Power aware provisioning in cloud computing environment

• Published in: 2011 International Conference on Computer, Communication and Electrical Technology pp. 6 - 11
• Main Authors: Raj, V K Mohan, Shriram, R
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2011
• Subjects: cloud computing; Computational modeling; Energy consumption; power aware provisioning; Power demand; Servers; Silicon; Switches; Virtual machining
• ISBN: 1424493935; 9781424493937
• DOI: 10.1109/ICCCET.2011.5762447

In today's IT computing era, there is shift to aggregate computing resources into data centers (DC) shared by pool of users. With the increasing popularity of cloud computing, this pool of users are now more global than any-time in the past The resource demand of these users are inadvertently governed by strict "service level agreements". In this paradigm, data centers' operational energy costs are a rising concern as they continue an upward trend that is poised to surpass the capital cost of equipment in a typical lifetime usage model. A data center is a complex distributed system comprised of a hierarchy of numerous components; and operating at multitude levels of usage abstractions. Virtualization is an approach to consolidating multiple services onto a smaller number of computing resources. A virtualized server environment allows computing resources to be shared among multiple performance-isolated platforms called virtual machines. Virtual Machine(VM) technology has been widely applied in data center environments due to its seminal features, including reliability, flexibility, and the ease of management. We define set of service-provider controllable policies comprising of number of physical servers, possible number of VMs, possible VM request queue length and maximum number of requests per VM threshold. For each of these defined policy, we implement a dynamic resource provisioning framework for virtualized server environment which achieves power efficiency by using optimum power efficient allocation and workload forecasting scheme to ensure that workload request meet the defined SLAs and the data center operates with least power consumption. Finally, based on the results from our implementation, we analyze and infer on the best policy which meets SLA commitments and has the least energy consumption.