Quantum Reinforcement Learning for QoS-Aware Real-Time Job Scheduling in Cloud Systems

Abstract

Effective cloud job scheduling is essential for enhancing the performance and operational efficiency of cloud-based services, directly impacting their quality of service (QoS). Among existing methodologies, deep reinforcement learning (DRL) has proven effective in addressing complex, multidimensional optimization challenges in real-time scheduling. With advancements in quantum computing, quantum neural networks (QNNs) are showing unique advantages in information representation and processing. This study is the first to explore quantum reinforcement learning (QRL) for real-time job scheduling in cloud systems. Specifically, we propose a QRL framework that utilizes variational and encoding layers to convert state information into quantum data, repeatedly embedded into a QNN to compute optimal value returns. This approach aims to enhance QoS by improving job execution success rates and reducing average response times with unpredictable job arrivals. We present the detailed design of our approach, and our simulation results demonstrate that the QRL method significantly exceeds established baselines, including those based on DRL, across a range of workload intensities and computational resource configurations. This is particularly evident under high-load conditions, where our approach can achieve 55.2% higher success rates, underscoring its significant potential in cloud job scheduling optimization.