Green Cloud Computing with Efficient Resource Allocation Approach

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Author(s): Fei Cao (University of Central Missouri, USA), Michelle M. Zhu (Southern Illinois University – Carbondale, USA)and Chase Q. Wu (New Jersey Institute of Technology, USA)
Copyright: 2015
Pages: 33
Source title: Green Services Engineering, Optimization, and Modeling in the Technological Age
Source Author(s)/Editor(s): Xiaodong Liu (Edinburgh Napier University, UK)and Yang Li (British Telecom, UK)
DOI: 10.4018/978-1-4666-8447-8.ch005

Keywords: Business & Management / Electronic Services & Service Science / Environmental Science and Technologies / Information Science Reference

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Abstract

Due to the increasing deployment of data centers around the globe escalated by the higher electricity price, the energy cost on running the computing, communication and cooling together with the amount of CO2 emissions have skyrocketed. In order to maintain sustainable Cloud computing facing with ever-increasing problem complexity and big data size in the next decades, this chapter presents vision and challenges for energy-aware management of Cloud computing environments. We design and develop energy-aware scientific workflow scheduling algorithm to minimize energy consumption and CO2 emission while still satisfying certain Quality of Service (QoS). Furthermore, we also apply Dynamic Voltage and Frequency Scaling (DVFS) and DNS scheme to further reduce energy consumption within acceptable performance bounds. The effectiveness of our algorithm is evaluated under various performance metrics and experimental scenarios using software adapted from open source CloudSim simulator.

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Green Cloud Computing with Efficient Resource Allocation Approach

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