Department of Computer Science University of Warwick

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Presentation transcript:

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

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

Agent-Based Methodology 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.

Resource Discovery Resource Advertisement Data-pull Resource Discovery 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 R/A A U/A 1 AppInfo 2 Get 3 ResInfo 4 Return Data-push R/A A U/A 3 AppInfo 1 Get 2 ResInfo 4 Return Strategies: No resource advertisement, then complex resource discovery. Full resource advertisement, then no resource discovery.

Performance Metrics Discovery Speed System Efficiency Load balancing Success Rate

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

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)

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

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

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.