A Comprehensive Reasoning Framework for Hardware Refresh in Data Centers

With the increased trend of moving towards the consolidation of computing in larger facilities, and with the rise of paradigms such as Cloud, Smart Cities, and IoT, data centers have been highlighted as a major energy consumer. Yet, data centers exist purely to host IT services, which on average tend to account for the largest part of the overall facility energy consumption. Frequent refresh of IT hardware has emerged as a trend in hyper-scale data centers. However, the wider environmental impact of hardware refresh has become a concern. This work provides a comprehensive framework that helps identify the energy saving opportunities, while demonstrating the overall environmental impact related to hardware refresh. The work sheds new light on key relationships such as the one between hardware utilization and Power Usage Effectiveness (PUE) to drive efficiency. Various data center deployment scenarios are used as case studies (based on real-life datasets) to validate the proposed concepts.

[1]  THE CARBON EMISSIONS OF SERVER COMPUTING FOR SMALL- TO MEDIUM-SIZED ORGANIZATIONS , 2012 .

[2]  Albert Y. Zomaya,et al.  A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems , 2010, Adv. Comput..

[3]  Rami G. Melhem,et al.  Holistically evaluating the environmental impacts in modern computing systems , 2016, 2016 Seventh International Green and Sustainable Computing Conference (IGSC).

[4]  Amip J. Shah,et al.  Lifetime exergy consumption of enterprise servers , 2010 .

[5]  Inês L. Azevedo,et al.  Power usage effectiveness in data centers: overloaded and underachieving , 2016 .

[6]  Klaus-Dieter Lange,et al.  Identifying Shades of Green: The SPECpower Benchmarks , 2009, Computer.

[7]  Haifeng Xu,et al.  Green computing: A life cycle perspective , 2013, 2013 International Green Computing Conference Proceedings.

[8]  Rabih Bashroush,et al.  Measuring energy footprint of software features , 2016, 2016 IEEE 24th International Conference on Program Comprehension (ICPC).

[9]  Saurabh Kumar,et al.  Energy Efficient Utilization of Resources in Cloud Computing Systems , 2016 .

[10]  Klaus-Dieter Lange,et al.  The design and development of the server efficiency rating tool (SERT) , 2011, ICPE '11.

[11]  Rabih Bashroush,et al.  Architectural Principles for Energy-Aware Internet-Scale Applications , 2017, IEEE Software.

[12]  Rabih Bashroush,et al.  Data Center Energy Demand: What Got Us Here Won't Get Us There , 2016, IEEE Software.

[13]  Rabih Bashroush,et al.  gUML: Reasoning about Energy at Design Time by Extending UML Deployment Diagrams with Data Centre Contextual Information , 2017, 2017 IEEE World Congress on Services (SERVICES).

[14]  J. Koomey,et al.  Characteristics of low-carbon data centres , 2013 .

[15]  Charles H. Bennett,et al.  Notes on Landauer's Principle, Reversible Computation, and Maxwell's Demon , 2002, physics/0210005.

[16]  Eric Williams,et al.  Energy intensity of computer manufacturing: hybrid assessment combining process and economic input-output methods. , 2004, Environmental science & technology.

[17]  Nikil Kapur,et al.  A case study and critical assessment in calculating power usage effectiveness for a data centre , 2013 .

[18]  H. Arkes,et al.  The Psychology of Sunk Cost , 1985 .

[19]  Stephen Berard,et al.  Implications of Historical Trends in the Electrical Efficiency of Computing , 2011, IEEE Annals of the History of Computing.

[20]  Richard E. Brown,et al.  United States Data Center Energy Usage Report , 2016 .