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
PACE Toolkit
UserInterface
Interface
User
Source
Code
Analysis
CPU
Object
Editor
Object
Library
PSL Scripts
Compiler
Cache
Resource Tools
HMCL Scripts
Application Model
Application Tools
Network
Resource Model
Evaluation Engine
Evaluation
Engine
Performance
On-the-fly
Performance
Analysis
Analysis
Analysis
The Question Is …
A4 Methodology
An agent is …
•
•
•
•
A local manager
An user middleman
A broker
A coordinator
•
•
•
•
A
A
A
A
service provider
service requestor
matchmaker
router
Service Discovery
Local Management Layer
Local Management Layer
Coordination Layer
Coordination Layer
Communication Layer
Communication Layer
Service Advertisement
NEXT!
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!
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
The Answer Is …
At local level,
PACE functions can
supply accurate
performance info.
At meta level,
agents cooperate
with each other for
service discovery.
ARMS in Context
A4
Grid
Users
Application Tools
(AT)
A4 Simulator
PMA
ARMS
Evaluation Engine
(EE)
PACE
Grid
Resources
Resource Tools
(RT)
ARMS Architecture
ACT
EE
Bottleneck?
EE
ACT
Agents
EE
ACT
Application Models
Cost Models
ACT
EE
ACT
EE
EE
Resource Models
AT
RT
RT
Users
ACT
EE
PMA
ACT
RT
RT
Processors
ARMS Agent Structure
Resource
Allocation
PACE
Evaluation
Engine
Match
Maker
Agent ID
Comm.
Application Model
Cost Model
Eval Results
Application Execution
Communication Module
Advertisement
Discovery
Coordination
Scheduler
Sched. Cost
Service Info
ACT
Manager
App. Info Res. Info
ACTs
Application
Management
Local
Resource
Monitoring
To Another Agent
PMA Structure
ARMS Agent
Monitoring
PMA
Statistical
data
Model Composer
Performance Model
Reconfiguration
Strategies
Simulation Engine
Policies
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 multiagent 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.