Presenting a task scheduling model in grid networks based on the lion optimization algorithm

The load balancing challenge in cloud computing is very important for the effective management of cloud resources and it requires distributing the incoming network traffic or computational workload among multiple servers in a way that no server is overloaded, hence improving resource utilization, increasing throughput and reducing the response time of load balancing is crucial for distributed systems to achieve high availability and fault tolerance. In this paper, a task scheduling model in grid networks based on the Lion on the Optimization is presented. The aim of this paper is to present an improved Lion on the Optimization metaheuristic approach to solve load balancing problems in the cloud. In fact, the proposed method addresses the challenge of a single-machine scheduling problem in which the machine must be maintained after a fixed periodic interval. The aim is to minimize the total absolute deviation of the completion times for the problem under consideration. Hence, to solve this problem, a new metaheuristic algorithm, namely Lion on the Optimization, was used in the proposed method, and the comparative results showed the absolute superiority of our proposed algorithm over the base paper.