Data Replication in Large Distributed Computing Systems using Discriminatory Game Theoretic Mechanism Design

Replicating data over geographically dispersed web servers reduces network traffic, server load, and more importantly the user-perceived access delays. This paper proposes a unique replica placement technique using the concepts of a “supergame”. The supergame allows the agents who represent the data objects to continuously compete for the limited available server memory space, so as to acquire the rights to place data objects at the servers. At any given instance in time, the supergame is represented by a game which is a collection of subgames, played concurrently at each server in the system. We derive a resource allocation mechanism which acts as a platform at the subgame level for the agents to compete. This approach allows us to transparently monitor the actions of the agents, who in a non-cooperative environment strategically place the data objects to reduce user access time and latency which in turn, adds reliability and fault-tolerance to the system. We show that this mechanism exhibits Nash equilibrium at the subgame level which in turn conforms to games and supergame Nash equilibrium, respectively. The mechanism is extensively evaluated against some well-known algorithms, such as: greedy, branch and bound, game theoretical auctions and genetic algorithms. The experimental results reveal that the mechanism provides excellent solution quality, while maintaining fast execution time.