Paper Info
Title
Improved Genetic Algorithms and List Scheduling Techniques for Independent Task Scheduling in Distributed Systems
Abstract
Given a set of tasks with certain characteristics, e.g., data size, estimated execution time and a set of processing nodes with their own parameters, the goal of task scheduling is to allocate tasks at nodes so that the total makespan is minimized. The problem has been studied under various assumptions concerning task and node parameters with the resulting problem statements usually being NP-complete. List scheduling (LS) heuristics such as MaxMin and MinMin together with genetic algorithms (GAs) were applied in the past to find solutions. In this paper we investigate new heuristics for both the LS and the GA paradigm with the specific aim of improving the performance of the standard algorithms when task computations involve large data transfers. Experimental results under various environment assumptions illustrate the merits of the new algorithms.
Year
DOI
Venue
2007
10.1109/PDCAT.2007.51
PDCAT
Keywords
Field
DocType
distributed programming,scheduling,distributed system,genetic algorithm,computational complexity,genetic algorithms,distributed systems,np complete problem,task analysis
Standard algorithms,Job shop scheduling,Task analysis,Scheduling (computing),Computer science,Real-time computing,Heuristics,Dynamic priority scheduling,Genetic algorithm,Computational complexity theory,Distributed computing
Conference
ISBN
Citations 
PageRank 
0-7695-3049-4
3
0.57
References 
Authors
17
3
Name
Order
Citations
PageRank
Thanasis Loukopoulos129330.66
Petros Lampsas2859.10
Panos Sigalas330.57