Optimal Task Assignment to Heterogeneous Cores in Cloud Computing Using Particle Swarm Optimization
Keywords:
Energy Consumption, Heterogeneous Cores, Particle Swarm Optimization, Cloud ComputingAbstract
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.
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