Quantum-Inspired Swarm Intelligence for Dynamic Resource Allocation in Cloud Computing Environments

Cloud computing changed how computational resources are provided and used. Still, managing resource allocation efficiently is a big challenge in changing cloud settings where workloads shift quickly. In this paper, we introduce a novel method that effectively combines principles from quantum computing with swarm intelligence to address the challenges of dynamic resource allocation in cloud computing. The framework we propose in this paper uses insights from the intrinsic uncertainties of quantum mechanics and group decision making behaviors seen at swarm intelligence, such that resources will be allocated dynamically during design-time to take advantage of increasing effective use leading to cost reduction. In this document, a model and method based on the use of concepts from quantum mechanics such as superposition and entanglement with algorithms swarm intelligence like ants colonia or optimization techniques based on particle behavior in swarm intelligence are described, focusing on their capacity to enhance resource allocation. We validate that our approach performs well and scales better than conventional resource scheduling algorithms by executing multiple simulations through various tests. Our results consistently demonstrate significant improvements in resource usage, including CPU and energy consumption, as well as workload distribution among machines. This highlights the effectiveness of quantum swarm intelligence in enhancing cloud computing's cost-effectiveness.