1. 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

2. 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

3. 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.

4. 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.

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.