Abstract
Information technologies must be made aware of the sustainability of cost reduction. Data centers may reach energy consumption levels comparable to many industrial facilities and small-sized towns. Therefore, innovative and transparent energy policies should be applied to improve energy consumption and deliver the best performance. This paper compares, analyzes and evaluates various energy efficiency policies, which shut down underutilized machines, on an extensive set of data-center environments. Data envelopment analysis (DEA) is then conducted for the detection of the best energy efficiency policy and data-center characterization for each case. This analysis evaluates energy consumption and performance indicators for natural DEA and constant returns to scale (CRS). We identify the best energy policies and scheduling strategies for high and low data-center demands and for medium-sized and large data-centers; moreover, this work enables data-center managers to detect inefficiencies and to implement further corrective actions.
Highlights
Data centers, which constitute the computational muscle for cloud computing, can be compared in energy consumption to many industrial facilities and towns
We identify the best energy policies and scheduling strategies for high and low data-center demands and for medium-sized and large data-centers; this work enables data-center managers to detect inefficiencies and to implement further corrective actions
The innovation of the research presented in this paper involves the utilization of data envelopment analysis (DEA) as a mathematical technique to compare the efficiency regarding the consumption of energy and the performance of various workload scenarios, scheduling models and energy efficiency policies
Summary
Data centers, which constitute the computational muscle for cloud computing, can be compared in energy consumption to many industrial facilities and towns. The innovation of the research presented in this paper involves the utilization of data envelopment analysis (DEA) as a mathematical technique to compare the efficiency regarding the consumption of energy and the performance of various workload scenarios, scheduling models and energy efficiency policies. This efficiency analysis enables data-center operators to make appropriate decisions about the number of machines, the scheduling solution and the shut-down strategy that must be applied so that data-centers run optimally. 3. DEA-conducted analysis of the performance impact and energy consumption of a set of scheduling models for large-scale data-centers.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
