Energy Optimization for Virtual Machines Scheduling in Cloud Data Centers
The rapid growth in the data storage and data processing demands the energy consumption of data centers. Recently it became a major issue in large data centers due to financial and environmental concerns. Virtual Machine (VM) allocation for multiple tenants is an important and challenging problem to provide efficient infrastructure services in cloud data centers. Tenants run applications on their allocated VMs and the network distance between a tenant’s VMs may considerably impact the tenant’s Quality of Service (QoS). Existing two greedy approximation algorithms like Minimum Energy virtual machine Scheduling algorithm (MinES) and Minimum Communication virtual machine Scheduling algorithm (MinCS) had been proposed to reduce the consumption of energy in data centers. In order to overcome this drawback, we proposed Dynamic Data Virtualization (DDV) algorithm, which reduces the CPU utilization, energy consumption in data centers and more number of requests can be sent to the server is demonstrated. The performance of the DDV algorithm with the the real-time constraint of both VMs and Physical Machines (PMs).
Keywords - Energy, Real-time, Tenants, Virtual machine, Physical machine.