Multi-Objective Optimization-Oriented Resource Allocation in the Fog Environment

Abstract

Fog computing is a decentralized computer system where data, processing, storage, as well as applications are located anywhere between the cloud and data source. Fog computing takes the cloud closer to users, decreasing the latency and allows the deployment of new delay-sensitive appliances. An important feature of a fog-cloud network is the process of decision-making on assigning the resources to execute the tasks of application. This paper aims to propose a resource allocation strategy for fog computing that determines the effective process under the consideration of the objectives that includes the constraints like credibility score, concurrency, price affordability and task time computation. Moreover, the credibility score is determined based on the execution efficiency, Service response rate, access reliability and Reboot rate. Thereby, the optimal allocation of resources is handled by a new Hybrid Monarch-Dragon Algorithm (HM-DA) that hybrids the concept of Dragonfly Algorithm (DA) and Monarch Butterfly Optimization (MBO) algorithm.