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Performance Evaluation of an Agent-based Resource Management Infrastructure for Grid Computing

This paper evaluates an agent-based resource management infrastructure tailored for grid computing, addressing key challenges like resource heterogeneity, scalability, and adaptability across vast administrative domains. The proposed methodology involves hierarchical agent structures acting as resource providers and requesters, facilitating efficient resource discovery and advertisement. Performance metrics such as discovery speed, system efficiency, and load balancing are analyzed through simulations, demonstrating the trade-offs between resource advertisement strategies. Ongoing work includes the development of the Agent-based Resource Management System (ARMS) and the Performance Analysis and Characterization Environment (PACE).

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Performance Evaluation of an Agent-based Resource Management Infrastructure for Grid Computing

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  1. Performance Evaluation of anAgent-based Resource Management Infrastructure for Grid Computing Junwei Cao Darren J. Kerbyson Graham R. Nudd Department of Computer Science University of Warwick

  2. Grid Resource Management Globus Requirements • Heterogeneity • multiplicity of resources and numerous administrative domains • Scalability • millions of resources across wide geographical distances. • Adaptability • resource failure, performance change, etc Legion Condor Ninf NetSolve

  3. Agent-Based Methodology • Agent • A representation of computing resources in the metacomputing environment. • Both a resource provider and a resource requestor. • A router (or matchmaker) between an application and an available resource. • Organised into a hierarchy. • Resource • The detail information of a resource within the grid. • Request • The detail information of an application from the user.

  4. 1 Get 2 Get 1 AppInfo 3 AppInfo R/A R/A A A U/A U/A 3 ResInfo 2 ResInfo 4 Return 4 Return Resource Discovery • Resource Advertisement • The resource information can be advertised in the agent hierarchy (both up and down). • Resource Discovery • The application information from the user can be transferred in the agent hierarchy to discover an available resource. Data-pull • Strategies: • No resource advertisement, then complex resource discovery. • Full resource advertisement, then no resource discovery. Data-push

  5. Performance Metrics • Discovery Speed • System Efficiency • Load balancing • Success Rate

  6. Performance Optimisation Strategies Vary by • Dynamics • Agent structure • Resource distribution • Pre-knowledge Caching resource info Using local resource info Using global resource info Limit scope Limit resource validation

  7. A4 Simulator - Modelling • Input to model • Agent system structure • Request distribution • Resource distribution • Performance optimisation strategies • Modelling level • Agent-level (each individuals) • System-level (global)

  8. A4 Simulator - Simulation • Full support for all performance metrics • Multi-view simulation results • Each step view • Accumulative view • Agent View • Log view • Dynamic simulation result display • Comparing strategies

  9. A Case Study • Choice of strategies >> higher performance • No resource advertisement >> low discovery speed low efficiency • Too much resource advertisement >> extreme high discovery speed extreme low efficiency • Reasonable resource advertisement >> high discovery speed high efficiency

  10. Ongoing Work - ARMS • ARMS: an Agent-based Resource Management System for grid computing • A hierarchy of homogenous agents with resource discovery capabilities as meta-level resource management • PACE (a Performance Analysis and Characterisation Environment) as local resource scheduler.

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