Mapping Data Centre Business Types with Power Management Strategies to Identify Demand Response Candidates

Data centres are key to a future energy system with a high share of intermittent renewable energy sources. They form the cloud that enables a modern life-style and cyber-physical based production; however, fulfilling these tasks makes them the fastest growing component of IT power demand. In the future energy system the flexibility of consumers' power demand will be a necessary response to the growing intermittency of power supply. Therefore, data centres as big consumers become one target power consumer group to integrate their flexibility potential into daily business operation. Research has seen this necessity for some years and developed technical strategies to control the power profile of a data centre, identifying a huge demand response potential. To date, however, hardly any data centre offers their flexbility to the power market. The reasons for the gap between the technical potential and realized valorization of a data centre's power flexibility are manifold. This paper aims at analysing this gap from a business viewpoint: Which data centre types do generally have customer contracts allowing for the necessary power flexibility? And to which degree do data centres have the necessary control over their power profile? In order to approach this set of questions, in a first step, typologies of data centres and of power saving strategies are created. Then data centre types are mapped with suitable power management strategies, and finally the results of this mapping process are discussed.

[1]  Daniel A. Menascé,et al.  A Taxonomy of Job Scheduling on Distributed Computing Systems , 2016, IEEE Transactions on Parallel and Distributed Systems.

[2]  Adam Wierman,et al.  Opportunities and challenges for data center demand response , 2014, International Green Computing Conference.

[3]  Anders Clausen,et al.  Supercomputing Centers and Electricity Service Providers: A Geographically Distributed Perspective on Demand Management in Europe and the United States , 2016, ISC.

[4]  Damián Fernández-Cerero,et al.  Energy wasting at internet data centers due to fear , 2015, Pattern Recognit. Lett..

[5]  Maurizio Giacobbe,et al.  Towards energy management in Cloud federation: A survey in the perspective of future sustainable and cost-saving strategies , 2015, Comput. Networks.

[6]  Sonja Klingert,et al.  Making Data Centers Fit for Demand Response: Introducing GreenSDA and GreenSLA Contracts , 2018, IEEE Transactions on Smart Grid.

[7]  M.K. Patterson,et al.  The effect of data center temperature on energy efficiency , 2008, 2008 11th Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems.

[8]  Sonja Klingert,et al.  Introducing Flexibility into Data Centers for Smart Cities , 2015, SMARTGREENS/VEHITS.

[9]  Rolf Stadler,et al.  Resource Management in Clouds: Survey and Research Challenges , 2015, Journal of Network and Systems Management.

[10]  Cheng-Jen Tang,et al.  Exploring the Potential Benefits of Demand Response in Small Data Centers , 2013 .

[11]  Baochun Li,et al.  Reducing electricity demand charge for data centers with partial execution , 2013, e-Energy.

[12]  Rebecca Green,et al.  Typologies and taxonomies: An introduction to classification techniques , 1996 .

[13]  Chuan Zhu,et al.  A Survey and Taxonomy of Energy Efficiency Relevant Surveys in Cloud-Related Environments , 2017, IEEE Access.

[14]  Torsten Wilde,et al.  Analysis of the efficiency characteristics of the first High-Temperature Direct Liquid Cooled Petascale supercomputer and its cooling infrastructure , 2017, J. Parallel Distributed Comput..

[15]  Kai Zhu,et al.  Estimating the maximum energy-saving potential based on IT load and IT load shifting , 2017 .

[16]  Fabien Hermenier,et al.  An Energy Aware Application Controller for Optimizing Renewable Energy Consumption in Data Centres , 2015, 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC).

[17]  Mikko Majanen,et al.  FIT4Green : energy aware ICT optimization policies , 2010, e-Energy 2010.

[18]  Thorsten Urbaneck,et al.  Advanced Concepts for Renewable Energy Supply of Data Centres , 2017 .

[19]  Barbara Pernici,et al.  CO2-Aware Adaptation Strategies for Cloud Applications , 2016, IEEE Transactions on Cloud Computing.

[20]  Kevin Skadron,et al.  Understanding the energy efficiency of simultaneous multithreading , 2004, Proceedings of the 2004 International Symposium on Low Power Electronics and Design (IEEE Cat. No.04TH8758).

[21]  Tajana Simunic,et al.  Providing regulation services and managing data center peak power budgets , 2014, 2014 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[22]  Laurent Lefèvre,et al.  A survey on techniques for improving the energy efficiency of large-scale distributed systems , 2014, ACM Comput. Surv..

[23]  Jeffrey S. Chase,et al.  Making Scheduling "Cool": Temperature-Aware Workload Placement in Data Centers , 2005, USENIX Annual Technical Conference, General Track.

[24]  James R. Larus,et al.  Zeta: scheduling interactive services with partial execution , 2012, SoCC '12.

[25]  César A. F. De Rose,et al.  Modeling power consumption for DVFS policies , 2015, 2015 IEEE International Symposium on Circuits and Systems (ISCAS).

[26]  Marcel Antal,et al.  Optimized flexibility management enacting Data Centres participation in Smart Demand Response programs , 2018, Future Gener. Comput. Syst..

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

[28]  Torsten Wilde,et al.  Taking Advantage of Node Power Variation in Homogenous HPC Systems to Save Energy , 2015, ISC.

[29]  Qi Li,et al.  Thermal Time Shifting: Decreasing Data Center Cooling Costs with Phase-Change Materials , 2017, IEEE Internet Computing.