An availability-aware task scheduling for heterogeneous systems using quantum-behaved particle swarm optimization

Authors:
Hao Yuan;Yong Wang;Long Chen
Affiliations:
Electronic Commerce & Modern Logisties Key Laboratory, Chongqing University of Posts and Telecommunications, Chongqin;Electronic Commerce & Modern Logisties Key Laboratory, Chongqing University of Posts and Telecommunications, Chongqin;School of Computer Science, Chongqing University of Posts and Telecommunications, Chongqin
Venue:
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Year:
2010

Citing 8
Cited 0

Safety and Reliability Driven Task Allocation in Distributed Systems

IEEE Transactions on Parallel and Distributed Systems
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing

IEEE Transactions on Parallel and Distributed Systems
A high performance, low complexity algorithm for compile-time task scheduling in heterogeneous systems

Parallel Computing - Heterogeneous computing
Risk-Resilient Heuristics and Genetic Algorithms for Security-Assured Grid Job Scheduling

IEEE Transactions on Computers
An Efficient Task Scheduling Technique in Heterogeneous Systems Using Self-Adaptive Selection-Based Genetic Algorithm

PARELEC '06 Proceedings of the international symposium on Parallel Computing in Electrical Engineering
An Availability-Aware Task Scheduling Strategy for Heterogeneous Systems

IEEE Transactions on Computers
A Modified Quantum-Behaved Particle Swarm Optimization for Constrained Optimization

IITAW '08 Proceedings of the 2008 International Symposium on Intelligent Information Technology Application Workshops
An Improved Task Scheduling Algorithm for Heterogeneous Systems

CSO '09 Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization - Volume 01

Quantified Score

Hi-index	0.00

Visualization

Abstract

A major challenge in task scheduling is the availability of resources In a heterogeneous environment, where processors operate at different speeds and are not continuously available for computation, achieving a better make-span is a key issue The existing algorithm SSAC has proved to be a good trade-off between availability and responsiveness while maintaining a good performance in the average response time of multiclass tasks But the makespan may be influenced due to load imbalance In this paper we proposed approach try to further optimize this scheduling strategy by using quantum-behaved particle swarm optimization And compared with SSAC and MINMIN in the simulation experiment; results indicate that our proposed technique is a better solution for reducing the makespan considerably.