1. Agent-based Resource Management for Grid Computing
Junwei Cao
University of Warwick
May 2002

2. Introduction
Ten years research on high performance computing at Warwick:
Performance evaluation
Local grid scheduling
Global grid management
Collaborations with:
NASA Ames Research Centre
Los Alamos National Laboratory
IBM T J Watson Research Centre

3. Agents in Context Grid Users PACE Application Tools
Global Grid Management - Agents
Local Grid Schedulers
PACE
Performance
Evaluation
Engine
Grid Resources
PACE Resource Tools

4. Challenges
Scalability. A grid has the potential to encompass a large number of high performance computing resources.
Adaptability. A grid is a dynamic environment where performance of grid resources are changing over time.

5. A4 Methodology Agent structure Agent hierarchy Service advertisement
Communication layer
Decision-making layer
Local management layer
Agent hierarchy
Service advertisement
Service discovery
Agent capability tables A
User A

6. Optimisation Strategies
Advertisement
Data-push & data-pull
Periodic & event-driven
Discovery
Local services
Services in ACTs
Lower or upper agents
Optimisation
Modelling
Simulation M A
User A A A A

7. A Case Study sweep3d fft improc closure jacobi memsort cpi S1 S2 S4 S3
(SGIOrigin2000, 16) S2 S4
(SunUltra10, 16) S3 S5
(SunUltra5, 16) S6
S12
(SunSPARCstation2, 16)
S11 S8
(SunUltra1, 16) S7
S10 S9
sweep3d
fft
improc
closure
jacobi
memsort
cpi

8. Experiment 1
FIFO

9. Experiment 1
FIFO

10. Experiment 2
FIFO

11. Discovery Speed & Efficiency
No advertisement:
Low speed
Low efficiency
Reasonable advertisement:
High speed
High efficiency
Discovery efficiency (*100)
Too much advertisement:
Very high speed
Very low efficiency

12. Conclusions A hierarchy of homogenous agents
Reconfigurable using different optimisation strategies
Step-by-step service advertisement and discovery among agents
Quantitative performance evaluation of agent behaviours

13. Future Work
Use of heuristic iterative algorithms for local grid scheduling
Integrating agents with grid tools, e.g. Globus, Condor, and NWS
More than discovery, enabling automated negotiation and coordination
Dynamic performance tuning of agent behaviours