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Smart Resource Allocation in Internet-Of-Things Smart Resource Allocation in Internet-of-Things: Perspectives of Network, Security, and Economics by Ruozhou Yu A Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Approved June 2019 by the Graduate Supervisory Committee: Guoliang Xue, Chair Dijiang Huang Arunabha Sen Yanchao Zhang ARIZONA STATE UNIVERSITY August 2019 ABSTRACT Emerging from years of research and development, the Internet-of-Things (IoT) has finally paved its way into our daily lives. From smart home to Industry 4.0, IoT has been fundamentally transforming numerous domains with its unique superpower of interconnecting world-wide devices. However, the capability of IoT is largely constrained by the limited resources it can employ in various application scenarios, including computing power, network resource, dedicated hardware, etc. The situation is further exacerbated by the stringent quality-of-service (QoS) requirements of many IoT applications, such as delay, bandwidth, security, reliability, and more. This mismatch in resources and demands has greatly hindered the deployment and utilization of IoT services in many resource-intense and QoS-sensitive scenarios like autonomous driving and virtual reality. I believe that the resource issue in IoT will persist in the near future due to technological, economic and environmental factors. In this dissertation, I seek to address this issue by means of smart resource allocation. I propose mathematical models to formally describe various resource constraints and application scenarios in IoT. Based on these, I design smart resource allocation algorithms and protocols to maximize the system performance in face of resource restrictions. Different aspects are tackled, including networking, security, and economics of the entire IoT ecosystem. For different problems, different algorithmic solutions are devised, including optimal algorithms, provable approximation algorithms, and distributed protocols. The so- lutions are validated with rigorous theoretical analysis and/or extensive simulation experiments. i Dedicated to my wife Rui Sun and my family ii ACKNOWLEDGMENTS Foremost, I would like to express my sincere gratitude to my Ph.D. advisor, Dr. Guoliang Xue, for his continuous support and guidance throughout my study at Arizona State University, for his knowledge, patience, enthusiasm, and probity that have deeply affected me, and most importantly, for his standing care and friendship during my hard times. Having him as my advisor is one of the most fortunate things in my life. This dissertation would not have been possible without the academic guidance and freedom he has given me over the last six years. My Ph.D. study has been supported by NSF grants 1461886, 1704092, and 1717197. I would like to thank my dissertation committee members, Dr. Dijiang Huang, Dr. Arunabha Sen, and Dr. Yanchao Zhang, for their advices and comments. I am honored to have worked with an incredible group of co-authors and collaborators. I am specially thankful to Dr. Jun Huang and Dr. Dan Li, who led me onto the path of academic research. I want to thank my academic siblings, Jian Tang, Satyajayant Misra, Xi Fang, Dejun Yang, Lingjun Li, Xinxin Zhao, and Xiang Zhang, for their precious advices and support for my study, career and life. I am also very grateful to my colleagues and friends: Jian Cai, Xinhui Hu, Yiming Jing, Vishnu Teja Kilari, Bing Li, Liangyue Li, Yashu Liu, Jianqing Liu, Qiang Liu, Yaozhong Song, Yinxin Wan, Siqi Wei, Haiqin Wu, Haitao Xu, Rui Zhang, Ziming Zhao, and Zhuoyang Zhou. Their friendship and care are what have helped me survive over the years. Finally and most importantly, none of my achievements would have been possible without the love and patience from my wife and my family. My most sincere thanks go to my wife Rui Sun, for her endless support and caring over the years. She is the reason, the why, for which I have strived hard in the past and will keep on striving iii in the future. I am very thankful to my parents, Bing Yu and Lianna Xie, for their unconditional and forbearing love and support throughout my life. iv TABLE OF CONTENTS Page LIST OF TABLES . xii LIST OF FIGURES . xiii CHAPTER 1 INTRODUCTION . 1 1.1 Motivation . 1 1.2 Overview . 3 1.3 Summarized Contributions . 5 1.3.1 Part I: Network Resource Allocation in IoT . 5 1.3.2 Part II: Robust Security Deployment in IoT . 8 1.3.3 Part III: Micropayment Routing in Blockchain-based PCN. 9 I NETWORK RESOURCE ALLOCATION IN IOT 2 QOS-AWARE AND RELIABLE TRAFFIC STEERING FOR SERVICE FUNCTION CHAINING IN MOBILE NETWORKS . 13 2.1 Introduction . 13 2.2 Background and Related Work . 16 2.2.1 NFV and SFC. 16 2.2.2 Software-defined Mobile Networks . 17 2.3 System Model . 18 2.3.1 Network Topology . 18 2.3.2 Service Functions . 19 2.3.3 Traffic Model. 20 2.3.4 Feasible Routing Graph . 21 2.3.5 Reliability . 23 v CHAPTER Page 2.4 Problem Statement . 24 2.4.1 Problem Description and Formulation . 24 2.4.2 Computational Complexity . 25 2.4.3 Optimization Formulation . 25 2.5 Fully Polynomial-Time Approximation Scheme . 27 2.5.1 Dual Analysis . 28 2.5.2 Primal-Dual Algorithm . 30 2.5.3 Approximating Shortest Feasible Paths. 32 2.5.4 Algorithm Analysis . 32 2.5.5 Feasibility and Demand Scaling . 35 2.5.6 Extension to Multiple QoS Requirements . 38 2.6 Performance Evaluation. 38 2.6.1 Experiment Settings . 38 2.6.2 Evaluation Results . 40 2.6.2.1 Comparison with theoretical upper bound . 40 2.6.2.2 Comparison with baseline heuristics . 41 2.7 Conclusions . 44 3 PROVISIONING QOS-AWARE AND ROBUST APPLICATIONS IN INTERNET-OF-THINGS: A NETWORK PERSPECTIVE . 46 3.1 Introduction . 46 3.2 Background and Related Work . 49 3.2.1 Internet-of-Things and Fog Computing . 49 3.2.2 Network Service Provisioning . 50 3.2.3 Robustness Applications and Networks . 51 vi CHAPTER Page 3.3 System Model . 52 3.3.1 Infrastructure Model . 52 3.3.2 Application Model . 52 3.3.3 Basic Provisioning Model . 53 3.3.4 Robustness Model . 55 3.3.5 Notations . 57 3.4 Problem Statement and Complexity . 58 3.5 Single-Application Provisioning . 60 3.6 Multi-Application Provisioning . 61 3.6.1 Problem Formulation for PO-MAP . 62 3.6.2 An FPTAS to PO-MAP . 64 3.6.3 NO-MAP Formulation and Randomized Algorithm. 73 3.7 Performance Evaluation. 76 3.7.1 Experiment Settings . 76 3.7.2 Evaluation Results . 78 3.7.2.1 Single-Application Scenario. 78 3.7.2.2 Multi-Application Scenario . 83 3.8 Conclusions . 87 4 LOAD BALANCING FOR INTERDEPENDENT IOT MICROSER- VICES ............................................................ 88 4.1 Introduction . 88 4.2 Background and Related Work . 91 4.2.1 Microservices and Application Graph Models . 91 4.2.2 Application-level Load Balancing . 93 vii CHAPTER Page 4.3 System Model and Basic Formulation . 94 4.3.1 Application Model . 94 4.3.2 Infrastructure Model . 95 4.3.3 Basic Load Balancing Model . 96 4.4 QoS-aware Load Balancing . 98 4.5 Approximation Scheme Design . 103 4.5.1 Pseudo-Polynomial Time Optimal Algorithm . 104 4.5.2 Approximation Scheme for O-QLB . 106 4.5.3 Efficiency Enhancement . 110 4.6 Performance Evaluation. 112 4.7 Conclusions . 116 4.8 Appendix . ..
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