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.

Federated distributed systems present new challenges to resource management, which cannot be met by conventional systems that employ relatively static resource models and centralized allocators. We previously argued that matchmaking provides an elegant and robust resource management solution for these highly dynamic environments (R. Raman et al., 1998). Although powerful and flexible, multiparty policies (e.g., co-allocation) cannot be accommodated by matchmaking. The authors present Gang-Matching, a multilateral matchmaking formalism to address this deficiency.

Conventional resource management systems use a system model to describe resources and a centralized scheduler to control their allocation. We argue that this paradigm does not adapt well to distributed systems, particularly those built to support high throughput computing. Obstacles include heterogeneity of resources, which make uniform allocation algorithms difficult to formulate, and distributed ownership, leading to widely varying allocation policies. Faced with these problems, we developed and implemented the classified advertisement (classad) matchmaking framework, a flexible and general approach to resource management in distributed environment with decentralized ownership of resources. Novel aspects of the framework include a semi structured data model that combines schema, data, and query in a simple but powerful specification language, and a clean separation of the matching and claiming phases of resource allocation. The representation and protocols result in a robust, scalable and flexible framework that can evolve with changing resources. The framework was designed to solve real problems encountered in the deployment of Condor, a high throughput computing system developed at the University of Wisconsin-Madison. Condor is heavily used by scientists at numerous sites around the world. It derives much of its robustness and efficiency from the matchmaking architecture.