Enhancing cloud resource efficiency with quantum-inspired swarm intelligence

Provisioning and utilization of computational resources via cloud computing has brought countless changes. Efficiently managing resource allocation, however, is a challenging task given typical dynamic nature of cloud environments where workloads change very quickly. In this paper, an innovative way to integrating the principles of quantum computing and swarm intelligence is proposed for dynamically optimal resource allocation in cloud computing. Since collective decisions of swarm intelligence are inherently uncertain, and such uncertainties are the very nature of quantum mechanics, the proposed framework benefits from the inherent uncertainties of quantum mechanics and leverages the collective decision making behavior of swarm intelligence to dynamically allocate resources to improve efficiency and reduce cost during design time. Towards this end, a model that integrates quantum concepts i.e., superposition and entanglement with its corresponding swarm intelligence algorithms i.e., ant colony algorithm and particle based optimization is developed. We show our approach is better scalable and more efficient than existing resource scheduling algorithms through extensive simulations and performance evaluations. Results consistently provide valid hints of significant improvements in resource utilization, such as a reduction in CPU and energy consumptions spent plus balanced workload partitioning, which means that quantum inspired swarm intelligence can definitely drive the way of cheap cloud computing.