diffusion scheduling in multiagent computing system

Diffusion scheduling in multiagent computing system

Motivation Architecture Algorithms Examples Dynamics

Robert Schaefer, AGH University of Science and Technology, Kraków, Poland

The Group Members:

Maciej Smołka Jagiellonian University, Kraków, Poland

Piotr Uhruski, Marek GrochowskiAGH University of Science and Technology, Kraków, Poland

MotivationDistributed computation paradigms

Diffusion scheduling in multiagent computing system

Motivation Architecture Algorithms Examples Dynamics

message passing librariesPVM Parallel Virtual Machine (1990), MPI Message-Passing Interface (1992)

SOA (Service Oriented Architecture)CORBA (1996), SOAP (1998)

GRIDCondor (1997), Globus (1998), OGSI/OGSA (2002)

Some drawbacks : partially manual resources allocation time consuming deployment and maintenance of the system usually assuming static resources

Motivation

Computation + Agent logic

Agents environmentMiddleware

Application

Network

Heterogeneous Operating Systems

Distributed computing using MAS technology

Diffusion scheduling in multiagent computing system

Motivation Architecture Algorithms Examples Dynamics

Sample task implementations

Smart Solid

Connections

Overview of the OCTOPUS architecture

Middleware

Application

Diffusion scheduling in multiagent computing system

Java

CORBA

Octopus

. . .Java

CORBA

Octopus

Java

CORBA

Octopus

Agents (scheduling, grain control)Agent SDK

Virtual Topology

VCN

Motivation Architecture Algorithms Examples Dynamics

Diffusion scheduling in multiagent computing system

ArchitectureOCTOPUS Key Tasks

Execute Agents

Distributed Communication

Environment Information

Migration

Virtual Network Topology Virtual Computation Node (VCN)

Agent’s Construction Kit

Agents environment

Motivation Architecture Algorithms Examples Dynamics

Algorithms

Analogy to molecular diffusion phenomena

Local scheduling method – every agent is autonomously

searching and allocating resources at neighbouring node

We hope to obtain the asymptotically balanced load

Diffusion scheduling idea

Diffusion scheduling in multiagent computing system

Motivation Architecture Algorithms Examples Dynamics

Diffusion schduling in multiagent computing system

Motivation Architecture Algorithms Examples Dynamics

Diffusion scheduling – main parameters

Diffusion scheduling in multiagent computing system

Motivation Architecture Algorithms Examples Dynamics

Diffusion scheduling algorithm

Diffusion scheduling in multiagent computing system

Motivation Architecture Algorithms Examples Dynamics

Binding energy formulas under consideration

(2)

(1)

Algorithms

Internal job is a dynamic structure of atomic jobs

Sequential computation of contained atomic jobs

New agent creation when the number of contained jobs

exceeds the capacity of the agent

Controlling the computation grain – Container agent

Diffusion scheduling in multiagent computing system

Motivation Architecture Algorithms Examples Dynamics

Algorithms„Weak” synchronization strategy – „Leo the Professional” agent

(J. Momot, K. Kossacki – 2004)

Migrates through the network and gathers information about

computing agents

Responsible for removing redundancy

Allows to avoid total synchronization of the local system

Diffusion scheduling in multiagent computing system

Motivation Architecture Algorithms Examples Dynamics

TestsSpeedup vs. grain in CAE computation

Diffusion scheduling in multiagent computing system

Motivation Architecture Algorithms Examples Dynamics

TestsAgents amount

Execution times [sec] Objective function

Total Average

Migration

Communication

Computation

Parallel time [sec]

Overhead %

1 2 3 4 5 6 7 8

Rastrigin 193,0 42,7 358,9 141,7 7923,0 187,1 5,94 Griewangk 183,7 22,0 168,55 110,95 6421,3 288,2 4,17 Schwefel 163,0 40,4 288,6 138,0 22259,4 558,1 1,88

Overhead of the Agent Oriented technology (the case of HGS computation)

Diffusion scheduling in multiagent computing system

Motivation Architecture Algorithms Examples Dynamics

TestsSpeedup of the Diffusion Scheduling(the case of HGS computation)

Diffusive Scheduling Round-Robin Serial time [sec]

Total agents amount

Parallel time [sec]

Speedup Parallel time [sec]

Speedup

6891 244 371 18,57412 318 21,66981 (a) 6766 299 299 22,62876 334 20,25749 3387 122 374 9,05615 163 20,77914 (b) 2687 94 228 11,78509 131 20,51145

(c) 4221,9 183,7 288,2 14,44104 204,9 20,66099

Diffusion scheduling in multiagent computing system

Motivation Architecture Algorithms Examples Dynamics

Diffusion scheduling in multiagent computing system

Motivation Architecture Algorithms Examples Dynamics

Communication dependent rules

„LAN” case „WAN” emulation

Diffusion scheduling in multiagent computing system

Motivation Architecture Algorithms Examples Dynamics

Experiments in the local area network

(1) (2)

Diffusion scheduling in multiagent computing system

Motivation Architecture Algorithms Examples Dynamics

Experiments in the wide area network

(2)(1)

Conclusions

Motivation Architecture Algorithms Examples Dynamics

Diffusion scheduling in multiagent computing system

Preliminaries

Conclusions

Motivation Architecture Algorithms Examples Dynamics

Diffusion scheduling in multiagent computing system

State equations

Conclusions

Motivation Architecture Algorithms Examples Dynamics

Diffusion scheduling in multiagent computing system

Optimal scheduling problem

Conclusions

• Diffusion scheduling is an effective tool of managing large-scale distributed systems. It is achieved by the low complexity of local scheduling rules and only local communication. It ensures proper agent location in the dynamic network environment.

• Introduced formal description provides the discrete equation of evolution and the characterization of admissible controls as well as the cost functional for computing MAS.

• The optimal scheduling problem posses the unique solution in the class of stationary strategies.

• Total overhead is low in comparison with the computation time (~ 5%).

• No significant requirements imposed over applications.

Diffusion scheduling in multiagent computing system

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Diffusion scheduling in multiagent computing system

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