Coordinated learning to support resource management in computational grids

Managing resources in large scale distributed systems is an important concern for both peer-2-peer and computational grid systems, and is a complex and time sensitive process. Although existing peer-2-peer systems are divided into those that support computation (CPU) sharing or data sharing, users in a computational grid generally need to share both. Identifying which resources to select is important to guarantee reasonable execution time and cost to a given user or group of users. We provide first a description of a framework to support learning in the context a community of interacting peers, and show how this can be used to support resource sharing in computational grids.