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Artificial Life Techniques for Load Balancing in Computational Grids

Subrataa, R.; Zomaya, A. and Landfeldt, Björn LU (2007) In Journal of Computer and System Sciences 73(8). p.1176-1190
Abstract
Load balancing is a very important and complex problem in computational grids. A computational grid differs from traditional high performance computing systems in the heterogeneity of the computing nodes and communication links, as well as background workloads that may be present in the computing nodes. There is a need to develop algorithms that could capture this complexity yet can be easily implemented and used to solve a wide range of load balancing scenarios. Artificial life techniques have been used to solve a wide range of complex problems in recent times. The power of these techniques stems from their capability in searching large search spaces, which arise in many combinatorial optimization problems, very efficiently. This paper... (More)
Load balancing is a very important and complex problem in computational grids. A computational grid differs from traditional high performance computing systems in the heterogeneity of the computing nodes and communication links, as well as background workloads that may be present in the computing nodes. There is a need to develop algorithms that could capture this complexity yet can be easily implemented and used to solve a wide range of load balancing scenarios. Artificial life techniques have been used to solve a wide range of complex problems in recent times. The power of these techniques stems from their capability in searching large search spaces, which arise in many combinatorial optimization problems, very efficiently. This paper studies several well-known artificial life techniques to gauge their suitability for solving grid load balancing problems. Due to their popularity and robustness, a genetic algorithm (GA) and tabu search (TS) are used to solve the grid load balancing problem. The effectiveness of each algorithm is shown for a number of test problems, especially when prediction information is not fully accurate. Performance comparisons with Min–min, Max–min, and Sufferage are also discussed. (Less)
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author
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Computer and System Sciences
volume
73
issue
8
pages
1176 - 1190
publisher
Elsevier
external identifiers
  • scopus:34948846654
ISSN
0022-0000
DOI
10.1016/j.jcss.2007.02.006
language
English
LU publication?
no
id
94fc11d2-b847-4108-a6e6-45d3b257adee (old id 3173118)
date added to LUP
2012-11-19 15:41:21
date last changed
2017-10-01 04:34:06
@article{94fc11d2-b847-4108-a6e6-45d3b257adee,
  abstract     = {Load balancing is a very important and complex problem in computational grids. A computational grid differs from traditional high performance computing systems in the heterogeneity of the computing nodes and communication links, as well as background workloads that may be present in the computing nodes. There is a need to develop algorithms that could capture this complexity yet can be easily implemented and used to solve a wide range of load balancing scenarios. Artificial life techniques have been used to solve a wide range of complex problems in recent times. The power of these techniques stems from their capability in searching large search spaces, which arise in many combinatorial optimization problems, very efficiently. This paper studies several well-known artificial life techniques to gauge their suitability for solving grid load balancing problems. Due to their popularity and robustness, a genetic algorithm (GA) and tabu search (TS) are used to solve the grid load balancing problem. The effectiveness of each algorithm is shown for a number of test problems, especially when prediction information is not fully accurate. Performance comparisons with Min–min, Max–min, and Sufferage are also discussed.},
  author       = {Subrataa, R. and Zomaya, A. and Landfeldt, Björn},
  issn         = {0022-0000},
  language     = {eng},
  number       = {8},
  pages        = {1176--1190},
  publisher    = {Elsevier},
  series       = {Journal of Computer and System Sciences},
  title        = {Artificial Life Techniques for Load Balancing in Computational Grids},
  url          = {http://dx.doi.org/10.1016/j.jcss.2007.02.006},
  volume       = {73},
  year         = {2007},
}