Hybrid quantum binary light spectrum classical convolutional neural network based Cost reduction and work load scheduling of geo-distributed data centres

Abstract

Cloud service providers are increasingly shifting workloads across geographically dispersed data centers to reduce energy costs. As data volumes grow in cloud applications, the network expenses associated with moving these workloads between data centers become a significant concern. Previous research has explored various strategies for optimizing inter-data center workload distribution and reducing energy costs. However, none has fully integrated the complexities of energy costs, data transport expenses, and data center queuing times in a holistic manner. In this research, we propose a novel approach that combines a Hybrid Quantum Classical Convolutional Neural Network (HQCCNN) with a Binary Light Spectrum Optimization Algorithm (BLSOA) for intelligent workload distribution across data centers. This hybrid approach optimizes the decision-making process by taking into account resource availability, energy consumption, and latency. Additionally, we introduce the Elk Herd Energy Valley Optimizer to enhance resource allocation by optimizing energy, computing costs, storage capacity, and bandwidth. The model was evaluated using the Amazon EC2 dataset, reflecting real-world cloud workloads. Experimental results demonstrated impressive performance, including a 109 Kbps throughput, 0.18 mJ energy consumption, 0.1 s computation cost, 95% storage capacity, 96% data allocation efficiency, and a delay of only 0.03 s for 120 data points.