Sustainable Service Allocation using Metaheuristic Technique in Fog Server for Industrial Applications

Abstract

Reducing energy consumption in the fog computing environment is both a research and an operational challenge for the current research community and industry. There are several industries such as finance industry or health care industry required rich resource platform to process big data along with edge computing in fog architecture. As a result, sustainable computing in fog server plays a key role in fog computing hierarchy. The energy consumption in fog servers depends on the allocation techniques of services (user requests) to a set of virtual machines (VMs). This service request allocation in a fog computing environment is a non-deterministic polynomial-time hard (NP-hard) problem. In this paper, the scheduling of service requests to VMs is presented as a bi-objective minimization problem, where a trade-off is maintained between the energy consumption and makespan. Specifically, this paper propose a metaheuristic-based service allocation framework using three metaheuristic techniques, such as Particle Swarm Optimization (PSO), Binary PSO (BPSO) and Bat algorithm (BAT). These proposed techniques allow us to deal with the heterogeneity of resources in the fog computing environment. This paper has validated the performance of these metaheuristic based service allocation algorithms by conducting a set of rigorous evaluations.