Energy Efficient Resource Scheduling in Fog Computing

Abstract

Fog Computing paradigm extends the cloud computing technology to the edge of the network. The basic concept is kind of similar to cloud computing and supports virtualizations as well. It is very useful in healthcare, intelligent transportation systems and smart cities. Optimal resource scheduling is an important topic in fog computing virtualization. The resource 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 resource scheduling of tasks in fog computing can be classified as heuristic, swarm intelligence 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 swarm intelligence 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 swarm intelligence algorithm. Swarm intelligence 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.