Use of Agent-Based Service Discovery for Resource Management in Metacomputing Environment

1. Use of Agent-Based Service Discovery for Resource Management in Metacomputing Environment Junwei Cao Darren J. Kerbyson Graham R. Nudd Department of Computer Science University of Warwick

2. PACE Toolkit User Interface User Interface Application Tools Resource Tools Cache Object Library Network CPU Object Editor Source Code Analysis HMCL Scripts Resource Model Resource Model PSL Scripts Evaluation Engine Compiler Application Model Application Model Evaluation Engine Performance Analysis Performance Analysis On-the-fly Analysis

3. The Question Is … ?

4. A4 Methodology An agent is … • A local manager • An user middleman • A broker • A coordinator • A service provider • A service requestor • A matchmaker • A router

5. Local Management Layer Local Management Layer Coordination Layer Coordination Layer Communication Layer Communication Layer Service Discovery Service Advertisement NEXT!

6. Service Advertisement Hi, please find attached my service information. Hi, could you please give me some service information that you have? • Full service advertisement – requires no service discovery. • No service advertisement – results in complex service discovery. Make Balance!

7. Agent Capability Tables The process of the service advertisement and discovery corresponds to the maintenance and lookup of the ACTs. Vary by source: • T_ACT: contains service info of local resources • L_ACT: contains service info coming from lower agents • G_ACT: contains service info coming from upper agent • C_ACT: contains cached service info during discovery Strategies: • Data-push: submit service info to other agents • Data-pull: ask for service info from other agents • Periodical: Periodical ACT maintenance • Event-driven: ACT maintenance driven by system events

8. The Answer Is … At meta level, agents cooperate with each other for service discovery. At local level, PACE functions can supply accurate performance info.

9. ARMS in Context A4 A4 Simulator Grid Resources Grid Users ARMS PMA PACE Application Tools (AT) Evaluation Engine (EE) Resource Tools (RT)

10. EE ACT EE ACT Application Models Cost Models Agents ACT EE EE ACT PMA ACT EE Users EE ACT ACT EE Resource Models RT RT RT RT Processors ARMS Architecture Bottleneck? ? AT

11. Resource Monitoring Resource Allocation Application Management Application Execution Sched. Cost App. Info Service Info Cost Model Eval Results Res. Info Agent ID Application Model Discovery Advertisement To Another Agent ARMS Agent Structure Local Coordination ACTs Scheduler Match Maker ACT Manager PACE Evaluation Engine Comm. Communication Module

12. Model Composer Monitoring Simulation Engine Reconfiguration PMA Structure PMA ARMS Agent Statistical data Policies Performance Model Strategies

13. Conclusions • Performance prediction driven for QoS support of grid resource management • Agent based hierarchical model for grid resource advertisement and discovery • Simulation based performance optimisation and steering of service discovery in large scale multi-agent systems In summary, all of above go together to provides an available methodology and prototype implementation of agent-based resource management for grid computing, which can be used as a fundamental framework for further improvement and refinement.