Friday 23rd of February 2018

Profit-Aware DVFS Enabled Resource Management of IaaS Cloud

Shahzad Ali

Power consumption of IaaS Cloud is growing at a rapid pace during the last decade. This leads to high operational and maintenance cost, and also reducing the profit margin of IaaS Cloud providers. Apparently profit margin depends on operational cost and revenue earned to comply with the Service Level Agreement (SLA) on the basis of achieved performance level. In this paper, we are building the relationship between energy consumption of servers and fulfillment of SLA ( QoS) in IaaS Cloud. So we propose a framework to maximize revenues from SLA achieved requests on the energy efficient resource management in IaaS Cloud, in which the servers are equipped with DVFS module. Servers are not allowed to be turned on or turned off. Moreover, we consider a common cost of virtual machine (VM) migration or start/stop. This framework explains four sub-problems, i.e. service placement which encapsulating in VM, resource capacity allocation, load balancing and dynamic voltage/frequency scaling. A hybrid optimization solution is proposed to solve these problems and its effectiveness, compared to existing approach, is evaluated using synthetic workloads. Results show that our solution as compared to others can yield significant profit gain for the cloud provider.

Keywords: IaaS Cloud Data center, Energy Efficiency Resource Management, Service Level Agreement (SLA), Virtualization, Dynamic Voltage and Frequency Scaling (DVFS)

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Shahzad Ali
My name is Shahzad Ali. I received the MSc degree in computer Science from the Bahauddin Zakriya University, Multan, Pakistan, in 1994. After my master degree I joined the department of Computers Science & IT, The Islamia University Bahawalpur, Pakistan, as a lecture in 1995 and now I am Assistant Professor there. Currently I am doing Ph.D. degree in the Graduate School of Computer Science and Engineering, University Of Electronic Science and Technology Of China, Chengdu, China. My research interests include energy efficient cloud computing, game theoretical approach for resource allocation in cloud data centers.

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