Using distributed local information to improve global performance in Grids

Abstract

Grid computing refers to the federation of geographically distributed and heterogeneous computer resources. These resources may belong to different administrative domains, but are shared among users. Every grid presents a key component responsible for obtaining, distributing, indexing and archiving information about the configuration and state of services and resources. Optimizing tasks assignations and user requests to resources require the maintenance of up-to-date information about the grid.

In large scale Grids, the dynamics of the resource information cannot be captured using a static hierarchy and relying in manual configuration and administration. It is necessary to design new policies for discovery and propagation of resource information. There is a growing interest in the interaction of Grid Computing and the Peer to Peer (P2P) paradigm, pushing towards scalable solutions.

In this work, starting from the Best-Neighbor policy based on previously published ideas, the reasons behind its lack of performance are explored. A new improved Best-Neighbor policy are proposed and analyzed, comparing it with Random, Hierarchical and Super- Peer policies.