Mathematical modeling of incentive policies in P2P systems.

Zhao, Qiao.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (leaves 35-36).Abstracts also in Chinese.Abstract --- p.iAcknowledgement --- p.vChapter 1 --- Introduction --- p.1Chapter 2 --- Model Description --- p.3Chapter 2.1 --- An Incentive Model for P2P Networks --- p.3Chapter 2.2 --- Learning Models for P2P Networks --- p.5Chapter 2.2.1 --- Current-best Learning Model (CBLM) --- p.5Chapter 2.2.2 --- Opportunistic Learning Model (OLM) --- p.6Chapter 2.3 --- Incentive Policies for P2P Networks --- p.7Chapter 2.3.1 --- Mirror Incentive Policy Vmirror --- p.8Chapter 2.3.2 --- Proportional Incentive Policy Vprop --- p.9Chapter 2.3.3 --- Linear Incentive Policy Class CLIP --- p.9Chapter 2.4 --- Performance and Robustness of Incentive Policies --- p.10Chapter 2.4.1 --- Robustness Analysis of Mirror Incentive Policy using the current-best learning method --- p.10Chapter 2.4.2 --- Robustness Analysis of Mirror Incentive Policy using the opportunistic learning method --- p.12Chapter 2.4.3 --- Robustness Analysis of Proportional Incentive Policy Using the current-best learning method --- p.12Chapter 2.4.4 --- Robustness Analysis of Proportional Incentive Policy Using the opportunistic learning method --- p.13Chapter 2.4.5 --- Robustness Analysis for Incentive Protocol in the Linear Incentive Class --- p.14Chapter 2.5 --- Connection with Evolutionary Game Theory --- p.17Chapter 3 --- Performance Evaluation --- p.21Chapter 3.1 --- Performance and Robustness of the Mirror Incentive Policy (Pmirror): --- p.21Chapter 3.2 --- Performance and Robustness of the Proportional Incentive Policy {Pprop): --- p.23Chapter 3.3 --- Performance and Robustness of incentive policy in the Linear Incentive Class (CLIP)： --- p.24Chapter 3.4 --- The Effect of Non-adaptive Peers: --- p.25Chapter 4 --- Adversary Effect of Altruism --- p.29Chapter 4.1 --- The Effect of Protocol Cost --- p.29Chapter 4.2 --- The Tradeoff between Altruism and System Robustness --- p.30Chapter 5 --- Related Work --- p.33Chapter 6 --- Conclusion --- p.34Bibliography --- p.3

A mathematical framework for analyzing incentives in peer-to-peer networks

The existence and performance of peer-to-peer systems depend on thecontribution of resources from interacting peers. One of the challenges ofresource sharing in peer-to-peer systems is free riding. A situation usersattempt to exploit the system by utilizing the resources of others withoutcontributing. We view this from rationality perspective that every peer inthe network will attempt to maximize their utility of the system. In thispaper, we approach the problem of free riders mitigation from utilityoptimization point of view, by modeling each peer's interest as UtilityMaximization Problem (UTP). We propose analytical model for the wholenetwork as a mixed integer linear programming model. The super peers inthe network are given the responsibility of maximizing the utility of all peers connected to them. This is to ensure fairness among the interacting peers and the stability of the entire system. This technique allows peers to either upload or download resources based on their best strategy and interest.Keywords: Free rider, Utility, Peer-to-Peer, Incentives, Maximization,Resource

Computational Mechanism Design: A Call to Arms

Game theory has developed powerful tools for analyzing decision making in systems with multiple autonomous actors. These tools, when tailored to computational settings, provide a foundation for building multiagent software systems. This tailoring gives rise to the field of computational mechanism design, which applies economic principles to computer systems design

06431 Abstracts Collection -- Scalable Data Management in Evolving Networks

From 22.10.06 to 27.10.06, the Dagstuhl Seminar 06431 ``Scalable Data Management in Evolving Networks\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

On the Applicability of Resources Optimization Model for Mitigating Free Riding in P2P System

The survival of peer-to-peer systems depends on the contribution of resources by all the participating peers. Selfish behavior of some peers that do not contribute resources inhibits the expected level of service delivery. Free riding has been found to seriously affect the performance and negates the sharing principle of peer-to-peer networks. In this paper, first, we investigate through simulations the effectiveness of a proposed linear model for mitigating free riding in a P2P system. Second, we extended the initial linear model by incorporating additional constraints on download and upload of each peer. This helps in reducing the effects of free riding behavior on the system. Lastly, we evaluate the impacts of some parameters on the models.Keywords: Peer-to-Peer, Resources, Free rider, Optimization, Constraints, Algorith

Mitigating Free Riding in Peer-To-Peer Networks: Game Theory Approach

The performance of peer-to-peer systems is based on the quality and quantity of resource contributions from participating peers. In most systems, users are assumed to be cooperative, but in reality, sharing in peer-to-peer systems is faced with the problem of free riding. In this paper, we model the interactions between peers as a modified gift giving game and proposed an utility exchange incentive mechanism to inhibit free riding. This technique allows peers to either upload or download resources based on their best strategy and interest. Through extensive simulations, we show that this mechanism can increase fairness and encourage resource contribution by peers to the network. This will ensure a resourceful and stable peer- to-peer systems.http://dx.doi.org/10.4314/njt.v34i2.2

A Comprehensive Analysis of Swarming-based Live Streaming to Leverage Client Heterogeneity

Due to missing IP multicast support on an Internet scale, over-the-top media streams are delivered with the help of overlays as used by content delivery networks and their peer-to-peer (P2P) extensions. In this context, mesh/pull-based swarming plays an important role either as pure streaming approach or in combination with tree/push mechanisms. However, the impact of realistic client populations with heterogeneous resources is not yet fully understood. In this technical report, we contribute to closing this gap by mathematically analysing the most basic scheduling mechanisms latest deadline first (LDF) and earliest deadline first (EDF) in a continuous time Markov chain framework and combining them into a simple, yet powerful, mixed strategy to leverage inherent differences in client resources. The main contributions are twofold: (1) a mathematical framework for swarming on random graphs is proposed with a focus on LDF and EDF strategies in heterogeneous scenarios; (2) a mixed strategy, named SchedMix, is proposed that leverages peer heterogeneity. The proposed strategy, SchedMix is shown to outperform the other two strategies using different abstractions: a mean-field theoretic analysis of buffer probabilities, simulations of a stochastic model on random graphs, and a full-stack implementation of a P2P streaming system.Comment: Technical report and supplementary material to http://ieeexplore.ieee.org/document/7497234

Computational-Mechanism Design: A Call to Arms

Game theory has developed several powerful tools for analyzing decision making in systems composed of multiple autonomous actors. Given this fact, AI practitioners would like to exploit these tools when building software systems containing multiple agents. However, to do this, the tools must be tailored to computational settings. To this end, the authors provide an overview of computational-mechanism design, which deals with the application of economic principles in computer systems design. Moreover, because many complex systems are inherently distributed, they also present initial results from the relatively new field of distributed-computational-mechanism design and outline the key challenges involved in making the ideas practicable.Engineering and Applied Science