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Copyright by Pablo Caballero Garces 2018 The Dissertation Committee for Pablo Caballero Garces certifies that this is the approved version of the following dissertation: Design and Performance of Resource Allocation Mechanisms for Network Slicing Committee: Gustavo de Veciana, Supervisor Albert Banchs Roca, Supervisor Jeffrey G. Andrews Francois Baccelli Sanjay Shakkottai John J. Hasenbein Design and Performance of Resource Allocation Mechanisms for Network Slicing by Pablo Caballero Garces. DISSERTATION Presented to the Faculty of the Graduate School of The University of Texas at Austin in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY THE UNIVERSITY OF TEXAS AT AUSTIN August 2018 Dedicated to my parents, my brother and Ghadi. Acknowledgments I would like to express my gratitude to my PhD supervisors Prof. Gustavo de Veciana and Prof. Albert Banchs. Their masterful and inspiring tutoring have deeply contributed to my growth, not only professionally but also personally. They helped and supported me during this fantastic adventure and have always stood by my side in the difficult moments. Your patience, encouragement and extraordinary guidance set an example that I would carry on during the rest of my life. I would also like to thank Dr. Xavier Perex-Costa, Prof. Jeffrey Andrews, Prof. Francois Baccelli, Prof. Sanjay Shakkottai, Prof. John Hasenbein and Prof. Evdokia Nikolova for their time and valuable comments on this dissertation that undoubtedly improved my approach and the final result. I want also to thank my lab-mates that helped and accompanied me during these intense years: Arjun, Jiaxiao, Yicong, Saadallah, Jean, Yuhuan, Ambika, Re- bal, Philippe, Christian, Patricia and many others. Thanks to my friend Dr. Evgenia Christoforou, you helped me greatly at all times when it was not easy. Also to my friends Enol, Sofia, Rober, Maria, MJ for showing me that real friendship endures through distance. Finally, I will always be indebted to my family: mom, dad, Enrique and Ghadi, you are the most important pillar in my life and nothing that I achieved I could have done without you. This milestone is as yours as mine. Also, I am v thankful for the rest of my wonderful family: grandpas, Manoli, Charo, Ernesto, Pedro, Gregorio Pablo, Carlos, Carlota, Pedro; thanks for always being there and for your love. I would like to dedicate this work to my uncle Gregorio, I hope this dissertation validate for that field report in Valle and that you are proud of all of us from up there. vi Design and Performance of Resource Allocation Mechanisms for Network Slicing Publication No. Pablo Caballero Garces, Ph.D. The University of Texas at Austin, 2018 Supervisors: Gustavo de Veciana Albert Banchs Roca Next generation wireless networks are expected to handle an exponential increase in demand for capacity generated by a collection of tenants and/or services with heterogeneous requirements. Multi-tenant network sharing, enabled through virtualization and network slicing, offers the opportunity to reduce operational and deployment costs, and the challenge of managing resource allocations among mul- tiple tenants serving possibly mobile diverse customers. When designing shared radio resource allocation mechanisms, it is as important to provide tenants with customization and isolation guarantees, as it is to achieve high resource utilization and to do so via low complexity and easy to implement algorithms. This thesis is devoted to the design and analysis of resource allocation mechanisms that meet these objectives. We propose a sharing model in which tenants are assigned a share/budget of a pool of network resources. This share is then redistributed in the form of weights vii amongst users, which in turn drive dynamic resource allocations which are par- tially able to adapt to the traffic demands on, and requirements of, different slices customer populations. We propose and analyze two approaches for redistributing slices’ share among customers which we classify into their associated (i) coopera- tive, and (ii) competitive resource allocations. In the cooperative resource allocation setting, a pre-established policy is proposed, in which resources are eventually assigned in proportion to the slice’s share and the relative number of active users in currently has at a resource. This is shown to be socially optimal in a particular setting and simple to implement, with statistical multiplexing gains that increase with the number of tenants and the size of the resource pool. These gains stem from the ability of the scheme to adapt to dynamic loads leading to an up to 50% network capacity savings with respect to static allocations. We further improve these gains by presenting a framework that combines resource allocation and wireless user association which uses limited computational, information, and handoff overheads. However, using our coopera- tive scheme over a large pool of resources restricts the degree to which a slice can differentiate its customers’ performance at a per resource level. Thus, we study how this trade-off affects the network utility and propose a mechanism to determine an optimal partition the resources into a collection of self-managed pools under coop- erative resource allocations. Our competitive resource allocation approach enables tenants to reap the performance benefits of sharing while retaining the ability to customize their own users’ allocations. This setting results in a network slicing game in which each ten- viii ant reacts to the user allocations of the others so as to maximize its own customers’ utility. We show that, under appropriate conditions, the game associated with such strategic behavior converges to a Nash equilibrium. At the Nash equilibrium, a ten- ant always achieves the same, or better, utility than it could achieve under a static partitioning of resources, hence providing the same level of inter-slice protection as static resource partitioning. The network utility of the equilibrium allocations is shown to be, under mild conditions, close to the socially optimal ones. The com- petitive resource allocation framework is complemented with a study on admis- sion control policies that enable tenants to ensure minimum rate guarantees to their users. Our analysis and extensive simulation results confirm that our framework provides a comprehensive practical solution towards multi-tenant network slicing. We also discuss how our theoretical results fill a gap in the general resource alloca- tion literature for strategic players. ix Table of Contents Acknowledgments v Abstract vii List of Tables xvi List of Figures xvii Chapter 1. Resource Allocation for Network Slicing 1 1.1 The origin of network sharing . .1 1.2 Who shares network resources? . .2 1.3 What resources can be shared? . .3 1.4 How should network resources be shared? . .4 1.4.1 Architectural enablers and network slicing . .5 1.4.2 Vision and objectives for RAN slicing . .6 1.4.3 Virtual pooling resource allocation mechanisms: cooperative vs competitive . .8 1.5 Outline . 10 1.6 Publications . 11 Part I Cooperative Resource Allocation 13 Chapter 2. Multi-Tenant Radio Access Network Slicing 14 2.1 Related work . 15 2.2 Chapter organization . 18 2.3 System model . 19 2.4 MORA criterion . 20 2.4.1 Properties of MORA resource allocation . 24 x 2.4.1.1 Per-base station resource allocation . 24 2.4.1.2 User association . 25 2.5 Gains and Savings of MORA . 26 2.5.1 Static Slicing (SS) baseline . 26 2.5.2 Operator utility gains and protection . 28 2.5.3 Capacity Savings . 28 2.6 Approximation algorithm for MORA . 31 2.6.1 Complexity and state-of-the-art algorithms . 32 2.6.2 Algorithm design . 33 2.6.2.1 Need for reassociations . 34 2.6.2.2 Criterion for (re)associations . 35 2.6.2.3 Order of reassociations . 37 2.6.2.4 Proposed algorithm . 38 2.6.2.5 Controlling the number of reassociations . 39 2.7 Performance evaluation . 41 2.7.1 Utility gains . 43 2.7.2 Capacity savings . 45 2.7.3 User performance . 47 2.7.4 Computational complexity . 49 2.7.5 Impact of non-uniform load distributions . 50 2.8 Conclusions . 51 2.9 Proofs of chapter results . 53 2.9.1 Proof of Theorem 1 . 53 2.9.2 Proof of Theorem 2 . 55 2.9.3 Proof of Theorem 3 . 57 2.9.4 Proof of Theorem 4 . 57 2.9.5 Proof of Theorem 5 . 60 Chapter 3. Optimizing Network Slicing via Virtual Resource Pool Parti- tioning 64 3.1 Related Work . 65 3.2 Chapter organization . 68 3.3 System model . 69 xi 3.3.1 Virtual Resource Pools and resource allocation . 70 3.3.2 Benchmark allocations . 72 3.3.3 Share, load and capacity distributions . 72 3.4 VRP partitioning . 74 3.4.1 Stochastic network utility . 74 3.4.2 Slices protection guarantees . 76 3.4.3 Design constraints . 79 3.4.3.1 Pooling management capacity constraints . 80 3.4.3.2 Connectivity and locality constraints . 80 3.4.4 Optimal VRP Partitioning . 81 3.5 Algorithm Design . 82 3.5.1 Greedy algorithm for OVP . 82 3.5.2 Greedy algorithm performance . 83 3.6 Utility approximation and analysis . 85 3.7 Performance evaluation . 93 3.7.1 Numerical evaluation of synthetic scenarios . 94 3.7.1.1 Optimal partitions for uniform shares . 96 3.7.1.2 Pooling capacity savings . 97 3.7.1.3 Optimal partitions for shares/loads proportional net- works . 98 3.7.2 Performance evaluation in realistic scenarios . 100 3.7.2.1 Capacity savings for uniform shares . 101 3.7.2.2 User utility in proportional shares/loads scenarios . 102 3.8 Conclusions . 104 3.9 Proofs of chapter results . 105 3.9.1 Proof of Lemma 1 . 105 3.9.2 Proof of Theorem 6 .