Policy driven heterogeneous resource co-allocation with Gangmatching

Abstract

Dynamic, heterogeneous and distributively owned resource environments present unique challenges to the problems of resource representation, allocation and management. Conventional resource management methods that rely on static models of resource allocation policy and behavior fail to address these challenges. We previously argued that Matchmaking provides an elegant and robust solution to resource management in such dynamic and federated environments. However, Matchmaking is limited by its purely bilateral formalism of matching a single customer with a single resource, precluding more advanced resource management services such as co-allocation. In this paper, we present Gangmatching, a multilateral extension to the Matchmaking model, and discuss the Gangmatching model and its associated implementation and performance issues in context of a real-world license management co-allocation problem.