Optimal Task Assignment to Heterogeneous Cores in Cloud Computing Using Particle Swarm Optimization

Musa Mojarad¹ , Nafiseh Sadat Hosseini² , Tahere Lalesangi³

Section:Research Paper, Product Type: Journal-Paper
Vol.9 , Issue.3 , pp.1-6, Jun-2021

Online published on Jun 30, 2021

Copyright © Musa Mojarad, Nafiseh Sadat Hosseini, Tahere Lalesangi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Recently, mobile heterogeneous embedded systems have developed rapidly and significantly due to hardware upgrades. These systems support multiple processor cores, and their energy consumption is increasing as computing capacity increases. Cloud computing is a way to reduce energy costs. In this paper, the issue of energy dissipation when assigning tasks to heterogeneous processors or cloud servers is considered. The objective is to minimize energy cost from all embedded heterogeneous mobile systems via optimally assigning tasks to mobile clouds and heterogeneous cores. The suggested method is an energy-conscious heterogeneous resource management approach that is supported by the heterogeneous task allocation approach. Here, to solve this problem, a combined method according to Particle Swarm Optimization (PSO) and greedy algorithm is used. Experiments performed provide a heterogeneous mobile embedded system with more efficient energy savings in mobile cloud computing.

Key-Words / Index Term :
Energy Consumption, Heterogeneous Cores, Particle Swarm Optimization, Cloud Computing

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