An Enhanced Critically Self Correlated Particle Swarm Optimization (CSC-PSO) algorithm for efficient Resource Allocation in Cloud Computing

Abstract

Abstract As we move in the direction of the technology-driven world, cloud computing has become an inevitable paradigm in various communities. Typically, cloud users and providers' contradicting requirements like maximized performance with minimal cost and maximized revenue with increased resource utilization are of great importance. Likewise, there has been an extensive array of improvements achieved in the last few decades targeting minimal response time and cloud resources latency. However, there is still a need for cloud platforms' robustness addressing the peculiar parameter Quality of Service (QoS) which encompass a group of parameters resource availability, throughput, and security. This paper investigates the resource management between the cloud service providers and consumers, considering security as a primary concern. This paper proposes novel method of enhanced Critically Self Correlated Particle Swarm Optimization (CSC-PSO) algorithm for efficient performance of resource allocation in cloud services. The proposed algorithms' performance and Quality of Service properties are evaluated with extensive experiments using real-time datasets in a large-scale network prototyped system and is compared with the existing algorithms.