A Group Selection Pattern Applied to Grid Resource Management

A key challenge in grid computing is the achievement of efficient and self-organized resource management. Grids are often large scale, heterogeneous, and unpredictable systems. Introducing group structures can help to distribute coordination efforts, but higher levels of adaptation and learning in the coordination protocols are still required in order to cope with system complexity. We provide a solution based on a self-organized and emergent mechanism evolving congregations of resource management agents through a group selection process which maximizes utility outcomes for system-wide performance. We provide a formalization of this process into a group selection pattern, and we propose several instantiations optimizing grid resource management scenarios such as adaptive job scheduling, market-based resource management, and policy coordination in virtual organizations (VOs). We further evaluate by simulation the performance of the mechanism in those scenarios. The results support the conclusion that group selection optimizes coordination by evolving small and dynamic groups.