Autonomous DRL-based energy efficient VM consolidation for cloud data centers.

The exponential data growth and demands for high computing resources lead to excessive resource use in cloud data centers, which cause an increase in energy consumption and high carbon emissions in the environment. So, the high energy consumption, inefficient resource usage, and quality of service assurance (QoS) are major challenges for cloud data centers. The dynamic consolidation of Virtual Machines (VMs) is proven to be an efficient way to tackle these issues while reducing energy consumption and improving resource utilization in data centers. It reduces the number of active hosts for energy efficiency by switching under-utilized and idle hosts into lower power mode. So, several heuristics and Artificial Intelligence (AI) based VM consolidation approaches have been published in papers. Most existing approaches rely on the aggressive consolidation of VMs for energy efficiency, thus causing performance degradation and high SLA violation. However, an automated solution is needed to reduce energy consumption and SLA violation by ensuring efficient resource usage in the cloud data center environment. Therefore, this article proposes an energy-efficient autonomous VM consolidation (AVMC) mechanism that has Deep Reinforcement Learning (DRL) based agent for performing VM consolidation decisions. The DRL agent learns the optimal distribution of VMs in the data center, considering energy efficiency and QoS assurance. The real-time workload traces from PlanetLab have been used to validate the proposed mechanism. Experimental results reveal the superiority of the proposed AVMC system over the existing models. AVMC reduced the energy consumption and SLA violation rate significantly. • DRL-based Autonomous VM consolidation (AVMC) mechanism is presented for cloud data centers. • The main goal is to maximize energy saving and minimize SLA violations. • Real workload traces have been used for the experimental evaluation. • Several performance metrics have been used to compare the AVMC method with existing approaches. [ABSTRACT FROM AUTHOR]