Independent Task Scheduling by Artificial Immune Systems, Differential Evolution, and Genetic Algorithms

Author

Krömer, Pavel ; Plato, Jan ; Snasel, Vaclav

Author_Institution

Fac. of Electr. Eng. & Comput. Sci., VSB-Tech. Univ. of Ostrava, Ostrava-Poruba, Czech Republic

fYear

2012

fDate

19-21 Sept. 2012

Firstpage

28

Lastpage

32

Abstract

Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing systems and it is also an appealing NP-complete problem. There is a number of heuristic and metaheuristic algorithms that were tailored to deal with scheduling of independent jobs. In this study we investigate the efficiency of three bio-inspired metaheuristics for finding good schedules of independent tasks.

Keywords

artificial immune systems; computational complexity; distributed processing; genetic algorithms; scheduling; NP-complete problem; artificial immune systems; bioinspired metaheuristics; differential evolution; genetic algorithms; heterogeneous distributed computing systems; independent task scheduling; Genetic algorithms; Immune system; Processor scheduling; Schedules; Sociology; Statistics; Vectors; artificial immune systems; differential evolution; genetic algorithms; independent task scheduling; machine learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Networking and Collaborative Systems (INCoS), 2012 4th International Conference on

Conference_Location

Bucharest

Print_ISBN

978-1-4673-2279-9

Type

conf

DOI

10.1109/iNCoS.2012.76

Filename

6337895