Dynamic multi objective task scheduling in cloud computing using reinforcement learning for energy and cost optimization

Abstract

Efficient task scheduling in cloud computing is crucial for managing dynamic workloads while balancing performance, energy efficiency, and operational costs. This paper introduces a novel Reinforcement Learning-Driven Multi-Objective Task Scheduling (RL-MOTS) framework that leverages a Deep Q-Network (DQN) to dynamically allocate tasks across virtual machines. By integrating multi-objective optimization, RL-MOTS simultaneously minimizes energy consumption, reduces costs, and ensures Quality of Service (QoS) under varying workload conditions. The framework employs a reward function that adapts to real-time resource utilization, task deadlines, and energy metrics, enabling robust performance in heterogeneous cloud environments. Evaluations conducted using a simulated cloud platform demonstrate that RL-MOTS achieves up to 27% reduction in energy consumption and 18% improvement in cost efficiency compared to state-of-the-art heuristic and metaheuristic methods, while meeting stringent deadline constraints. Its adaptability to hybrid cloud-edge architectures makes RL-MOTS a forward-looking solution for next-generation distributed computing systems.