Improved wild horse optimization with levy flight algorithm for effective task scheduling in cloud computing

Abstract Cloud Computing, the efficiency of task scheduling is proportional to effectiveness users. The improved algorithm (also known as Wild Horse Optimization, or IWHO) proposed address problems lengthy time, high-cost consumption, and high virtual machine load in cloud computing scheduling. First, a distribution model built, with cost, machines primary factors. Second, feasible plan for each whale individual corresponding find best individual, which plan; better optimal we use inertial weight strategy Improved optimization improve local search ability effectively prevent from reaching premature convergence. To deliver services access shared resources, Computing (CC) employs service provider (CSP). In CC context, has significant impact on resource utilization overall system performance. It Nondeterministic Polynomial (NP)-hard problem that solved using metaheuristic techniques job environment. This incentive used this study provide Optimization Levy Flight Algorithm Task Scheduling (IWHOLF-TSC) approach, an wild horse levy flight can be addressed environment by utilizing some form symmetry, achieve optimization, such balancing energy efficiency. IWHOLF-TSC technique constructs multi-objective fitness function reducing Makespan maximizing platform. combines (WHO) theory (LF). WHO inspired social behaviours horses. approach's performance validated, results evaluated variety methods. simulation revealed outperformed others situations.