Abstract
In recent times, internet of things (IoT) applications on the cloud might not be the effective solution for every IoT scenario, particularly for time sensitive applications. A significant alternative to use is edge comput-ing that resolves the problem of requiring high bandwidth by end devices. Edge computing is considered a method of forwarding the processing and communication resources in the cloud towards the edge. One of the consid-erations of the edge computing environment is resource management that involves resource scheduling, load balancing, task scheduling, and quality of service (QoS) to accomplish improved performance. With this motivation, this paper presents new soft computing based metaheuristic algorithms for resource scheduling (RS) in the edge computing environment. The SCBMA-RS model involves the hybridization of the Group Teaching Optimization Algorithm (GTOA) with rat swarm optimizer (RSO) algorithm for optimal resource allocation. The goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way, that the client???s necessities are satisfied with the minimum number of possible resources and optimal energy consumption. The problem is formulated based on the availability of VMs, task characteristics, and queue dynamics. The integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data center. For experimental validation, a comprehensive set of simulations were performed using the CloudSim tool. The experimental results showcased the superior performance of the SCBMA-RS model interms of different measures.