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Title	Optimized Task Scheduling On Fog Computing Environment Using Meta Heuristic Algorithms
ID_Doc	40757
Authors	Jayasena K.P.N.; Thisarasinghe B.S.
Year	2019
Published	Proceedings - 4th IEEE International Conference on Smart Cloud, SmartCloud 2019 and 3rd International Symposium on Reinforcement Learning, ISRL 2019
DOI	http://dx.doi.org/10.1109/SmartCloud.2019.00019
Abstract	Fog Computing paradigm extends the cloud computing technology to the edge of the computer network. The basic concept is kind of similar to cloud computing and supports virtualizations as well. It is very useful in healthcare application, intelligent transportation systems, financial and smart cities. Optimal task scheduling is an important topic in fog computing virtualization. The task scheduling procedure is an NP-complete problem where the time needed to locate the solution varies by the size of the problem. There are various computation-based performance metrics use in scheduling procedure such as energy consumption and execution cost. Optimal task scheduling of tasks in fog computing can be classified as heuristic, meta-heuristics and hybrid task scheduling approaches. The heuristic task scheduling algorithms deliver ease to schedule the task and deliver the best possible solutions, but it doesn't guarantee the optimal result. The meta-heuristics approaches can handle massive search space to discover better optimal solution for task scheduling problem within reasonable time. Smart healthcare application model is implemented and simulated in iFogSim simulator tool which is used to test and select the technique to introduce a Whale optimization algorithm. Whale optimization algorithm is compared with several heuristic algorithms (RR, SJF) and PSO meta-heuristic algorithm. The results show that proposed algorithm improved the average energy consumption of 4.47% and cost 62.07% relative to the RR, SJF algorithms and energy consumption of 4.50% and cost 60.91% relative to the PSO algorithm. © 2019 IEEE.
Author Keywords	Energy efficiency; Fog Computing; iFogsim; Meta-heuristics algorithms; task scheduling; Whale optimization

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