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Table 3.10 Sender and Receiver average Throughput at R=2Mbps. ΔT=1s

SMP1 SMP2 SMP3 PL Sender [Mbps] Receiver [Mbps] Sender [Mbps] Receiver [Mbps] Sender [Mbps] Receiver [Mbps] [Bytes] λ [%] λ [%] λ [%] Mean CoTV Mean CoTV Mean CoTV Mean CoTV Mean CoTV Mean CoTV 400 1.98714 0.00075 1.98708 0.06376 0.00302 1.98710 0.00089 1.98688 0.01890 0.01092 1.98714 0.00082 1.95964 0.01015 1.38370 450 1.98858 0.00094 1.98854 0.05309 0.00192 1.98862 0.00101 1.98856 0.01483 0.00302 1.98852 0.00102 1.97692 0.00694 0.58330 500 1.98905 0.00118 1.98895 0.04995 0.00514 1.98924 0.00105 1.98922 0.09377 0.00101 1.98914 0.00119 1.98890 0.00693 0.01207 550 1.99015 0.00128 1.99014 0.05330 0.00045 1.99012 0.00123 1.99012 0.00610 0.00000 1.99004 0.00121 1.98102 0.05183 0.45349 600 1.99100 0.00081 1.99098 0.02898 0.00088 1.99105 0.00075 1.99104 0.00608 0.00050 1.99098 0.00094 1.99076 0.00918 0.01118 650 1.99166 0.00082 1.99166 0.00862 0.00000 1.99156 0.00099 1.99148 0.00449 0.00428 1.99158 0.00116 1.99153 0.00582 0.00227 700 1.99270 0.00107 1.99224 0.05596 0.02308 1.99220 0.00105 1.99214 0.04342 0.00301 1.99212 0.00111 1.99177 0.00164 0.01752 750 1.99310 0.00111 1.99232 0.00111 0.03892 1.99266 0.00113 1.99266 0.00475 0.00000 1.99264 0.00112 1.99264 0.00890 0.00000 800 1.99325 0.00150 1.99298 0.06342 0.01357 1.99327 0.00143 1.99318 0.01278 0.00457 1.99334 0.00144 1.98691 0.00327 0.32252 850 1.99368 0.00126 1.99361 0.04868 0.00344 1.99373 0.00116 1.98771 0.01359 0.30211 1.99368 0.00126 1.99351 0.00194 0.00868 900 1.99416 0.00055 1.99352 0.07716 0.03209 1.99400 0.00064 1.99396 0.00179 0.00206 1.99416 0.00045 1.99390 0.00455 0.01294 950 1.99428 0.00180 1.99427 0.01658 0.00074 1.99426 0.00172 1.99424 0.00881 0.00100 1.99421 0.00186 1.99422 0.00234 0.00000 1000 1.99469 0.00162 1.99468 0.00840 0.00054 1.99468 0.00166 1.99450 0.00286 0.00922 1.99474 0.00165 1.98177 0.03573 0.65016 1050 1.99470 0.00188 1.99164 0.21029 0.15347 1.99473 0.00200 1.99472 0.00242 0.00029 1.99476 0.00181 1.99201 0.00213 0.13796 1100 1.99520 0.00164 1.99497 0.12952 0.01153 1.99515 0.00160 1.99514 0.03672 0.00061 1.99516 0.00167 1.98336 0.00214 0.59125 1150 1.99528 0.00105 1.99502 0.06464 0.01302 1.99532 0.00099 1.99528 0.00396 0.00200 1.99534 0.00108 1.99275 0.01495 0.12990 1200 1.99552 0.00114 1.99506 0.22874 0.02320 1.99552 0.00116 1.99546 0.00484 0.00301 1.99548 0.00116 1.99293 0.00158 0.12779 1250 1.99568 0.00216 1.99545 0.22779 0.01138 1.99567 0.00218 1.99565 0.00250 0.00100 1.99575 0.00212 1.99260 0.03187 0.15828 1300 1.99586 0.00093 1.99468 0.09240 0.05899 1.99593 0.00083 1.99585 0.00218 0.00403 1.99596 0.00088 1.99355 0.00142 0.12094 1350 1.99592 0.00162 1.99560 0.28995 0.01618 1.99602 0.00155 1.99602 0.00357 0.00000 1.99603 0.00157 1.99383 0.00204 0.11013 1400 1.99625 0.00198 1.99556 0.21323 0.03448 1.99626 0.00186 1.99626 0.00373 0.00000 1.99622 0.00198 1.99388 0.00212 0.11747 1450 1.99636 0.00132 1.99624 0.22506 0.00610 1.99638 0.00124 1.99637 0.00219 0.00060 1.99645 0.00119 1.99386 0.00167 0.12965

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For data rate of 5.5Mbps as tabulated in Table 3.11, the average received throughput increases as payload size increases for all the three networks as stated above except at some payload sizes. Network with SMP1 has the highest average received throughput compare to the network with SMP2 and SMP3. At higher payload size they all have comparable throughput as shown in the Figure 3.9.

Figure 3.9: Sender and Receiver average Throughput at 5.5Mbps.

From Figure 3.9 we can also observe that at lower payload size for SMP1 and SMP2 from 400 bytes up to 800 bytes, and for SMP3 from 400 bytes to 1200 bytes the network experiences packet loss in which results the network throughput at the range of PL’s is relatively smaller. The reason could be the narrow IFG between packets at the sender creates more strain in the short distant network [31, 39]. Furthermore the data rate at the lower layer (layer

1 and 2) for shorter PL are higher than longer PL’s which ultimately might lead to loss of packets in the network. The difference between in the network throughput of SMPs might be due to the difference in SMP’s CPU capability, OS and their tethering software. SMP1 has smaller CPU RAM size but the same CPU processing speed with SMP2. Their difference might

53

be due to the tethering software treatment of packets as previously discussed in section 3.4.2. The tethering software of SMP1 holds packets for longer period of time and keeps them from getting lost in the network. SMP3 has lowest throughput this is due to it is limitation in CPU RAM and processing speed.

As reported in [13] it has been evaluated for WLAN based setup as transparent network, i.e. the sender and receiver Application-perceived throughput values have very small difference. In this thesis it has been shown similar trend for R=1Mbp and R=2Mbps with some packets losses i.e. the sender is slightly greater than the receiver throughput. But for

R=5.5Mbps and shorter PL’s the sender and receiver throughput have at worst case of 46% difference due to packet losses as show in Figure 3.14 . This setback can be accounted due to the efficiency of packet processing and buffer management of the SMPs [2]. The packets enter the SMP USB interface (480Mbps) and leave the WLAN (11Mbps) interface, i.e. the incoming packets are pumping into high speed and exit with relatively bottleneck at the exit WLAN interface. Hence for shorter PL (400-800bytes for SMP2) the IFG of the consecutive packets is very short in effect it creates congestion overhead inside the SMP which might result in loss of packets.

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Table 3.11 Sender and receiver average throughput at R=5.5Mbps ΔT=1s

SMP1 SMP2 SMP3 PL Sender [Mbps] Receiver [Mbps] Sender [Mbps] Receiver [Mbps] Sender [Mbps] Receiver [Mbps] [Bytes] λ [%] λ [%] λ [%] Mean CoTV Mean CoTV Mean CoTV Mean CoTV Mean CoTV Mean CoTV 400 5.41229 0.00076 4.46200 0.02057 17.55803 5.41247 0.00098 3.81700 0.02405 29.47766 5.41243 0.00096 2.92700 0.01618 45.92078 450 5.41857 0.00118 4.76300 0.01974 12.09860 5.41930 0.00107 4.10200 0.02292 24.30755 5.41830 0.00126 3.13700 0.02872 42.10361 500 5.42485 0.00078 5.00200 0.02628 7.79470 5.42461 0.00103 4.35500 0.03018 19.71775 5.42420 0.00124 3.37400 0.01668 37.79728 550 5.42968 0.00072 5.23700 0.15189 3.54861 5.42955 0.00106 4.59200 0.17322 15.42569 5.42941 0.00116 3.51000 0.08685 35.35209 600 5.44006 0.00083 5.41500 0.06782 0.46063 5.44043 0.00091 4.60300 0.07978 15.39278 5.43967 0.00095 3.73500 0.01898 31.33775 650 5.44300 0.00083 5.44299 0.05550 0.00023 5.44323 0.00086 4.83200 0.06252 11.22914 5.44263 0.00106 3.81600 0.17684 29.88684 700 5.44600 0.00105 5.44569 0.06234 0.00566 5.44556 0.00094 4.86500 0.06979 10.66113 5.44495 0.00097 3.90600 0.07009 28.26380 750 5.45300 0.00088 5.45254 0.06438 0.00848 5.45268 0.00089 4.93900 0.07108 9.42066 5.45235 0.00101 3.97500 0.11778 27.09566 800 5.45400 0.00100 5.45361 0.02085 0.00724 5.45401 0.00089 5.44000 0.02090 0.25684 5.45328 0.00123 4.23600 0.08829 22.32198 850 5.45535 0.00088 5.45500 0.01106 0.00640 5.45494 0.01106 5.44900 0.01107 0.10889 5.45511 0.00111 4.53900 0.06908 16.79361 900 5.45645 0.00090 5.45600 0.01556 0.00819 5.45700 0.01556 5.45680 0.01556 0.00360 5.45665 0.00086 4.76900 0.08545 12.60205 950 5.46200 0.00075 5.46192 0.01197 0.00155 5.46200 0.00086 5.46162 0.01197 0.00694 5.46191 0.00093 4.97400 0.05511 8.93296 1000 5.46306 0.00075 5.46300 0.01184 0.00101 5.46241 0.00089 5.46200 0.01184 0.00743 5.46220 0.00113 4.75800 0.08122 12.89224 1050 5.46305 0.00094 5.46300 0.01182 0.00088 5.46400 0.00084 5.46377 0.01182 0.00417 5.46274 0.00103 4.99900 0.09392 8.48915 1100 5.39700 0.08205 5.39663 0.01201 0.00680 5.46400 0.00080 5.46399 0.01186 0.00024 5.46392 0.00088 5.36800 0.04973 1.75552 1150 5.43536 0.05595 5.43500 0.01590 0.00669 5.46800 0.00078 5.46789 0.01580 0.00210 5.46833 0.00078 5.18900 0.05676 5.10814 1200 5.46844 0.00077 5.46800 0.00594 0.00805 5.46900 0.00088 5.46845 0.00594 0.01002 5.46805 0.00103 5.46700 0.03226 0.01920 1250 5.46900 0.00068 5.46878 0.00919 0.00410 5.46919 0.00065 5.46900 0.00919 0.00340 5.46900 0.00106 5.46834 0.04282 0.01207 1300 5.47200 0.00094 5.47191 0.00504 0.00171 5.47250 0.00069 5.47200 0.00504 0.00915 5.47195 0.00087 5.47100 0.03159 0.01736 1350 5.47224 0.00077 5.47200 0.00862 0.00442 5.47300 0.00063 5.47262 0.00861 0.00686 5.47200 0.00101 5.47182 0.02490 0.00329 1400 5.47119 0.00095 5.47100 0.00351 0.00348 5.47201 0.00075 5.47200 0.00351 0.00012 5.47100 0.00119 5.47043 0.03593 0.01042 1450 5.47200 0.00073 5.47163 0.00867 0.00670 5.47200 0.00065 5.47163 0.00867 0.00669 5.47131 0.00091 5.46900 0.01700 0.04222

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Another experiment was performed for longest PL=1472bytes without fragmentation in the WLAN link at R=5.2Mbps using Exp. No. 8. As shown in Figure 3.10 the average throughput at the receiver are estimated 5.1185Mbps, 5.1187Mbps and 5.099Mbps for each network having SMP1, SMP2 and SMP3 respectively. From this we can see that the received throughput for the networks of using SMP1 and SMP2 have more or less same average throughput but they have greater average throughput than SMP3. This decrease in throughput at the receiver side is contributed due to loss of packet roughly 1.9%.

Figure 3.10: Throughput vs Time at PL=1472 for R=5.2Mbps.

Moreover the difference in CoTV between sender and receiver for the network using SMP3 (1.2%) is much higher than SMP2 ( 0.021%) and SMP1 (0.074%). The network with SMP2 has lowest difference in CoTV between sender and receiver compared with the two SMPs. The CoTV

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difference shows burstiness of traffic in the network [13]. The statatstical throughput difference of the network might be contributed to by the difference in hardware and software capabilty of each SMP. The network throughput ECDF in Figure 3.11 using SMP1 and SMP2 also show close probability distribution between them. But ECDF distribution for SMP3 lies beneath the two SMP’s.

Figure 3.11: CDF graph for probabilty (P(X>x)) versut throughput

PL=1472byte at R=5.2Mbps.

This difference in average and distribution throughput of the network might be due to their hardware capability of the SMP’s. SMP3 have lower CPU processing capability compared between the two. Hence the average throughput of the network using SMP3 has the least, whereas SMP1 and SMP2 have small difference in CPU capability as shown in Table A.1.

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3.6 Packet Loss Ratio Analysis To observe the effect of transmission pattern variation on packet loss the three experiments performed at R 1Mbps, 2Mbps and 5.5Mbps are used here. For R=8kbps there were not noticeable packet losses in the network traces. Hence the packet loss ratio for lower R are considered negligible. From Table 3.12, Table 3.13 and Table 3.14, it’s observed that when the data rate increases from 1Mbps to 5.5Mbps the packet loss incresease. And for each increase in the payload size the packet loss decreases for each data rate.

Figure 3.12 Packet loss ratio at R=1Mbps.

For R=1Mbps as can be seen in Figure 3.12 and Table 3.12, the packet loss ratio of network having SMP3 is relatively higher than that of the network with SMP1 and SMP2 for each payload size. At lower payload size there is a packet loss in all the SMPs. Since small packets have relatively higher ratio of packet header, their link layer R is greater than the nominal R, hence they are more sensitive to packet loses than large payload size

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packets [31].

Table 3.12 Packet loss ratio at R=1Mbps. Packet Loss Ratio at R=1Mbps P L SMP1 [%] SMP2 [%] SMP3 [%] [Byte] 400 0.02160 0.20658 0.01071 450 0.03009 0.15491 0.00602 500 0.00602 0.14001 0.00201 550 0.00100 0 0.00802 600 0.00739 0.00902 0.00902 650 0.01303 0 0.53007 700 0.00301 0.00100 0.02705 750 0.00401 0.00739 0.01302 800 0.01202 0.41569 0.01903 850 0.00300 0.10616 0.15324 900 0.00300 0.09414 0.01702 950 0.00130 0.08354 0.01102 1000 0.13420 0.21127 0 1050 0.00901 0.23453 0.00991 1100 0.00400 0.07509 0.00400 1150 0.00501 0.06407 0.01629 1200 0.00767 0.07520 0.00300 1250 0.00544 0.05706 0.05033 1300 0.01201 0.06000 0.09009 1350 0.18690 0.06846 0 1400 0.02102 0.06105 0.01101 1450 0.00300 0.05387 0.00200

For R=2Mbps as shown in Figure 3.13 and Table 3.13. As it is said earlier at lower payload size there is a relatively higher packet loss especially at 400 bytes in SMP3 there is 1.3% loss. And as we continue to increase the data rate to 5.5Mbps the packet loss ratio increase became worse. The loss packet as a function of payload size for R=5.5Mbps is shown in Figure 3.14 and Table 3.14.

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Table 3.13 Packet Loss Ratio at R=2Mbps

Packet Loss Ratio at R=2Mbps

PL SMP1 [%] SMP2 [%] SMP3 [%] [Bytes] 400 0.00302 0.01092 1.38370 450 0.00192 0.00302 0.58330 500 0.00514 0.00101 0.01207 550 0.00045 0.00000 0.45349 600 0.00088 0.00050 0.01118 650 0 0.00428 0.00227 700 0.02308 0.00301 0.01752 750 0.03892 0 0 800 0.01357 0.00457 0.32252 850 0.00344 0.30211 0.00868 900 0.03209 0.00206 0.01294 950 0.00074 0.00100 0 1000 0.00054 0.00922 0.65016 1050 0.15347 0.00029 0.13796 1100 0.01153 0.00061 0.59125 1150 0.01302 0.00200 0.12990 1200 0.02320 0.00301 0.12779 1250 0.01138 0.00100 0.15828 1300 0.05899 0.00403 0.12094 1350 0.01618 0 0.11013 1400 0.03448 0 0.11747 1450 0.00610 0.00060 0.12965

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Figure 3.13 Packet Loss ratio at R=2Mbps.

From the Table 3.14 at R=5.5Mbps for network with SMP1, payload size range from 400 byte to 550 byte the packet loss ratio decrease from 17% to 7.7% and for the network with SMP2 payload sizes range of 400 bytes to 850 bytes the packet loss ratio decrease from 29.4% to 0.1%. For network having SMP3 the loss rate become higher and the range of payload size with loss become wider than network of SMP1 and SMP2 as shown in Figure 3.14.

The loss rate decreases with increasing payload sizes for each SMP. This is due to the fact that shorter payload sizes have higher ratio of lower layers headers (42bytes) in addition to the extra MAC and PHY wireless headers such as DIFS (50µs), PLCP Preamble (144bits) and Header (48bits), SIFS (10µs), ACK (304µs) and so on [29, 31]. Moreover for the same R the IFG for shorter length packets is smaller hence packets leaving the sender are more densely and have high probability of causing congestion in the network compared with longer packets.

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Figure 3.14 Packet Loss Ratio at the R=5.5Mbps.

Even though it is difficult to completely rule the loss effect inside the network is solely due to SMPs. But as previous section show GW and

AP have less chance of losing packets at the given range of PL‘s. The loss of packets in the network might be most likely contributed due to sender (SMP) hardware, OS, and application software activity and in addition to the wireless link losses. By repeating same experimental processing with same setting the losses using different SMPs show differently with trend of losses in the end to end network. As media have less interference from the surrounding, the packet losses contributed by WLAN link remain nearly the same range for each SMP. But since SMPs have limited hardware and software resources the loss rate difference might be due to the difference in hardware, software and network stack in their operating systems.

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Table 3.14 Packet Loss Ratio at R=5.5Mbps Packet Loss Ratio at 5.5Mbps PL SMP1 SMP2 SMP3 [bytes] [%] [%] [%] 400 17.55803 29.47766 45.92078 450 12.0986 24.30755 42.10361 500 7.7947 19.71775 37.79728 550 3.54861 15.42569 35.35209 600 0.46063 15.39278 31.33775 650 0.00023 11.22914 29.88684 700 0.00566 10.66113 28.2638 750 0.00848 9.42066 27.09566 800 0.00724 0.25684 22.32198 850 0.0064 0.10889 16.79361 900 0.00819 0.0036 12.60205 950 0.00155 0.00694 8.93296 1000 0.00101 0.00743 12.89224 1050 0.00088 0.00417 8.48915 1100 0.0068 0.00024 1.75552 1150 0.00669 0.0021 5.10814 1200 0.00805 0.01002 0.0192 1250 0.0041 0.0034 0.01207 1300 0.00171 0.00915 0.01736 1350 0.00442 0.00686 0.00329 1400 0.00348 0.00012 0.01042 1450 0.0067 0.00669 0.04222

Hence based on the management of buffer size in the transmission process inside the SMP, the threshold number of packet allowed in the queue determines the contribution of the SMP in packet loss. At higher R=5Mbps the loss for SMP3 is very huge, it has been investigated that persistent traffic injection would create severe overheating which would trigger the drainage of internal battery even when it is plugged to the GW drawing power. This overheating problem was not common for SMP1 and SMP2. Particularly SMP2 has better stability in power management. This effect might be due to the nature of battery management, operating system and hardware communication processes.

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3.7 Throughput difference between DPMI and Iperf. To observe the estimated throughput difference between DPMI and Iperf, the Iperf at the SMP and the receiver are made to display the bitrate at each interval of 1 s throughout the observation time of 100 s. From Exp. No. 9 as shown in Table 3.15 and Table 3.16, the throughput of DPMI is greater than the Iperf measured output at the receiver. However, the calculated throughput and the Iperf output reported result for both SMP and receiver shows small differences.

In addition the standard deviation of DPMI is less than the receiver workstation and sender SMPs. This shows a sign of loss or burtiness in the network but under the given experimental condition this is not the case because the sending rate (1Mbps) is much less than the available data rate (11Mbps). Furthermore the standard deviation comparison between DPMI and Iperf depend on the type of SMP. SMP1 and SMP2 have less throughput standard deviation as show in Table 3.16. SMP3 has higher standard deviation throughput compared with the two SMPs.

Table 3.15 DPMI vs. Iperf average throughput output at R=1Mbps

Average Throughput at R=1Mbps SMPs Sender Receiver DPMI (SMP) (PC) SMP1 1000070 1000188 999835.2 SMP2 1000070 999956.4 999952.8 SMP3 996025 997389.1 991368

As previous sections 3.5 and 3.6 show, at this payload size and R the network has little chance of loss of packets. This difference is mainly because of the accuracy of the devices capacity i.e. SMP and receiver, compared to the DPMI as reported [12]. DPMI reports fractional PDUs at each sampling interval but for all the SMPs and receiver the Iperf output doesn’t show fractional bitrate throughout the observation interval. And the accuracy of the Iperf is dependent on the timestamp at each end point. The SMP and receiver can only offer timestamp accuracy in microseconds to the

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Iperf which is higher than the DPMI (<100ns) [15, 36]. Most importantly SMPs have very limited resource in hardware and OS. Longer period traffic injection into SMPs would deteriorate the network performance of SMPs (by more than 50%) hence using SMPs as a MP would lead to erroneous result.

Table 3.16 Standard deviation DPMI vs. Iperf at R=1Mbps. Std.dev [bps] SMP SMP DPMI Receiver

SMP1 4397.01 7142.91 7282.04 SMP2 2856.25 3303.96 3903.93 SMP3 85081.1 26245.9 90584.8

As show in Figure 3.15, Figure 3.16 and Figure 3.17, the SMPs throughput graph and the DPMI show a similar trend while the receiver show deviated from the sender. The throughput of SMP2 has smaller upper hand compared with SMP1 but the standard deviation is vice versa. SMP3 has lowest average throughput than the two SMPs.

Figure 3.15 Throughput variations for DPMI vs. Iperf on SMP1.

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As reported in [2] et. al. they are able to compare the network performance of 3G network with Iperf like measurement tool. But application level measurements are seriously affected by the OS, primarily scheduling and network stack process [11, 12]. In comparing the SMPs, the hardware, software, OSs, network stack architecture and interference play important roles. Hence due to the above mentioned reason it is difficult to get accurate results when we measure network performance of SMPs using application software measurement tools.

In the next chapter the paper will be finalized by discussing the key points and contributions that the thesis provide. In addition, future works will be proposed.

Figure 3.16 Throughput variations for DPMI vs. Iperf on SMP2.

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Figure 3.17 Throughput variation for DPMI vs. Iperf on SMP3.

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CHAPTER 4

Conclusions and Future work 4.1 Conclusion In this paper we discussed the effect of traffic pattern on OWD, throughput and packet loss on SMP network performance and their comparison with each other based on the selected metrics. The estimation of these parameters was conducted using hardware based experiment which can give timestamp accuracy of less than 100ns. The experimental setup was controlled to ensure less effect in underwent experimental procedure.

From experimental data we were able to answer our research questions, the first RQ1: aimed to answer the effect of transmission pattern on OWD, from the experimental results as payload size increase the minimum OWD of the network also increase for each SMP. Using simple linear regression curve- fitting the minimum OWD and payload size have approximately direct relationship. Based on the minimum OWD comparison, the network with SMP2 has the lowest OWD and SMP3 has the highest OWD. The minimum OWD of the SMPs directly relates with the efficiency of SMP in forwarding packets. The effects of GW and AP on OWD of the network have been evaluated. The contribution of GW and AP on the overall OWD variation of the network is evaluated to be insignificant. Moreover it has been investigated that the OWD variation and huge spikes are due to the retransmission of 802.11 algorithms in MAC and PHY layer. In this course of action particularly the stop and wait ARQ for 802.11b technique plays a great role in packet loss and OWD distribution of the network.

From the second research question RQ 2: our aim is to observe the effect of transmission pattern on throughput of the SMPs. From the results we observed that for each increase in payload size the throughput also increases slightly for each SMP. At higher payload sizes the IFG between packets

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becomes wider for a given R, so the probability of packet loss decrease which results in higher throughput but for shorter PL the opposite phenomenon was observed i.e. the network tends to lose packets. Comparing the SMPs based on throughput, we have observed that in addition to the SMPs hardware performance the underlying tethering application has advantage in handling buffer of the SMPs during forwarding of the packets. Using the third research question RQ 3: the effect of transmission patterns on packet loss ratio of the network is evaluated, the experimental result shows that for each increase in data rate the packet loss ratio also increases for all the SMPs.

In our last RQ 4: The difference in SMPs throughput between the common network performance evaluation tool Iperf and DPMI have been evaluated. From the estimated result we have seen that there is a throughput difference between DPMI and Iperf. In our analysis the DPMI result is compared with the receiver side throughput and DPMI gives better accuracy in throughput measurement. The estimated throughput of the network is affected due to the accuracy of the timestamp of SMPs, consideration of fractional PDU, the limitation of hardware and software resources of the SMPs. From each experiment and comparison of SMPs using higher precision experimental setup, application developers can tune their design based on the network performance of SMPs to enhance the QoE for end users.

4.2 Future Work  To extend the work with more SMPs having variety of platforms.  To analyze the performance of the SMPs on a controlled 3G network.  In our work we used UDP protocol as a transfer protocol, as a future work we propose the same experiment to be performed on TCP protocol.

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APPENDIX A SMP Specification Overview

A.1 SMP Specification Overview

Platform RAM Processor Operating Wireless USB Size Speed System LAN Support 768 1GHz Scorpion Android Wi-Fi USB 2.0 HTC MiB processor. OS. v2.2 802.11 client. Desire Adreno205 (Froyo). b/g/n. 480Mbit/s HD GPU. upgradable DLNA. micro-USB A9191 Qualcomm to v2.3 Wi-Fi MSM8255 hotspot Snapdragon HTC 448 1GHz Scorpion Microsoft Wi-Fi USB 2.0 HD2 MiB processor. Windows 802.11 client. T8585 Adreno200 Mobile 6.5 b/g. Wi-Fi 480Mbit/s GPU. Professional router micro-USB Qualcomm QSD8250 Snapdragon chipset Sony 256 528MHZ Microsoft Wi-Fi USB 2.0 Ericsson MiB Qualcomm Windows 802.11 b/g client. Xperia MSM7200A Br Mobile6.1 480Mbit/s X1a owse devices Professiona mini-USB based on this l AKU microprocessor 1.2.0

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APPENDIX B

Additional Experimental Results

Figure B. 1 Minimum OWD of the Network at IFG=1s.

Figure B. 2 Minimum OWD of the Network at IFG=2s.

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Figure B.3 Minimum OWD of the network at 8ms.

Figure B.4 Histogram distribution of OWD for SMPs, GW and AP

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Table B. 1 Statistical results of OWD at IFG=1s PL=400bytes to PL=1450bytes.

SMP1[ms] SMP2[ms] SMP3[ms] PL (bytes) mean min max Std.dev mean min max Std.dev mean min max Std.dev 400 3.15 1.55 185.68 7.26 1.73 1.35 18.33 0.52 2.81 2.29 33.34 0.84 450 2.69 1.61 14.68 1.37 2.00 1.46 9.96 0.48 3.22 2.57 468.93 11.66 500 2.89 1.75 40.55 1.65 2.09 1.55 6.96 0.47 3.04 2.49 8.37 0.48 550 2.97 1.85 16.36 1.46 2.18 1.64 33.56 0.68 3.12 2.62 8.93 0.47 600 3.02 1.91 17.25 1.40 2.21 1.73 5.85 0.44 3.20 2.70 19.62 0.58 650 3.13 2.02 19.91 1.45 2.31 1.80 20.88 0.71 3.30 2.81 10.76 0.49 700 3.19 2.12 19.19 1.42 2.43 1.88 13.40 0.53 3.39 2.89 12.45 0.55 750 3.30 2.17 16.45 1.41 2.52 1.97 14.34 0.53 3.67 2.99 317.06 7.85 800 3.34 2.29 13.08 1.36 2.60 2.05 11.94 0.51 3.68 3.04 199.51 4.92 850 3.49 2.36 13.91 1.44 2.70 2.13 17.87 0.60 3.64 3.27 17.46 0.57 900 3.54 2.46 18.47 1.43 2.79 2.18 44.51 0.89 3.74 3.24 35.52 0.93 950 3.64 2.53 28.34 1.43 2.86 2.32 16.30 0.61 4.18 3.33 450.84 11.24 1000 3.74 2.60 25.41 1.46 2.90 2.34 20.75 0.53 3.95 3.40 20.69 0.67 1050 3.80 2.73 12.99 1.39 3.04 2.47 12.92 0.52 4.03 3.47 11.06 0.52 1100 3.89 2.83 21.95 1.40 3.17 2.56 55.06 1.15 4.15 3.56 34.41 0.97 1150 4.27 2.90 117.98 4.92 3.25 2.65 65.74 1.14 4.22 3.77 29.24 0.84 1200 4.43 2.97 183.52 6.36 3.38 2.72 14.12 0.70 4.43 3.87 153.93 4.13 1250 4.46 3.09 120.44 4.89 3.49 2.82 21.51 0.83 4.44 3.85 12.90 0.72 1300 4.64 3.17 200.77 6.64 3.60 2.89 18.35 0.81 4.51 3.94 25.05 0.88 1350 4.62 3.24 121.45 4.80 3.71 2.99 25.51 1.01 4.57 4.03 8.54 0.56 1400 4.71 3.30 154.55 4.60 3.77 3.07 19.99 0.85 4.63 4.11 14.00 0.52 1450 4.75 3.42 119.40 4.28 3.96 3.16 26.52 1.23 4.98 4.38 38.05 1.18 Slope (µs/byte) 1.789 1.716 1.832 Intercept (µs) 836.01 670.10 1601.26 Corr.

Coff (%) 99.97 99.97 99.74

260

015 096

Regression

=1.832X+1601.

Y=1.789X+836. Y=1.716X+670. Y

77

Table B. 2 Statistical results of OWD at IFG=2s PL=400bytes to PL=1450bytes.

SMP2[ms] SMP3[ms] SMP1[ms]

PL [byte] mean min max Std.dev mean min max Std.dev mean min max Std.dev 400 1.78 1.33 13.96 0.69 2.86 2.48 10.96 0.59 2.75 1.66 26.15 1.85 450 1.88 1.46 15.00 0.67 2.98 2.57 7.46 0.58 2.86 1.71 15.82 1.69 500 2.01 1.56 80.39 2.52 3.09 2.68 14.33 0.75 3.17 1.90 52.86 2.36 550 2.04 1.65 5.45 0.48 3.19 2.76 8.64 0.64 3.19 1.99 16.29 1.69 600 2.16 1.72 46.94 1.56 3.27 2.84 9.35 0.67 3.19 2.06 14.32 1.57 650 2.24 1.85 10.04 0.57 3.38 2.94 47.88 1.54 3.21 2.10 17.22 1.64 700 2.30 1.88 6.33 0.48 3.53 3.00 109.03 3.41 3.35 2.24 18.93 1.64 750 2.49 1.97 85.94 2.70 3.51 3.04 10.52 0.64 3.46 2.32 18.83 1.66 800 2.49 2.09 9.27 0.57 3.75 3.21 117.65 3.68 3.57 2.37 17.76 1.75 850 2.55 2.12 9.82 0.61 3.67 3.29 10.02 0.58 3.68 2.50 14.12 1.68 900 2.67 2.24 6.70 0.58 3.82 3.36 32.24 1.15 3.86 2.53 21.33 1.88 950 2.76 2.35 10.95 0.64 3.93 3.49 11.75 0.78 3.91 2.60 22.21 1.78 1000 2.80 2.42 6.68 0.50 4.00 3.58 11.79 0.83 4.30 2.76 18.76 2.01 1050 2.98 2.51 56.89 1.81 4.12 3.65 59.23 1.87 4.52 2.95 20.74 2.07 1100 3.01 2.58 7.89 0.61 4.20 3.75 12.78 0.75 4.52 3.01 22.37 1.89 1150 3.07 2.65 6.03 0.56 4.26 3.84 13.27 0.73 4.58 3.13 16.03 1.83 1200 3.13 2.74 7.56 0.50 3.46 3.82 12.18 0.56 4.71 3.22 18.24 1.93 1250 3.45 2.82 5.34 0.61 4.04 3.83 9.92 0.72 4.75 3.29 15.93 1.86 1300 3.55 2.93 22.51 1.08 4.42 4.06 8.40 0.48 4.77 3.33 21.41 1.89 1350 3.77 3.00 10.70 1.18 4.52 4.13 9.52 0.53 4.79 3.45 24.58 1.78 1400 3.86 3.09 17.05 1.53 4.58 4.22 8.40 0.44 4.86 3.57 18.99 1.78 1450 3.76 3.19 22.95 1.16 4.85 4.39 15.20 0.52 4.99 3.55 17.51 1.86 Slope (µs/byte) 1.740 1.757 1.859 Intercept (µs) 666.9 1778.8 930.0 Corr.

Coff (%) 99.97 99.86 99.86

8

.

05

.

86

.

Regression Y=1,859X+930

Y=1,757X+1778

Y=1,740X+666

78

Table B.3 Statistical results of OWD at IFG=8ms for PL=400bytes to PL=1450bytes.

SMP1[ms] SMP2[ms] SMP3[ms] PL [Byte] Mean Min Max Std.dev Mean Min Max Std.dev Mean Min Max Std.dev

400 2.03 1.38 8.98 0.76 1.62 1.16 78.67 1.98 3.24 2.33 39.16 1.73 450 2.14 1.48 14.6 0.83 1.72 1.3 55.13 1.14 2.85 2.43 12.56 0.46 500 2.28 1.61 15.75 0.86 1.76 1.37 19.02 0.44 2.92 2.51 30.11 0.58 550 2.37 1.71 13.95 0.84 1.86 1.46 25.6 0.53 3.02 2.6 21.16 0.51 600 2.45 1.79 16.83 0.82 1.93 1.54 25.51 0.58 3.45 2.68 37.18 1.45 650 2.53 1.88 17.07 0.82 2.03 1.62 11.41 0.39 3.53 2.77 19.73 1.22 700 2.62 1.95 14.86 0.83 2.17 1.71 74.6 1.66 3.4 2.86 26.72 0.8 750 2.71 2.05 10.37 0.83 2.3 1.8 73.34 2.02 4.07 2.95 40.83 1.99 800 2.79 2.14 13.36 0.82 2.27 1.88 21.02 0.52 5.53 2.99 68.55 3.95 850 2.89 2.23 19.63 0.85 2.37 1.97 16.51 0.49 5.78 3.12 57.91 4.1 900 2.96 2.31 20.06 0.85 2.45 2.05 22.15 0.58 4.57 3.21 78.37 3.11 950 3.1 2.41 14.36 0.88 2.55 2.15 21.1 0.52 8.13 3.31 68.19 6.2 1000 3.15 2.5 14.94 0.83 2.6 2.22 25.72 0.57 8.68 3.4 101.8 6.85 1050 3.23 2.6 10.01 0.8 2.71 2.32 14.84 0.46 4.94 3.48 44.14 2.96 1100 3.32 2.67 10.68 0.82 2.77 2.39 11.31 0.43 4.24 3.59 40.81 1.53 1150 3.39 2.76 21.02 0.89 2.87 2.48 17.14 0.5 5.17 3.66 56.82 3.43 1200 3.5 2.84 11.16 0.82 2.93 2.56 22.08 0.49 4.24 3.75 38.1 0.94 1250 3.6 2.92 12.61 0.87 3.06 2.64 50.34 0.99 4,27 3.82 36.54 0.76 1300 3.71 3.01 11.35 0.88 3.2 2.72 100.1 2.34 4.37 3.92 25.33 0.62 1350 3.81 3.1 22.43 0.93 3.24 2.81 14.35 0.51 4.47 4.01 15.03 0.6 1400 3.92 3.18 16.17 0.96 3.29 2.89 19.18 0.52 4.55 4.1 15.58 0.6 1450 4 3.28 20.46 0.94 4.24 2.99 93.88 6.87 4.8 4.37 37.28 0.75 Slope 1.774 1.703 1.813 (µs/byte) Intercept 713.3 517.3 1589 (µs) Corr. 99.98 99.98 99.79

Coff (%)

3

.

3X+1589

1

Regression

Y=1.8

Y=1.774X+713.3 Y=1.703X+517

79

APPENDIX C

Estimation of Average bitrate and throughput using PERL and bitrate c++ tool

##########automizescript.pl#########################

#! usr/bin/perl open ALL_FILE , "+>>bitratesonyericsson_5Mbps.txt" or die $!; print ALL_FILE "FILENAME:Sample:AVERAGEthru:STDthru:MinThru:MaxThru:CoTV:N_Zeros \n"; close ALL_FILE; for($j=50;$j<+1450;$j=$j+50) {

$z="udp_u_".$n."_and_ubu.".cap"; $y="udp_u_".$n."_So_and_ub8k".$i.".txt"; $k="udp_u_".$n."_Re_and_ub8k".$i.".txt"; system("/data/hmte09/bitrateold/bitrate-0.1 --ip.proto udp --ip.src 10.42.43.2 --ip.dst \192.168.2.24 -l 10 -q transport –m 1 $z > bitrate/$y"); system("/data/hmte09/bitrateold/bitrate-0.1 --ip.proto udp --ip.src 192.168.2.10 --ip.dst \192.168.2.24 -l 10 -q transport –m 1 $z > bitrate/$k"); system("perl avgFinal.pl $y"); system("perl avgFinal.pl $k");

}

#########Statistical Throughput Estimation ############################## #### avgFinal.pl #!/usr/local/perl #To open the file #use Math::BigFloat; my $file=$ARGV[0]; #declaring the file to be input by a user chomp($file); #leaves \n character from input

80

open MYFILE,"<", $file or die "the file doesn't exist" ; #opening a file $i=0; while() {

$string=$_; #reading a file line by line excluding '\n' character

($x,$y)=split(/:/, $string); #if($y>0 && $y=~/\D/){ #if($string=~/EOF/)

#last; if($y>0 && $y=~/\W/){ $myarray[$i]=$y; $i++;

} } close MYFILE; print "the deleted element: $myarray[0]\n"; print "the last deleted element $myarray[@myarray-1]\n"; $n=@myarray; print "\n size before delete:$n\n";

$fst=shift(@myarray); # removing the first zero element $fst=shift(@myarray);

$lst=pop(@myarray); #removing the last zero average bitrate $u=@myarray; print "\n the new Fist element is: $myarray[0]\n"; print "\n the new Last element is:$myarray[@myarray-1]\n"; print "\n the new size: $u: \n";

#@myarray=split(/:/, $string); #changing the file to array form by split() function @sortedarray=sort {$a <=> $b} (@myarray); #sorting in ascending order using sort() function #print "@sortedarray \n"; $size =@myarray; #finding the size of the array which inlcudes enter key to avoid my $sum=0; my $average=0; for ($k=0;$k<$size;$k++) { $sum=$sum+$myarray[$k]; } $average=$sum/($size); my $sqtotal=0; my $std=0;

81

for (my $m=0;$m<$size;$m++) { $sqtotal += ($average-$myarray[$m]) ** 2;

} $std = ($sqtotal / ($size-1)) ** 0.5;

$myout=0; #How many zeros are in the average bit rate samples for($in=0;$in<@$size;$in++) { if($myarray[$in]==0) { $myout++; } }

$average=sprintf("%.3f",$average); $std=sprintf("%.3f",$std); $last=sprintf("%.3f",$sortedarray[$size-1]); $fisrt=sprintf("%.3f",$sortedarray[0]); print "\n The average value of the array is: $average"; print"\n standard deviation is : $std \n"; print "\n The maximum throughput is: $sortedarray[$size-1] \n"; print "\n The Minimum throughput is: $sortedarray[0] \n"; print "\n number of elements samples intervals: $size \n"; my $CoTV=$std/$average; #print "\n $size \n"; #Print "\n The first element in the array is:$firstElement"; open ALL_FILE , "+>>bitratesonyericsson_5Mbps.txt" or die $!; print ALL_FILE "$file:$size:$average:$std:$fisrt:$last:$CoTV:$myout\n"; close ALL_FILE;

82