Multiobjective evolutionary computation algorithms for solving task scheduling problem on heterogeneous systems

Authors:
P. Chitra;P. Venkatesh
Affiliations:
(Correspd. E-mail: pccse@tce.edu) Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, India;Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, India
Venue:
International Journal of Knowledge-based and Intelligent Engineering Systems
Year:
2010

Citing 18
Cited 1

Scheduling parallel program tasks onto arbitrary target machines

Journal of Parallel and Distributed Computing - Special issue: software tools for parallel programming and visualization
Applications and performance analysis of a compile-time optimization approach for list scheduling algorithms on distributed memory multiprocessors

Proceedings of the 1992 ACM/IEEE conference on Supercomputing
Using evolutionary programming to schedule tasks on a suite of heterogeneous computers

Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
Dynamic Critical-Path Scheduling: An Effective Technique for Allocating Task Graphs to Multiprocessors

IEEE Transactions on Parallel and Distributed Systems
Task matching and scheduling in heterogeneous computing environments using a genetic-algorithm-based approach

Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
Static scheduling algorithms for allocating directed task graphs to multiprocessors

ACM Computing Surveys (CSUR)
A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems

Journal of Parallel and Distributed Computing
Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing

IEEE Transactions on Parallel and Distributed Systems
Matching and Scheduling Algorithms for Minimizing Execution Time and Failure Probability of Applications in Heterogeneous Computing

IEEE Transactions on Parallel and Distributed Systems
Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness
Guest Editor's Introduction: Heterogeneous Processing

Computer
A Genetic Algorithm for Multiprocessor Scheduling

IEEE Transactions on Parallel and Distributed Systems
DSC: Scheduling Parallel Tasks on an Unbounded Number of Processors

IEEE Transactions on Parallel and Distributed Systems
A Dynamic Matching and Scheduling Algorithm for Heterogeneous Computing Systems

HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
A Multiobjective Resources Scheduling Approach Based on Genetic Algorithms in Grid Environment

GCCW '06 Proceedings of the Fifth International Conference on Grid and Cooperative Computing Workshops
Bi-objective scheduling algorithms for optimizing makespan and reliability on heterogeneous systems

Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
A New Approach to Scheduling Parallel Programs Using Task Duplication

ICPP '94 Proceedings of the 1994 International Conference on Parallel Processing - Volume 02

Application and comparison of hybrid evolutionary multiobjective optimization algorithms for solving task scheduling problem on heterogeneous systems

Applied Soft Computing

Quantified Score

Hi-index	0.01

Visualization

Abstract

The task scheduling problem in heterogeneous systems (TSPHS) is a NP-complete problem. It is a multiobjective optimization problem (MOP). The objectives such as makespan, average flow time, robustness and reliability of the schedule are considered for solving task scheduling problem. This paper considers only the two objectives of minimizing the makespan (schedule length) and maximizing the reliability in the multiobjective task scheduling problem. Multiobjective Evolutionary Computation algorithms (MOEAs) are well suited for Multiobjective task scheduling for heterogeneous environment. The two Multi-Objective Evolutionary Algorithms such as Multiobjective Genetic Algorithm (MOGA) and Multiobjective Evolutionary Programming (MOEP) with non-dominated sorting are developed and compared for the various random task graphs and also for a real-time numerical application graph. This paper demonstrates the capabilities of MOEAs to generate well-distributed Pareto optimal fronts in a single run.