POWER MANAGEMENT AND AUTOMATIC RESOURCE PROVISIONING FRAMEWORK FOR CLOUD INFRASTRUCTURES BASED ON MACHINE LEARNING

Power is becoming an increasingly important concern for large-scale cloud computing systems. Meanwhile, cloud service providers leverage virtualization technologies to facilitate service consolidation and enhance resource utilization. In a virtualized environment, resource needs to be configured at runtime at the cloud, server and virtual machine levels to achieve high power efficiency. In addition, cloud power management should guarantee high users’ SLA (service level agreement) satisfaction. This paper provides power management and automatic resource provisioning framework for cloud infrastructures based on machine learning. It is a proactive technique for power management and auto-scaling of resources that changes the number of resources for the private cloud dynamically based on system load is proposed. To configure cloud resources, we consider machine learning techniques to achieve automatic resource allocation and optimal power efficiency. The technique that supports both on-demand and advance reservation requests uses machine learning. Experimental results demonstrate that the proposed technique can effectively estimate the power usage of cloud servers and reduction of cost for the client enterprise. Additionally resource configuration approach achieves the lowest energy usage among the compared three approaches.