Optimizing Load Balancing and Task Scheduling Problems in Cloud Computing

The task scheduling problem is an important problem that needs to be optimized in cloud computing in terms of meeting the expectations of both the user and the provider. A poorly optimized scheduling method harms the customer and the provider due to non-fulfillment of quality of service (QoS) and violation of the service level agreement (SLA). It is possible to meet customer and provider demands in the best way with a well-optimized task scheduling algorithm. The cloud task scheduling problem is an NP-Hard problem. Metaheuristic algorithms produce good outcomes in solving these problems. In this study, clonal selection algorithm (CSA) and parallel CSA (PCSA) are proposed to optimize load balancing and task scheduling in cloud computing environments. In addition to the results of the CSA and PCSA, a similar parallelization method to improve searching performance is applied to the genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO), and comparative results are presented. The results showed that the CSA algorithm, like other compared metaheuristic algorithms, was successful in the cloud task scheduling problem. Parallelization improved the performance of all tested algorithms, and the proposed PCSA algorithm reached better results than other compared algorithms.