Adam-Ladybug Beetle Optimization enabled multi-objective service placement strategy in fog computing

Abstract

The Internet of Things (IoT) has transformed every aspect of our lives and has become universal in multiple fields from personnel to government and military applications. However, IoT suffers from the inherent limitation of latency and high computational costs, which can be effectively overcome by using a fog computing framework. However, the key challenge in fog computing is to address the problem of service placement among the nodes, thereby providing optimal utilization of resources and minimizing service time. This research work presents a novel service placement technique, by considering the service placement issue as a multi-objective optimization problem. Here, a two-level fog computing network comprising a fog master node and fog cells is considered. The master node is responsible for the service placement of the fog nodes, and the service placement is carried out using the Adam-Ladybug Beetle Optimization (ALBO) algorithm. Further, multiple objectives, like resource utilization, makespan, response time, service time, cost, and energy consumption are considered to enhance service placement. Moreover, the efficiency of the ALBO for service placement (ALBO_SP) is examined considering service cost, energy consumption, and service time and is found to attain values of 19.009, 73.581 J, and 4.854 s, respectively.