Optimizing load balancing and task scheduling algorithms in cloud computing

The load balancing and task scheduling are important problems that need 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. Metaheuristic algorithms produce good outcomes in solving NP-Hard problems such as cloud task scheduling problem. In this study, comparative performance analysis of metaheuristic algorithms such as Clonal Selection Algorithm (CSA), Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO) are presented to optimize load balancing and task scheduling in cloud computing environments. Contrary to methods such as hybridization of metaheuristic algorithms and adaptive hyperparameters methods used in the literature to increase the search performance of algorithms, a parallelization method is proposed to increase search performance in this study. The proposed hybrid parallelization method is created by taking advantage of the global population master-slave and multiple-deme methods to be independent of the metaheuristic algorithm and to be implementing flexible. The proposed parallelization model is applied separately to CSA, DE, PSO, and GA algorithms, and detailed search performance analyses are presented. The results showed that when the normal sequential versions of the algorithms are compared, the CSA algorithm achieves more successful results than other algorithms, even though it gives close results to GA. The proposed parallelization model improves the performance of all tested algorithms, and the Parallel Clonal Selection Algorithm (PCSA) that parallel version of the CSA, reached better results than other compared algorithms.