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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system.

Andrew J Page¹, Thomas M Keane, Thomas J Naughton

¹Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK.

Journal of Parallel and Distributed Computing

|September 24, 2010

Summary

This summary is machine-generated.

This study introduces a novel evolutionary task allocation algorithm that dynamically maps tasks to processors in distributed systems. The new method enhances efficiency by minimizing total execution time compared to existing algorithms.

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Area of Science:

Computer Science
Distributed Systems
Artificial Intelligence

Background:

Dynamic task allocation is crucial for optimizing performance in heterogeneous distributed systems.
Existing heuristic algorithms face challenges in efficiently mapping tasks to processors, especially in dynamic environments.

Purpose of the Study:

To develop and evaluate a multi-heuristic evolutionary task allocation algorithm for minimizing total execution time.
To dynamically map tasks to processors in heterogeneous distributed systems.

Main Methods:

A genetic algorithm combined with eight common heuristics was employed.
The algorithm operates on batches of unmapped tasks and supports preemptive remapping.
Implementation was performed on a Java distributed system.

Main Results:

The algorithm was evaluated on six diverse problems across bioinformatics, biomedical engineering, computer science, and cryptography.
Experiments utilized up to 150 heterogeneous processors.
The proposed algorithm demonstrated superior efficiency compared to state-of-the-art heuristic algorithms.

Conclusions:

The multi-heuristic evolutionary task allocation algorithm effectively minimizes execution time in heterogeneous distributed systems.
The approach offers improved efficiency over existing methods for dynamic task allocation.