Context: Recent trends show that there is a tremendous shift from IT companies following traditional methods by hosting their applications/systems in self-managed on premise data centers to using the so-called cloud data centers. Cloud computing has received immense popularity due to its architecture and the ease of usage. Due to this increase in demand and shift in practices, there has been a tremendous increase in number of data centers over a period, resulting in increase of energy consumption. In this thesis work, a research is carried out on optimizing the energy consumption of a typical cloud data center. OpenStack cloud computing software is chosen as the platform in this research. We have used live migration as a key aspect in this research.

Objectives: In this research, our objectives are as follows:

• Design an OpenStack testbed to implement the migration of virtual machines.
• To estimate the energy consumption of the data center.
• To design a heuristic algorithm to evaluate the performance metrics and to optimize the overall energy consumption.

Methods: We have used PowerAPI, a software tool to estimate the energy consumption of hosts as well as virtual machines. A heuristic algorithm is designed and implemented in an instrumental OpenStack testbed to optimize the energy consumption. Server consolidation and load balancing of virtual machines methodologies are used in the heuristic algorithm design. Our research is carried out against the functionality of Nova scheduler of OpenStack.

Results: Results section describes the values of performance metrics yielded by carrying out the experiment. The obtained results showed that energy can be optimized significantly by modifying the way OpenStack nova scheduler can work. The experiment is carried out on vanilla OpenStack and OpenStack with the heuristic algorithm in place, In the second case, the nova scheduler algorithms are not used but the heuristic algorithm is used instead. The CPU utilization and CPU load were noticed to be higher than the metrics observed in case of OpenStack with nova scheduler. Energy consumption is observed to be lesser than the consumption in OpenStack design with nova scheduler.

Conclusions: The research tells that energy consumption can be optimized significantly using desired algorithms without compromising the service quality it offers. However, the design impacts on CPU slightly as the metrics are observed to be higher when compared to that in case of OpenStack with nova scheduler. Although it won’t have noticeable impact on the system.