Ensemble Resource Allocation using Optimized Particle Swarm Optimization (PSO) in Cloud Computing

Cloud computing has revolutionized how resources are provisioned and managed in the information technology landscape. One of the most critical challenges in cloud computing is efficient resource allocation to ensure optimal utilization of resources while meeting the performance requirements of applications. Traditional resource allocation methods often need to be revised to address the dynamic and complex nature of cloud environments. This research presents an Ensemble Resource Allocation (ERA) in cloud computing using Linear Programming (LP) and Particle Swarm Optimization (PSO). PSO is a heuristic optimization technique inspired by the collective behavior of birds and fish. However, PSO, like many optimization algorithms, has its limitations, such as premature convergence and suboptimal solutions. To overcome these limitations, an optimized version of PSO is proposed that enhances its performance in cloud resource allocation. The proposed approach combines PSO with adaptive mechanisms to address the challenges specific to cloud environments. A fitness function is considered not only for resource utilization and application performance but also for cost, energy consumption, and service-level agreements. The multifaceted fitness function ensures that the allocation decisions are made with a holistic view of cloud resource management. Furthermore, the optimized PSO algorithm leverages intelligent heuristics to adjust its parameters dynamically, adapt to changing workloads, and optimize resource allocation continuously. This adaptability allows our approach to efficiently allocate resources under various operating conditions, making it well-suited for real-world cloud environments. In this work, the proposed system is evaluated through extensive simulations and practical experiments, comparing its performance against traditional resource allocation methods. The results demonstrate that the optimized PSO-based approach outperforms existing methods regarding resource utilization, application performance, and overall cost-effectiveness. This research contributes to the ongoing efforts to enhance resource allocation strategies in cloud computing, addressing the complexities of modern cloud environments while ensuring efficient resource utilization. The findings of this study have implications for cloud service providers, enterprises, and organizations seeking to optimize their resource allocation processes and, ultimately, enhance the quality of services delivered through the cloud.