Local Utility Aware Content Replication

Abstract

Abstract.A commonly employed abstraction for studying the object placement problem for the purpose of Internet content distribution is that of a distributed replication group. In this work the initial model of distributed replication group of Leff, Wolf, and Yu (IEEE TPDS '93) is extended to the case that individual nodes act selfishly, i.e., cater to the optimization of their individual local utilities. Our main contribution is the derivation of equilibrium object placement strategies that: (a) can guarantee improved local utilities for all nodes concurrently as compared to the corresponding local utilities under greedy local object placement; (b) do not suffer from potential mistreatment problems, inherent to centralized strategies that aim at optimizing the social utility; (c) do not require the existence of complete information at all nodes. We develop a baseline computationally efficient algorithm for obtaining the aforementioned equilibrium strategies and then extend it to improve its performance with respect to fairness. Both algorithms are realizable in practice through a distributed protocol that requires only limited exchange of information.

A Pure Nash Equilibrium-Based Game Theoretical Method for Data Replication across Multiple Servers

“…In game theory, such situations are studied via Algorithmic Mechanism Design (AMD) [28] techniques, by designing a payment scheme that forces the agents to always tell the truth and follow the rules. The selfish model is not considered in this paper, but curious readers are encouraged to review the work reported in [4], [18], and [22], which directly address the selfish model in the context of the RPP.…”

“…Moreover, because each server is only interested in those objects to which access have been generated, it only has to record a partial network or a snapshot and not the entire network. Furthermore, this assumption (about information sharing) has been undertaken in almost every RPP technique, e.g., [2], [4], [6], [17], [18], [22], [23], and [31].…”

“…The second work [18] focuses on mechanism design issues and derives an incentive-compatible auction for replicating data on the Web. The third work [22] deals with identifying Nash strategies derived from synthetic utility functions. Our work differs from all the game theoretical techniques in 1) identifying a noncooperative non-price-based replica allocation method to tackle the data replication problem, 2) using game theoretical techniques to study an environment where the agents behave in a selfinterested manner, and 3) deriving a pure Nash equilibrium and pure strategies for the agents.…”

A cooperative game theoretical replica placement technique

“…The second work [15] focuses on mechanism design issues and derives an incentive compatible auction for replicating data on the Web. The third work [16] deals with identifying Nash strategies derived from synthetic utility functions. Our work differs from all the game theoretical techniques in: 1) identifying a cooperative nonpriced based replica allocation method to tackle the data replication problem, 2) using game theoretical technique to study an environment that closely mimics the Internet, and 3) thoroughly comparing the proposed technique against several conventional replica placement techniques.…”

A pure Nash equilibrium guaranteeing game theoretical replica allocation method for reducing Web access time

“…The second set of works [9], [10], and [11] focuses on mechanism design issues and derives various incentive compatible auctions for replicating data on the Web. The last work [12] deals with identifying Nash strategies derived from synthetic utility functions. Our work differs from all the game theoretical techniques in: 1) identifying a noncooperative non-priced based replica allocation method to tackle the data replication problem, 2) using game theoretical techniques to study an environment where the agents behave in a self-interested manner, and 3) deriving pure Nash equilibrium and pure strategies for the agents.…”