Abstract
Tasks scheduling in cloud computing environment plays an important role for both Cloud Service Providers (CSPs) and the users of the services provided. Therefore, designing an efficient task scheduling algorithm, which fulfill the requirements of CSPs and their clients is essential. Several scheduling algorithms are proposed by various researchers for task scheduling in cloud computing environments. This paper introduces an alternative method for cloud task scheduling problem, which aims to minimize makespan of executing a number tasks on different Virtual Machines (VMs). This method is based on Bumble Bee Mating Optimization (BBMO) algorithm. BBMO is powered by the features of swarm intelligence and local search algorithms. The performance of BBMO is compared to two existing algorithms, Honey Bee Mating Optimization (HBMO) algorithm and Genetic Algorithm (GA). Finally, we analyze the performance of the proposed algorithm with other two algorithms using different scenarios of experiments. The results show that the proposed algorithm (BBMO) outperforms other algorithms.