GAME THEORETIC FLOW AND ROUTING CONTROL FOR COMMUNICATION NETWORKS

As the need to support high speed data exchange in modern communication networks grows rapidly, effective and fair sharing of the network resources becomes very important. Today's communication networks typically involve a large number of users that share the same network resources but may have different, and often competing, objectives. Advanced network protocols that are implemented to optimize the performance of such networks typically assume that the users are passive and are willing to accept compromising their own performance for the sake of optimizing the performance of the overall network. However, considering the trend towards more decentralization in the future, it is natural to assume that the users in a large network may take a more active approach and become more interested in optimizing their own individual performances without giving much consideration to the overall performance of the network. A similar situation occurs when the users are members of teams that are sharing the network resources. A user may find itself cooperating with other members of its team which itself is competing with the other teams in the network. Game theory appears to provide the necessary framework and mathematical tools for formulating and analyzing the strategic interactions among users, or teams of users, of such networks. In this thesis, we investigate networks in which users, or teams of users, either compete or cooperate for the same network resources. We considered two important network topologies and used many examples to illustrate the various solution concepts that we have investigated.. First we consider two-nodeiiiparallel link networks with non-cooperative users trying to optimally distribute their flows among the links. For these networks, we established a condition which guarantees the existence and uniqueness of a Nash equilibrium for the link flows. We derived an analytical expression for the Nash equilibrium and investigated its properties in terms of the network parameters and the users preferences. We showed that in a competitive environment users can achieve larger flow rates by properly emphasizing the corresponding term in their utility functions, but that this can only be done at the expense of an increase in the expected delay. Next, we considered a general network structure with multiple links, multiple nodes, and multiple competing users. We proved the existence of a unique Nash equilibrium. We also investigated many of its intuitive properties. We also extended the model to a network where multiple teams of users compete with each other while cooperating within the teams to optimize a team level performance. For this model, we studied the Noninferior Nash solution and compared its results with the standard Nash equilibrium solution

Incentivizing Stable Path Selection in Future Internet Architectures

By delegating path control to end-hosts, future Internet architectures offer flexibility for path selection. However, there is a concern that the distributed routing decisions by end-hosts, in particular load-adaptive routing, can lead to oscillations if path selection is performed without coordination or accurate load information. Prior research has addressed this problem by devising path-selection policies that lead to stability. However, little is known about the viability of these policies in the Internet context, where selfish end-hosts can deviate from a prescribed policy if such a deviation is beneficial fromtheir individual perspective. In order to achieve network stability in future Internet architectures, it is essential that end-hosts have an incentive to adopt a stability-oriented path-selection policy. In this work, we perform the first incentive analysis of the stability-inducing path-selection policies proposed in the literature. Building on a game-theoretic model of end-host path selection, we show that these policies are in fact incompatible with the self-interest of end-hosts, as these strategies make it worthwhile to pursue an oscillatory path-selection strategy. Therefore, stability in networks with selfish end-hosts must be enforced by incentive-compatible mechanisms. We present two such mechanisms and formally prove their incentive compatibility.Comment: 38th International Symposium on Computer Performance, Modeling, Measurements and Evaluation (PERFORMANCE 2020

Information Exchange rather than Topology Awareness: Cooperation between P2P Overlay and Traffic Engineering

Solutions to the routing strategic conflict between noncooperative P2P overlay and ISP underlay go separate ways: hyperselfishness and cooperation. Unpredictable (possibly adverse) impact of the hyperselfish topology awareness, which is adopted in both overlay routing and traffic engineering, has not been sufficiently studied in the literature. Topology-related information exchange in a cooperatively efficient way should be highlighted to alleviate the cross-layer conflict. In this paper, we first illustrate the hyperselfish weakness with two dynamic noncooperative game models in which hyperselfish overlay or underlay has to accept a suboptimal profit. Then we build a synergistic cost-saving (SC) game model to reduce the negative effects of noncooperation. In the SC model, through information exchange, that is, the classified path-delay metrics for P2P overlay and peer locations for underlay, P2P overlay selects proximity as well as saving traffic transit cost for underlay, and ISP underlay adjusts routing to optimize network cost as well as indicating short delay paths for P2P. Simulations based on the real and generated topologies validate cost improvement by SC model and find a proper remote threshold value to limit P2P traffic from remote area, cross-AS, or cross-ISP

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

When a few undermine the whole: a class of social dilemmas in ridesharing

We investigate a class of social dilemmas that arise when a heterogeneous group of agents potentially benefit from a joint enterprise such as ridesharing. Participation in the enterprise incurs positive externalities to other participants; social welfare is maximized with full participation. However, if some agents find it a dominant strategy to opt out, then the potential benefit from the enterprise will decrease, leading to more members opting out. This iterated disincentivizing effect could result in massive welfare losses. We construct a game-theoretical model to implement these social dilemmas and report experimental evidence for their existence and welfare impact.NSF Grant SES-141892

Advances in Reinforcement Learning

Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic

Game Theory Relaunched

The game is on. Do you know how to play? Game theory sets out to explore what can be said about making decisions which go beyond accepting the rules of a game. Since 1942, a well elaborated mathematical apparatus has been developed to do so; but there is more. During the last three decades game theoretic reasoning has popped up in many other fields as well - from engineering to biology and psychology. New simulation tools and network analysis have made game theory omnipresent these days. This book collects recent research papers in game theory, which come from diverse scientific communities all across the world; they combine many different fields like economics, politics, history, engineering, mathematics, physics, and psychology. All of them have as a common denominator some method of game theory. Enjoy