Enhanced task scheduling in cloud data centres using orthogonal opposition-based partial reinforcement optimizer

Abstract

Cloud computing has evolved into a mature technology, seamlessly integrating with modern internet services and functioning as utility computing that delivers infrastructure, platforms, and software on a pay-per-use basis. A key challenge in cloud computing is task scheduling, which significantly impacts both user satisfaction and system performance. Due to the NP-hard nature of the scheduling problem, developing efficient solutions remains complex. This paper proposes an orthogonal opposition-based learning partial reinforcement optimizer (OOLPRO) for efficient task scheduling in IoT-cloud environments. The OOLPRO framework integrates orthogonal oppositional functions (OOF) with the partial reinforcement optimizer (PRO) to overcome the drawbacks of conventional PRO, such as insufficient solution exploitation and premature convergence. The proposed framework integrates orthogonal opposition-based learning with reinforcement-driven optimization to enhance exploration–exploitation balance, accelerate convergence, and improve scheduling performance in terms of energy consumption, execution cost, makespan, and resource utilization compared with existing approaches. The proposed algorithm is designed to optimize multiple conflicting objectives, including cost, energy consumption, and makespan, thereby ensuring efficient allocation of tasks to physical machines within cloud data centres. By incorporating OOF functions, the algorithm enhances the exploration–exploitation balance, leading to improved convergence rates and higher-quality solutions. The performance of OOLPRO is evaluated through extensive simulations and benchmarked against existing task scheduling algorithms. Experimental results demonstrate that the proposed method achieves superior efficiency, resource utilization, and scalability, making it a promising approach for optimizing task scheduling in dynamic cloud computing environments.