Optimising Cloud Resource Allocation Using Particle Swarm Optimisation: A Nature-Inspired Approach

Abstract

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.