QUBO: Quantum-Inspired Metaheuristic-based IoT Task Scheduling in Multi-Cloud Environment

Abstract

Task scheduling in multi-cloud-based IoT is a very relevant issue as the demand on energy-efficient and environmentally conscious computing is growing. The proposed study suggests that a Quantum-Inspired Metaheuristic-based scheduling method based on a Quadratic Unconstrained Binary Optimization (QUBO) model could be used to optimize the task allocation among IoT devices over distributed cloud resources. The main goal is to reduce key performance parameters, such as temperature, cost, energy consumption, and carbon emissions, which are essential in sustainable cloud operations. The suggested approach is assessed on the CEA-Curie workload data set in the CloudSim simulation platform and compared to such traditional algorithms as Genetic Algorithm (GA), Deep Reinforcement Learning (DRL), and Asynchronous Advantage Actor-Critic (A3C). The experimental findings show that the QUBO-based algorithm is much better than the current method with an average temperature, operational cost, energy consumption, and carbon reduction of 21.8%, 19.5%, 24.3%, and 22.7% respectively relative to baseline algorithms. These enhancements indicate that quantum-inspired optimization is effective in solving more complicated multi-objective scheduling issues. The results indicate that the suggested model offers a scalable and sustainable solution to next-generation IoT-cloud ecosystems, which helps increase the impact of reduced environmental impact and improved resource efficiency in multi-cloud systems.