An intelligent energy efficiency framework in cloud computing using deep reinforcement learning

Abstract

The rapid expansion of cloud computing has resulted in a large increase of energy consumption, posing serious environmental and economic concerns. Data centers consume about 7% of worldwide electricity, a number that is anticipated to climb to 13% by 2030, contributing 2% of global emissions, comparable to the aviation industry. Current frameworks face challenges such as high energy consumption, poor job scheduling, non-adaptability to dynamic environments, and model optimization issues. These limitations demand the development of more efficient and adaptable solutions. This research proposes an intelligent energy efficiency framework for cloud computing based on Deep Reinforcement Learning (DRL). By dynamically adjusting to real- time workloads, this framework is designed for optimizing the energy consumption while maintaining the quality of service (QoS). This proposed DRL model formulates job scheduling through state and action spaces, incorporating real-time job characteristics such as arrival rates, types, and deadlines. The framework includes decision-making phases and reward functions to ensure effective and adaptable scheduling. The results show that the proposed DRL framework significantly outperformed related works demonstrating a 38% reduction in cloud energy consumption, a 98.4% jobs success rate, an improvement in QoS of 115%, a 66% reduction in response times, and a resource utilization of 90.7%. Additionally, the framework's scalability and adaptability to dynamic cloud conditions were validated through rigorous testing under various scenarios, confirming its robustness in handling fluctuating workloads and diverse job types. These outcomes address the identified problems by significantly enhancing energy efficiency, job scheduling effectiveness, and overall system performance.