Applying Reinforcement Learning towards automating energy efficient virtual machine consolidation in cloud data centers

Energy awareness presents an immense challenge for cloud computing infrastructure and the development of next generation data centers. Virtual Machine (VM) consolidation is one technique that can be harnessed to reduce energy related costs and environmental sustainability issues of data centers. In recent times intelligent learning approaches have proven to be effective for managing resources in cloud data centers. In this paper we explore the application of Reinforcement Learning (RL) algorithms for the VM consolidation problem demonstrating their capacity to optimize the distribution of virtual machines across the data center for improved resource management. Determining efficient policies in dynamic environments can be a difficult task, however, the proposed RL approach learns optimal behavior in the absence of complete knowledge due to its innate ability to reason under uncertainty. Using real workload data we provide a comparative analysis of popular RL algorithms including SARSA and Q-learning. Our empirical results demonstrate how our approach improves energy efficiency by 25% while also reducing service violations by 63% over the popular Power-Aware heuristic algorithm. •RL VM consolidation model capable of optimizing the distribution of VMs.•Comparative study to evaluating RL learning algorithms and mechanisms.•Energy efficient and performance driven RL consolidation approach.•Applying reward shaping for improved guidance during the learning process.