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Benchmarking the contention aware nature inspired metaheuristic task scheduling algorithms - 2020

Benchmarking the contention aware nature inspired metaheuristic task scheduling algorithms

Research Area:  Cloud Computing

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.

Keywords:  

Author(s) Name:  Abhishek Mishra & Prasoon Trivedi

Journal name:  Cluster Computing

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s10586-019-02943-z

Volume Information:  volume 23, pages 537–553 (2020)