Service Placement in Fog Computing Using a Combination of Reinforcement Learning and Improved Gray Wolf Optimization Method

Abstract

Fog computing extends cloud computing to the edge of the network, bringing processing and storage capabilities closer to end users and Internet of Things (IoT) devices. This paradigm helps to reduce latency, improve response time, and optimize bandwidth usage. In the cloud computing environment, service availability is a criterion for determining user satisfaction, which is strongly influenced by response time and optimal allocation of network resources (communication bandwidth). Service placement in fog computing refers to the process of determining optimal locations for placing services in the network. In this paper, the service placement is done by being aware of the volume of user requests from fog nodes by using neural networks, reinforcement learning, and the improved gray wolf optimization (IGWO) method. Based on the results obtained from simulation, the proposed approach has less response time (between 5% and 21%), more favorable load balance, more utility value (12%) and lower Energy consumption by a minimum of 10% and a maximum of 25%.