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Latency and Throughput Optimization in Modern Networks: a Comprehensive Survey Amir Mirzaeinnia, Mehdi Mirzaeinia, and Abdelmounaam Rezgui

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Latency and Throughput Optimization in Modern Networks: a Comprehensive Survey Amir Mirzaeinnia, Mehdi Mirzaeinia, and Abdelmounaam Rezgui READY TO SUBMIT TO IEEE COMMUNICATIONS SURVEYS & TUTORIALS JOURNAL 1 Latency and Throughput Optimization in Modern Networks: A Comprehensive Survey Amir Mirzaeinnia, Mehdi Mirzaeinia, and Abdelmounaam Rezgui Abstract—Modern applications are highly sensitive to com- On one hand every user likes to send and receive their munication delays and throughput. This paper surveys major data as quickly as possible. On the other hand the network attempts on reducing latency and increasing the throughput. infrastructure that connects users has limited capacities and These methods are surveyed on different networks and surrond- ings such as wired networks, wireless networks, application layer these are usually shared among users. There are some tech- transport control, Remote Direct Memory Access, and machine nologies that dedicate their resources to some users but they learning based transport control, are not very much commonly used. The reason is that although Index Terms—Rate and Congestion Control , Internet, Data dedicated resources are more secure they are more expensive Center, 5G, Cellular Networks, Remote Direct Memory Access, to implement. Sharing a physical channel among multiple Named Data Network, Machine Learning transmitters needs a technique to control their rate in proper time. The very first congestion network collapse was observed and reported by Van Jacobson in 1986. This caused about a I. INTRODUCTION thousand time rate reduction from 32kbps to 40bps [3] which Recent applications such as Virtual Reality (VR), au- is about a thousand times rate reduction. Since then very tonomous cars or aerial vehicles, and telehealth need high different variations of the Transport Control Protocol (TCP) throughput and low latency communication. These applica- have been designed and developed. tions have to communicate through variety of network and There are some main capabilities required to achieve. First, traffic characteristics that are evolved into massive diverse handling the congestion may cause the delay of a certain flow cases. High Bandwidth Product (HBD), Multi paths, Data which is not desired for the network users. Resource maximum Centers, and a variety of wireless networks are some of the utilization to serve more users is also desired. Therefore main domains that their traffic needs to be managed properly. increasing the number of users and their transmission rate Latency and throughput optimization (L&T) are even more is desired and this increasing makes it more challening to complex problems to achieve in such a huge environment. optimize user’s latency and throughput. Transport control is Network congestion, rate control, and load balancing are an approach to improve the (L&T) of the networks. There are some of the main approaches to imrpove the latency and two main class of throughput of the different networks. We have found that, TCP is known as connection oriented transmission which thus far, different networks need customized control mech- means senders wait to receive an acknowledgement signal anism to achieve optimum performance. However Machine packet. To continue transmission the sender needs to receive Learning (ML) algorithms (specially deep learning based) are acknowledgment of the previous packet in a certain timeout a promising approach to achieve such an ambitious adaptive period. Otherwise the previous packet which is not acknowl- intelligent mechanism to optimally work in different network edged yet would be retransmitted. Packet by packet data environments and serve different traffic characteristics. In 2017 transmission and waiting for its acknowledgment wastes time arXiv:2009.03715v1 [cs.NI] 1 Sep 2020 Silver et al. [1], [2] designed a deep learning based computer and network resources. Therefore the idea of sliding windows program to play Go that was able to defeat a world champion evolved to control the transmission rate more efficiently while challenge. These results show that ML algorithms are able it can also check the acknowledgment of the transmitted to achieve beyond human capability. Games are the best packet. Additionally there are circular sequence numbers to benchmark problems to test machine learning algorithms to keep track of the transmitted packets. control a sequence of decisions. Network data rate, congestion Generally, there are different classes of approaches proposed control and load balancing as main parts contributing on to control the rate in networks. Rate based [4] ( streaming (L&T) are also a decision sequence making problem that could media protocols) and window based are the two most common be resolved through deep learning algorithms. In this paper, groups of congestion control mechanisms. Different traffic we present a comprehensive survey of the network rate and packet scheduling is another way to address the congestion congestion control problem starting from the beginning till problem in the networks which is covered in more detail in recent intelligent approaches. section II-E1. Congestion Control mechanisms could be also classified A. Mirzaeinia is with the Department of Computer Science and Engineer- by incremental modification. Only sender needs modification; ing, New Mexico Institute of mining and Technology, Socorro, NM, 87801 sender and receiver need modification; middle nodes (like USA e-mail: [email protected] A. Rezgui is with Illinois State University. switches or routers) need modification; sender, receiver and Manuscript received XXXX; revised XXXX. middle nodes need modification. Another way to classify the READY TO SUBMIT TO IEEE COMMUNICATIONS SURVEYS & TUTORIALS JOURNAL 2 CC Feedback signals packet inter-time (scheduling time). Figure1. shows the main signals to detect the network congestion. Drop/loss based. TCP flows wait for the packet to receive LOSS RTT ECN RTT Gradient acknowledgement to continue with other packets to transmit. Time out is also used to control the time each transmitter TCP Tahow TCP Vegas DCTCP [5] Timely [8] has to wait until receiving the acknowledgment. If transmitter TCP Reno Fast TCP DCCP [6] Others [9], [10] SACK Westwood XCP [7] does not receive acknowledgement by end of the timeout BIC Jersey period then it is assumed that either transmitted packet or its acknowledgment is lost (either dropped or failed). Packet loss Fig. 1. feedback signals. is sensed after a time out or receiving a NAK packet. For example Additive Increase Multiplicative Decrease (AIMD) based TCP-Tahoe is slow to start when a drop happens, TCP- CC mechanisms could be based on performance metrics that NewReno and TCP-selective ACK (SACK) [35], [36] are a they tackle. High bandwidth-delay product networks, lossy more conservative slow start when a three duplicated acknowl- links, fairness, advantage to short flows, and variable-rate links edge packet is sensed, AIMD-FC [37], Binomial Mechanisms are some of the most commonly tried mechanisms. Congestion [38], HIGHSPEED-TCP (for high BDP) [39], BIC-TCP [40]. control mechanisms could be also classified by fairness criteria As another step, General AIMD Congestion Control GAIMD such as Max-min fairness and proportionally fair. Authors in is proposed to modify the window size increase/decrease rate [11] surveyed the fairness-driven queue management mecha- [41]. nisms. RTT Based. RTT based mechanisms measure the round trip When a CC protocol is about to be designed, there are some time for each packet to find the network congestion level. key points that need to be taken into account. 1- Competitive Different congestion levels cause different round trip times. flows have no information from each other. 2-The number Therefore the transmitter can react based on the measured of competitive flows are not known to flow sources. 3-Flows time to control the network congestion. TCP vegas [42], fast sources are not aware of available resources and bandwidth. TCP [43], TCP-WESTWOOD [44], TCP-JERSEY [45] are Network and traffic transmitters need to react against con- some of the known mechanisms that relys on network RTT. gestions they can sense. Almost all network devices are RTT based mechanisms usually suffer from unfairness because equipped with some input, output or input/output buffers [12]– the flows with shorter RTT are able to attain more shared [15] to manage the congestion to some extent. However net- bandwidth when they compete with flows with longer RTT. work excessive congestion causes the network devices buffer These mechanisms are hard to develop in networks where RTT to overflow. Drop tail is the first method that is performed is in the microsecond range such as Data Center networks. in routers/switches to control the incoming of excessive traf- Explicit Congestion Notification (ECN) Based. In ECN fic. Drop tail causes a situation called flow synchronization. based mechanisms traffic packets are marked by intermediary Flow synchronization reduces the lower bandwidth utilization. nodes in their way when switch buffers are build up to a certain Different Active Queue Management have been proposed to threshold. [5], [46]. Therefore transmitters start to reduce the manage network buffering. AQM schemes are surveyed in transmission rate. ECN flag bit will be set in middle way [16]. Random Early Detection (RED) is designed to overcome routers if they encounter a certain level of congestion. the flow synchronization issue, and Weighted RED (WRED) RTT Gradient, recently gradient of the RTTs are proposed is developed to provide fairness to responsive TCP and non- as more informative feedback from the networks [8]–[10]. responsive
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