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Benchmarking the contention aware nature inspired metaheuristic task scheduling algorithms

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Abstract

In this paper, we consider the contention aware task scheduling problem on a grid topology of processors. By contention awareness, we mean that simultaneous communication on a link has to be serialized. To solve this problem, we propose several nature inspired metaheuristic algorithms: Simulated Annealing (SA), Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Bat Algorithm (BA), Cuckoo Search (CS), and Firefly Algorithm (FA). We perform benchmark evaluation of these algorithms for the Normalized Schedule Length (NSL) parameter. The benchmark task graphs that we consider are: random task graphs, peer set task graphs, systolic array task graphs, Gaussian elimination task graphs, divide and conquer task graphs, and fast Fourier transform task graphs.

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Acknowledgements

The authors would like to thank the anonymous referees for giving valuable comments and suggestions to revise the manuscript in the present form.

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  1. Department of Computer Science & Information Systems, Birla Institute of Technology and Science Pilani, Pilani, 333 031, India

    Abhishek Mishra & Prasoon Trivedi

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  1. Abhishek Mishra
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  2. Prasoon Trivedi
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Correspondence to Abhishek Mishra.

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Mishra, A., Trivedi, P. Benchmarking the contention aware nature inspired metaheuristic task scheduling algorithms. Cluster Comput 23, 537–553 (2020). https://doi.org/10.1007/s10586-019-02943-z

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  • DOI: https://doi.org/10.1007/s10586-019-02943-z

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