Optimising Cloud Resource Allocation Using Particle Swarm Optimisation: A Nature-Inspired Approach - Harshita Shukla; Dr. Vijay Mohan Shrima (Sampreshan, ISSN:2347-2979 UGC CARE Group 1, 2026)
The widespread adoption of cloud computing has fundamentally changed the delivery and consumption of computing resources, providing organizations with scalable infrastructure and on-demand services. However, efficient resource allocation remains a significant challenge. Fluctuating workloads, energy consumption constraints, and cost considerations complicate the pursuit of optimal performance in cloud environments. Traditional heuristic techniques frequently encounter limitations when addressing tasks such as virtual machine (VM) placement, scheduling, and load distribution in these dynamic settings. This research investigates the use of Particle Swarm Optimization (PSO) to enhance cloud resource allocation. PSO, inspired by the collective behavior of swarms, iteratively searches for near-optimal solutions by coordinating multiple candidate solutions. The study assesses the extent to which PSO can reduce execution time, maximize resource utilization, and decrease power consumption relative to traditional allocation strategies. Furthermore, this study examines hybrid PSO approaches that integrate elements from Genetic Algorithms, Reinforcement Learning, and Neural Network models to address inherent challenges such as premature convergence and highdimensional search spaces. Experimental results demonstrate that PSO-based methods can yield measurable improvements in cloud system efficiency, cost savings, and overall performance. Future research should explore adaptive and real-time PSO frameworks capable of automatically adjusting to workload variations to further enhance cloud resource management. Keywords—Cloud Computing, Resource Allocation, Particle Swarm Optimization (PSO), Virtual Machine Placement, Task Scheduling, Load Balancing, Energy Efficiency, Metaheuristic Optimization, Hybrid PSO, Artificial Intelligence in Cloud Computing.