Presentation of a Multi-Agent Method to Improve Load Balance and Provisioning Dynamic Resources in the Cloud Computing Environment

Background: Cloud computing is an emerging technology that provides multiple processing and storage services over the Internet. Users can store data on the cloud instead of storing it on their devices, thus provide comprehensive access to the data, and also they can executed their applications on powerful cloud computing platforms, so there is no need to install software. Challenges: In such environments, providing resources has become a challenging issue. Another challenge in cloud computing is balancing the load between computing nodes. In resent researches, scheduling or balancing challenges have usually been addressed of one aspect. So that most systems have a factor that is either responsible for scheduling or used for balancing, and in limited multi-factor systems there is little use of new or meta-heuristic algorithms. Method and Finding: Accordingly, in this paper, by presenting a two-factor system based on two meta-heuristic algorithms, particle swarm optimization and ant colony optimization, an attempt is made to balance the load on the cloud infrastructure as well as reduce task execution time as well as energy consumption. Conclusion: According to the comparison made and CloudSim simulation output, the proposed method is more efficient than cat swarming methods as well as the single-factor system.

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