1. Junwei Cao Darren J. Kerbyson Graham R. Nudd
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 Resource Tools Application Tools
Code
Analysis
Object
Editor
Object
Library
CPU
Network
Cache
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. Service Discovery NEXT! Service Advertisement Local Management Layer
Coordination Layer
Communication Layer
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, At local level,
agents cooperate with each other for service discovery.
At local level,
PACE functions can supply accurate performance info.

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

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

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

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