Decentralized Adaptive Helper Selection in Multi-channel P2P Streaming Systems

In Peer-to-Peer (P2P) multichannel live streaming, helper peers with surplus bandwidth resources act as micro-servers to compensate the server deficiencies in balancing the resources between different channel overlays. With deployment of helper level between server and peers, optimizing the user/helper topology becomes a challenging task since applying well-known reciprocity-based choking algorithms is impossible due to the one-directional nature of video streaming from helpers to users. Because of selfish behavior of peers and lack of central authority among them, selection of helpers requires coordination. In this paper, we design a distributed online helper selection mechanism which is adaptable to supply and demand pattern of various video channels. Our solution for strategic peers' exploitation from the shared resources of helpers is to guarantee the convergence to correlated equilibria (CE) among the helper selection strategies. Online convergence to the set of CE is achieved through the regret-tracking algorithm which tracks the equilibrium in the presence of stochastic dynamics of helpers' bandwidth. The resulting CE can help us select proper cooperation policies. Simulation results demonstrate that our algorithm achieves good convergence, load distribution on helpers and sustainable streaming rates for peers

An Optimized AMS Based Cloud Downloading Service with Advanced Caching and Intelligent Data Distribution Mechanism

The popularity of peer-to-peer video content downloading has surged due to diverse content availability and convenient sharing among users. However, scaling systems to accommodate the growing number of users and content items poses a challenge. This research aims to optimize video content downloading in peer-to-peer systems. The objective is to improve performance by developing advanced caching mechanisms, an intelligent data distribution algorithm, and efficient bandwidth resource management. The proposed approach involves implementing innovative caching mechanisms that store frequently accessed content closer to users, reducing download time. An intelligent data distribution algorithm minimizes bottlenecks and maximizes download speeds. Efficient bandwidth resource management ensures fair allocation. Results demonstrate significant enhancements in download time and overall system performance, leading to improved user experience. This research addresses the need for an optimized video content downloading system to handle increasing user and content volumes. The findings hold the potential to enhance user experiences, facilitate seamless video sharing, and advance peer-to-peer video content downloading

Collusion in Peer-to-Peer Systems

Peer-to-peer systems have reached a widespread use, ranging from academic and industrial applications to home entertainment. The key advantage of this paradigm lies in its scalability and flexibility, consequences of the participants sharing their resources for the common welfare. Security in such systems is a desirable goal. For example, when mission-critical operations or bank transactions are involved, their effectiveness strongly depends on the perception that users have about the system dependability and trustworthiness. A major threat to the security of these systems is the phenomenon of collusion. Peers can be selfish colluders, when they try to fool the system to gain unfair advantages over other peers, or malicious, when their purpose is to subvert the system or disturb other users. The problem, however, has received so far only a marginal attention by the research community. While several solutions exist to counter attacks in peer-to-peer systems, very few of them are meant to directly counter colluders and their attacks. Reputation, micro-payments, and concepts of game theory are currently used as the main means to obtain fairness in the usage of the resources. Our goal is to provide an overview of the topic by examining the key issues involved. We measure the relevance of the problem in the current literature and the effectiveness of existing philosophies against it, to suggest fruitful directions in the further development of the field

Applications of Repeated Games in Wireless Networks: A Survey

A repeated game is an effective tool to model interactions and conflicts for players aiming to achieve their objectives in a long-term basis. Contrary to static noncooperative games that model an interaction among players in only one period, in repeated games, interactions of players repeat for multiple periods; and thus the players become aware of other players' past behaviors and their future benefits, and will adapt their behavior accordingly. In wireless networks, conflicts among wireless nodes can lead to selfish behaviors, resulting in poor network performances and detrimental individual payoffs. In this paper, we survey the applications of repeated games in different wireless networks. The main goal is to demonstrate the use of repeated games to encourage wireless nodes to cooperate, thereby improving network performances and avoiding network disruption due to selfish behaviors. Furthermore, various problems in wireless networks and variations of repeated game models together with the corresponding solutions are discussed in this survey. Finally, we outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference

A Game Theoretic Analysis of Incentives in Content Production and Sharing over Peer-to-Peer Networks

User-generated content can be distributed at a low cost using peer-to-peer (P2P) networks, but the free-rider problem hinders the utilization of P2P networks. In order to achieve an efficient use of P2P networks, we investigate fundamental issues on incentives in content production and sharing using game theory. We build a basic model to analyze non-cooperative outcomes without an incentive scheme and then use different game formulations derived from the basic model to examine five incentive schemes: cooperative, payment, repeated interaction, intervention, and enforced full sharing. The results of this paper show that 1) cooperative peers share all produced content while non-cooperative peers do not share at all without an incentive scheme; 2) a cooperative scheme allows peers to consume more content than non-cooperative outcomes do; 3) a cooperative outcome can be achieved among non-cooperative peers by introducing an incentive scheme based on payment, repeated interaction, or intervention; and 4) enforced full sharing has ambiguous welfare effects on peers. In addition to describing the solutions of different formulations, we discuss enforcement and informational requirements to implement each solution, aiming to offer a guideline for protocol designers when designing incentive schemes for P2P networks.Comment: 31 pages, 3 figures, 1 tabl