Hybrid meta-heuristics algorithms for task assignment in heterogeneous computing systems

In this paper we tackle the task assignment problem (TSAP) in heterogeneous computer systems. The TSAP consists of assigning a given distributed computer program formed by a number of tasks to a number of processors, subject to a set of constraints, and in such a way a given cost function to be mini...

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Vydáno v:Computers & operations research Ročník 33; číslo 3; s. 820 - 835
Hlavní autoři: Salcedo-Sanz, Sancho, Xu, Yong, Yao, Xin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Elsevier Ltd 01.03.2006
Elsevier Science
Pergamon Press Inc
Témata:
Algorithms
Applied sciences
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Exact sciences and technology
Flows in networks. Combinatorial problems
Genetic algorithms
Heterogeneous computer systems
Heuristic
Mathematical problems
Meta-heuristics
Neural networks
Operational research and scientific management
Operational research. Management science
Operations research
Simulated annealing
Software
Studies
Task assignment
Heterogeneous computer systems
Task assignment
Simulated annealing
Genetic algorithms
Meta-heuristics
Hopfield neural nets
Assignment problem
Distributed system
Penalty function
Heterogeneity
Genetic algorithm
Resource constraint
Computer system
Repair
ISSN:0305-0548, 1873-765X, 0305-0548
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Popis
Shrnutí:In this paper we tackle the task assignment problem (TSAP) in heterogeneous computer systems. The TSAP consists of assigning a given distributed computer program formed by a number of tasks to a number of processors, subject to a set of constraints, and in such a way a given cost function to be minimized. We introduce a novel formulation of the problem, in which each processor is limited in the number of task it can handle, due to the so called resource constraint. We propose two hybrid meta-heuristic approaches for solving this problem. Both hybrid approaches use a Hopfield neural network to solve the problem's constraints, mixed with a genetic algorithm (GA) and a simulated annealing for improving the quality of the solutions found. We test the performance of the proposed algorithms in several computational TSAP instances, using a GA with a penalty function and a GA with a repairing heuristic for comparison purposes. We will show that both meta-heuristics approaches are very good approaches for solving the TSAP.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2004.08.010