ABSTRACT GANJI, ANIRUDH. Network Performance Analysis of IoT, Cloud and Mobile systems. (Under the direction of Muhammad Shahzad.) Today’s end-user and enterprise applications heavily rely on large scale network infrastructure and novel network protocol implementations to provide their intended functionality. An ever- increasing demand for these applications has outpaced the rate of innovation and testing of the protocols and infrastructure necessary to support those demands. Hence, existing network pro- tocols and systems are being repurposed to facilitate these new application paradigms. But this repurposing can sometimes have unintended consequences on the network performance and can create performance bottlenecks in the system. Therefore, it is important to study such scenarios to not only understand what these bottlenecks are but also to find ways to alleviate these performance issues. In this work, I look at three such network scenarios, namely Internet of Things (IoT), Cloud and Datacenter networks, and Network protocol stacks in mobile systems. For the IoT scenario, I first provide a novel measurement framework to accurately measure fine-grained performance metrics in resource constrained IoT nodes. Next I look at the performance issues encountered when using an existing commercial wireless solution, like WiFi, to facilitate dense IoT deployments. Contrary to common intuition, our measurements showed that at higher IoT traffic rates the rate control mechanism by the Transmission Control Protocol (TCP) is a larger performance bottleneck when compared to frame collisions due to wireless medium access. Next, for Cloud and Datacenter networks, I mainly focus on the impact of TCP protocol on the performance of typical distributed workloads found in datacenter networks. Specifically, I look at how the choice of TCP in a cloud network environment can impact the application level metrics of a tenants workload. In another study, I evaluate the repercussions of coexisting TCP traffic from multiple different senders in a datacenter network in terms of its effect on the workload performance. Our evaluations have shown us that choosing a TCP variant in datacenter networks is not a trivial problem and heavily depends on the network infrastructure, type of workload or on the coexisting traffic. Moreover, I also demonstrated that low-level throughput unfairness caused due to coexistence of traffic from senders using different TCP variants can result in appropriate application-level performance unfairness in datacenter networks. Finally, for mobile systems, I look at how QUIC, a recent application-level transport protocol, integrates with the Android networking stack and the functional issues encountered during typical mobile traffic scenarios. I found that the benefits of QUIC are not universal unlike what is usually seen in its server deployments. Moreover, I also found that current QUIC implementation in Android can result in severe performance issues during mobile roaming scenarios. © Copyright 2021 by Anirudh Ganji All Rights Reserved Network Performance Analysis of IoT, Cloud and Mobile systems by Anirudh Ganji A dissertation submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the Degree of Doctor of Philosophy Computer Science Raleigh, North Carolina 2021 APPROVED BY: Rudra Dutta Khaled Harfoush Ismail Guvenc Anand Singh (External Member) Muhammad Shahzad Chair of Advisory Committee DEDICATION This dissertation is dedicated to my parents! ii ACKNOWLEDGEMENTS Working towards my Ph.D. has been a wonderful experience and I would like to thank everyone who has been part of this journey. First and foremost, I would like to thank Dr. Shahzad for his continuous support, guidance and faith in my work. He is a wonderful teacher and had a profound impact in shaping my research career and my personal life. In many ways, I feel lucky to have him during this journey. I am also deeply grateful to Dr. Anand Singh for being a valuable support during my Ph.D. His guidance and wit helped me both in my academic and personal life. I thank the members of the advisory committee, Dr. Rudra Dutta, Dr. Khaled Harfoush and Dr. Ismail Guvenc for their valuable feedback, insights and encouragement! Many thanks to Dr. Christina Vlachou at HPE. She was an incredible resource during my intern- ship. I am extremely lucky to have a very supportive group of lab-members during my Ph.D. Raghav, Shaohu, Hassan Iqbal, Hassan Ali and Usman have not only been my lab-mates, but are also good friends. I feel I found good friends at the end of my program. I enjoyed every bit of my time at the Department of Computer Science at NC State. Not only did I form new friendships here, but I owe a lot of my professional development to this department. I also specially like to thank NSF and other funding agencies which funded my Ph.D. My numerous friends who have been very helpful during these five years in keeping me happy and entertained whenever I felt stressed. They were my pillar of strength during these stressful times. Thanks guys! Thank you Amulya for being the best friend you are. Many thanks to my family! They have been very patient, kind and understanding when I moved to a different country to do my Ph.D. Thanks mom and dad! Finally, I do not know how to say this, but woof woof (thank you) Milo. Adopting you was one of the best decisions in my life. Thanks for being the bundle of joy you are! iii TABLE OF CONTENTS LIST OF TABLES ......................................................... vii LIST OF FIGURES ........................................................ viii Chapter 1 Introduction .................................................. 1 Chapter 2 IoTm: A Lightweight Framework for Fine-grained Measurements of IoT Per- formance Metrics .............................................. 5 2.1 Introduction ..................................................... 5 2.2 Data Structure.................................................... 8 2.2.1 Construction ............................................... 8 2.2.2 Management ............................................... 10 2.2.3 Analysis................................................... 10 2.3 Query Processing Engine ............................................ 12 2.4 Applications and Evaluation.......................................... 15 2.4.1 Disk IO Operations per Process.................................. 15 2.4.2 Round Trip Latency per Flow.................................... 20 2.5 Related Work..................................................... 23 2.5.1 Sketches................................................... 24 2.5.2 Data Center Monitoring Framework .............................. 24 2.5.3 Other Measurement Schemes................................... 24 2.6 Conclusion ...................................................... 25 Chapter 3 Characterizing the Performance of WiFi in Dense IoT Deployments ....................................... 27 3.1 Introduction ..................................................... 27 3.2 Related work..................................................... 30 3.3 Overview of IEEE 802.11’s MAC & Aggregation............................. 31 3.3.1 Regular DCF................................................ 31 3.3.2 RTS/CTS-based DCF.......................................... 32 3.3.3 Frame Aggregation........................................... 32 3.4 Experimental Setup................................................ 33 3.4.1 Testbed Setup............................................... 33 3.4.2 IoT Traffics................................................. 33 3.4.3 Experiment Execution......................................... 35 3.5 Characterization of the IEEE 802.11n/ac’s MAC............................ 36 3.5.1 Throughput................................................ 36 3.5.2 RTS/CTS Bandwidth and Block Acknowledgments.................... 38 3.5.3 Frame Aggregation........................................... 39 3.5.4 TCP Pipelining.............................................. 40 3.6 Key Take-aways................................................... 41 Chapter 4 Choosing TCP Variants for Cloud Tenants - A Measurement based Approach .. 43 4.1 Introduction ..................................................... 43 4.2 Related Work..................................................... 46 4.3 Measurement Setup................................................ 46 iv 4.3.1 Network and System Parameters................................. 46 4.3.2 Measurement Metrics......................................... 47 4.4 Measurement Methodology.......................................... 48 4.4.1 Data Collection.............................................. 49 4.4.2 Evaluations ................................................ 49 4.4.3 Implications................................................ 51 4.5 Application Traffic Scenarios ......................................... 52 4.5.1 Topology and Data Collection................................... 52 4.5.2 Streaming Traffic ............................................ 53 4.5.3 Distributed IO .............................................. 54 4.5.4 Sort Workload............................................... 55 4.6 Implications ..................................................... 56 Chapter 5 Characterizing the Impact of TCP Coexistence in Data Center Networks ......................................... 58 5.1 Introduction ..................................................... 58 5.2 Related Work....................................................