Multi-objective Task Scheduling Algorithm in Cloud Computing Using Improved Squirrel Search Algorithm

The optimization of resource allocation methods in cloud computing environments is essential for enhancing system performance and efficiency. The squirrel search algorithm (SSA) is a metaheuristic algorithm based on swarm intelligence, designed to address optimization challenges. One issue that might arises in the SSA is premature convergence. To address this concern, we propose improving the performance of SSA by integrating it with the opposition based learning (OBL) method. The primary objectives of this method encompass the optimization of makespan, throughput, and resource utilization. The improved SSA algorithm was subjected to a comparative analysis with the genetic algorithm (GA), particle swarm optimization (PSO), and the original SSA. The experiment was performed using the CloudSIM simulator, utilizing three different datasets: the SDSC dataset, a simple random dataset, and a stratified random dataset. The factors under consideration for evaluation encompass makespan, average start time, average finish time, average execution time, total wait time, total scheduling length, throughput, resource utilization, total energy consumption, and imbalance degree. From the experimental results, the improved SSA exhibits superiority over other algorithms in the optimization of makespan on a simple random dataset with an average value of 8.333, as well as minimizing overall energy consumption on the san diego super computer (SDSC) blue horizon dataset which has an average value of 449 kWH. The improved SSA exhibits a gradual increase in the experimental results, rendering the outcomes more foreseeable for a greater number of tasks.