Optimizing Energy Consumption Using Live Migration

Abstract: Context: Cloud Computing has evolved and advanced over the recent years due to its concept of sharing computing resources rather than having local servers to handle applications. The growth of Cloud Computing has resulted in large number of datacenters around the world containing thousands of nodes. The nodes are used to process various forms of workloads. Generally, the datacenters efficiency is calculated solely based how fast workload can be processed. Recently, energy consumption has been adopted as additional efficiency metric. The main reasons for this development is increased environmental awareness and escalating costs related to supplying power to large number units and to datacenter cooling. Cloud providers has developed the concept of virtualization, where multiple operating system and applications run on the same server at the same time. A key feature enabled by virtualization is migrating a virtual machine from one physical host to another. In particular, the capability of Virtual Machine (VM) migration brings multiple benefits such elastic resource sharing and energy aware consolidation. Live Virtual Machine migration in datacenters has great potential to decrease energy consumption up to certain level of usage. Objectives: The aim of this thesis is to perform cold and/or live migration to relocate Virtual Machines among hosts in a datacenter thereby reducing the energy consumption. PowerAPI is used to estimate the energy consumption of each VM. A heuristic algorithm is developed and evaluated in order to optimize energy consumption. The overall CPU utilization is calculated during the live migration when the energy consumed is optimized. Method: With the obtained knowledge about the VM migration and the factors that influence the migration process, a heuristic algorithm is designed for limiting energy consumption in datacenter. The algorithm takes the energy distribution over a set of VMs and corresponding hosts as input. The output of this algorithm will be the redistribution of VMs to the hosts such that the overall energy consumption is lowered. The proposed model is implemented and evaluated in an Openstack environment. Results: The results of the experiment study give the energy consumption of each node and then sumup to give the total energy consumption of the datacenter. The results are taken with the default OpenStack VM placement algorithm as well as with the heuristic algorithm developed in this work. The comparison of results indicate that the total energy consumption of the datacenter is reduced when the heuristic is used. The overall CPU utilization of each node is evaluated and the values are almost similar when compared with heuristic. Conclusion: The analysis of results concludes that the overall energy consumption of the datacenter is optimized by relocating the virtual machines among hosts according to the algorithm using virtual machine live migration. This also results that CPU Utilization is not varied much when live migration is used to optimize the energy consumption.