In Pursuit of Desirable Equilibria in Large Scale Networked Systems

This thesis addresses an interdisciplinary problem in the context of engineering, computer science and economics: In a large scale networked system, how can we achieve a desirable equilibrium that benefits the system as a whole? We approach this question from two perspectives. On the one hand, given a system architecture that imposes certain constraints, a system designer must propose efficient algorithms to optimally allocate resources to the agents that desire them. On the other hand, given algorithms that are used in practice, a performance analyst must come up with tools that can characterize these algorithms and determine when they can be optimally applied. Ideally, the two viewpoints must be integrated to obtain a simple system design with efficient algorithms that apply to it. We study the design of incentives and algorithms in such large scale networked systems under three application settings, referred to herein via the subheadings: Incentivizing Sharing in Realtime D2D Networks: A Mean Field Games Perspective, Energy Coupon: A Mean Field Game Perspective on Demand Response in Smart Grids, Dynamic Adaptability Properties of Caching Algorithms, and Accuracy vs. Learning Rate of Multi-level Caching Algorithms. Our application scenarios all entail an asymptotic system scaling, and an equilibrium is defined in terms of a probability distribution over system states. The question in each case is to determine how to attain a probability distribution that possesses certain desirable properties. For the first two applications, we consider the design of specific mechanisms to steer the system toward a desirable equilibrium under self interested decision making. The environments in these problems are such that there is a set of shared resources, and a mechanism is used during each time step to allocate resources to agents that are selfish and interact via a repeated game. These models are motivated by resource sharing systems in the context of data communication, transportation, and power transmission networks. The objective is to ensure that the achieved equilibria are socially desirable. Formally, we show that a Mean Field Game can be used to accurately approximate these repeated game frameworks, and we describe mechanisms under which socially desirable Mean Field Equilibria exist. For the third application, we focus on performance analysis via new metrics to determine the value of the attained equilibrium distribution of cache contents when using different replacement algorithms in cache networks. The work is motivated by the fact that typical performance analysis of caching algorithms consists of determining hit probability under a fixed arrival process of requests, which does not account for dynamic variability of request arrivals. Our main contribution is to define a function which accounts for both the error due to time lag of learning the items' popularity, as well as error due to the inaccuracy of learning, and to characterize the tradeoff between the two that conventional algorithms achieve. We then use the insights gained in this exercise to design new algorithms that are demonstrably superior

Resource Allocation and Service Management in Next Generation 5G Wireless Networks

The accelerated evolution towards next generation networks is expected to dramatically increase mobile data traffic, posing challenging requirements for future radio cellular communications. User connections are multiplying, whilst data hungry content is dominating wireless services putting significant pressure on network's available spectrum. Ensuring energy-efficient and low latency transmissions, while maintaining advanced Quality of Service (QoS) and high standards of user experience are of profound importance in order to address diversifying user prerequisites and ensure superior and sustainable network performance. At the same time, the rise of 5G networks and the Internet of Things (IoT) evolution is transforming wireless infrastructure towards enhanced heterogeneity, multi-tier architectures and standards, as well as new disruptive telecommunication technologies. The above developments require a rethinking of how wireless networks are designed and operate, in conjunction with the need to understand more holistically how users interact with the network and with each other. In this dissertation, we tackle the problem of efficient resource allocation and service management in various network topologies under a user-centric approach. In the direction of ad-hoc and self-organizing networks where the decision making process lies at the user level, we develop a novel and generic enough framework capable of solving a wide array of problems with regards to resource distribution in an adaptable and multi-disciplinary manner. Aiming at maximizing user satisfaction and also achieve high performance - low power resource utilization, the theory of network utility maximization is adopted, with the examined problems being formulated as non-cooperative games. The considered games are solved via the principles of Game Theory and Optimization, while iterative and low complexity algorithms establish their convergence to steady operational outcomes, i.e., Nash Equilibrium points. This thesis consists a meaningful contribution to the current state of the art research in the field of wireless network optimization, by allowing users to control multiple degrees of freedom with regards to their transmission, considering mobile customers and their strategies as the key elements for the amelioration of network's performance, while also adopting novel technologies in the resource management problems. First, multi-variable resource allocation problems are studied for multi-tier architectures with the use of femtocells, addressing the topic of efficient power and/or rate control, while also the topic is examined in Visible Light Communication (VLC) networks under various access technologies. Next, the problem of customized resource pricing is considered as a separate and bounded resource to be optimized under distinct scenarios, which expresses users' willingness to pay instead of being commonly implemented by a central administrator in the form of penalties. The investigation is further expanded by examining the case of service provider selection in competitive telecommunication markets which aim to increase their market share by applying different pricing policies, while the users model the selection process by behaving as learning automata under a Machine Learning framework. Additionally, the problem of resource allocation is examined for heterogeneous services where users are enabled to dynamically pick the modules needed for their transmission based on their preferences, via the concept of Service Bundling. Moreover, in this thesis we examine the correlation of users' energy requirements with their transmission needs, by allowing the adaptive energy harvesting to reflect the consumed power in the subsequent information transmission in Wireless Powered Communication Networks (WPCNs). Furthermore, in this thesis a fresh perspective with respect to resource allocation is provided assuming real life conditions, by modeling user behavior under Prospect Theory. Subjectivity in decisions of users is introduced in situations of high uncertainty in a more pragmatic manner compared to the literature, where they behave as blind utility maximizers. In addition, network spectrum is considered as a fragile resource which might collapse if over-exploited under the principles of the Tragedy of the Commons, allowing hence users to sense risk and redefine their strategies accordingly. The above framework is applied in different cases where users have to select between a safe and a common pool of resources (CPR) i.e., licensed and unlicensed bands, different access technologies, etc., while also the impact of pricing in protecting resource fragility is studied. Additionally, the above resource allocation problems are expanded in Public Safety Networks (PSNs) assisted by Unmanned Aerial Vehicles (UAVs), while also aspects related to network security against malign user behaviors are examined. Finally, all the above problems are thoroughly evaluated and tested via a series of arithmetic simulations with regards to the main characteristics of their operation, as well as against other approaches from the literature. In each case, important performance gains are identified with respect to the overall energy savings and increased spectrum utilization, while also the advantages of the proposed framework are mirrored in the improvement of the satisfaction and the superior Quality of Service of each user within the network. Lastly, the flexibility and scalability of this work allow for interesting applications in other domains related to resource allocation in wireless networks and beyond

Market Methods for Supply and Demand Management in the Smart Grid

This study addresses the resource management problem in a large scale networked system with high flexibility. We consider the supply and demand management problem specifically in the context of the future Smart Grid. On the supply side, we design a secondary market to provide stochastic energy service via distributed renewable energy resources. The performance of the proposed market is evaluated in two circumstances, i.e. whether or not the extra energy penetration caused by the market changes the operation point of the power grid. On the demand side, we would like to take the advantages of the residential demand flexibility to relieve consumption peaks and stabilize the system. We conduct certain demand response in a market approach and further build a real experiment system to analyze the performance of such regime. The study of supply side market is referred to the subheading: Small-Scale Markets for a Bilateral Energy Sharing Economy followed by an extension of the corresponding market which brings in the concern that the increased energy penetration may change the operation point of the grid. As for the demand side study, design and analysis of such demand response market is under the subheading: Mean Field Games in Nudge Systems for Societal Networks and the real experiment built-up is presented in Incentive-Based Demand Response: Empirical Assessment and Critical Appraisal. We model the agent behaviour in both markets via game theoretic approach and analyze the equilibrium performance. We show that a Mean Field Game regime can be applied to accurately approximate these repeated game frameworks and socially desirable equilibria that benefit both system operator and agents exist

In Pursuit of Desirable Equilibria in Large Scale Networked Systems

This thesis addresses an interdisciplinary problem in the context of engineering, computer science and economics: In a large scale networked system, how can we achieve a desirable equilibrium that benefits the system as a whole? We approach this question from two perspectives. On the one hand, given a system architecture that imposes certain constraints, a system designer must propose efficient algorithms to optimally allocate resources to the agents that desire them. On the other hand, given algorithms that are used in practice, a performance analyst must come up with tools that can characterize these algorithms and determine when they can be optimally applied. Ideally, the two viewpoints must be integrated to obtain a simple system design with efficient algorithms that apply to it. We study the design of incentives and algorithms in such large scale networked systems under three application settings, referred to herein via the subheadings: Incentivizing Sharing in Realtime D2D Networks: A Mean Field Games Perspective, Energy Coupon: A Mean Field Game Perspective on Demand Response in Smart Grids, Dynamic Adaptability Properties of Caching Algorithms, and Accuracy vs. Learning Rate of Multi-level Caching Algorithms. Our application scenarios all entail an asymptotic system scaling, and an equilibrium is defined in terms of a probability distribution over system states. The question in each case is to determine how to attain a probability distribution that possesses certain desirable properties. For the first two applications, we consider the design of specific mechanisms to steer the system toward a desirable equilibrium under self interested decision making. The environments in these problems are such that there is a set of shared resources, and a mechanism is used during each time step to allocate resources to agents that are selfish and interact via a repeated game. These models are motivated by resource sharing systems in the context of data communication, transportation, and power transmission networks. The objective is to ensure that the achieved equilibria are socially desirable. Formally, we show that a Mean Field Game can be used to accurately approximate these repeated game frameworks, and we describe mechanisms under which socially desirable Mean Field Equilibria exist. For the third application, we focus on performance analysis via new metrics to determine the value of the attained equilibrium distribution of cache contents when using different replacement algorithms in cache networks. The work is motivated by the fact that typical performance analysis of caching algorithms consists of determining hit probability under a fixed arrival process of requests, which does not account for dynamic variability of request arrivals. Our main contribution is to define a function which accounts for both the error due to time lag of learning the items' popularity, as well as error due to the inaccuracy of learning, and to characterize the tradeoff between the two that conventional algorithms achieve. We then use the insights gained in this exercise to design new algorithms that are demonstrably superior