Cloud computing: an efficient load balancing and scheduling of task method using a hybrid optimization algorithm

Abstract

The cloud computing trend on the internet is vital as it allows data and applications to be managed over the internet instead of requiring personal devices. The job of users is scheduled in the resources of the cloud in order to improve performance. Scheduling tasks is an non-deterministic polynomial (NP)-hard problem, as it may have multiple solutions. Various researchers have proposed different load balancing and job scheduling algorithms to optimize the scheduling process in cloud environments, each with disadvantages. Therefore, this research proposes a novel hybrid load balancing and scheduling of tasks by the whale optimization algorithm (WOA) and seagull optimization algorithm (SOA) in the cloud. This hybrid proposed whale-seagull optimization algorithm (WSOA) optimizes task scheduling in the cloud by reducing processing time, response, and execution time, maximizing central processing unit (CPU) utilization, memory utilization, throughput, reliability, and balancing the load. The algorithm is simulated using the CloudSim toolkit package. As compared with existing approaches, simulation results showed better performance in terms of response time, processing time, execution time, CPU utilization, memory utilization, throughput, and reliability and is analyzed by comparing with the harries hawks optimization (HHO), hybrid dragonfly and firefly algorithm (ADA), spider monkey algorithm (SMA) and bird swarm optimization (BSO).