Resource allocation and task planning in a cloud environment based on the particle swarm and R-factor optimization algorithm

Cloud computing is a powerful computing technology that provides flexible services to the user anywhere. Resource management and task planning are the most important perspectives of cloud computing. One of the main challenges of cloud computing was scheduling tasks. Typically, task planning and resource management in the cloud is a complex optimization task while considering quality of service requirements. Huge work within task planning focuses only on issues of deadlines and cost optimization, and avoids the importance of availability, reliability, and reliability. The main goal of this study is to develop an optimized algorithm for efficient resource allocation and planning in a cloud environment. This study uses the PSO and R-factor algorithm. The main purpose of the PSO algorithm is to have tasks scheduled on virtual machines to reduce latency and system throughput. PSO is a method generated by the social and collective behavior of swarms of living things in nature, and in which particles search for a problem space to predict a near-optimal or optimal solution. A hybrid algorithm has been developed that combines PSO and R-factor in order to reduce processing time, make the gap and the cost of performing the task at the same time. The results of tests and simulations show that the proposed method is more effective than previously common approaches.

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