Greening Multi-Tenant Data Center Demand Response

Data centers have become critical resources for emergency demand response (EDR). However, currently, data centers typically participate in EDR by turning on backup (diesel) generators, which are both expensive and environmentally unfriendly. In this paper, we focus on "greening" demand response in multi-tenant data centers by incentivizing tenants' load reduction and reducing on-site diesel generation. Our proposed mechanism, ColoEDR, which is based on parameterized supply function mechanism, provides provably near-optimal efficiency guarantees, both when tenants are price-taking and when they are price-anticipating.

[1]  Hamed Mohsenian Rad,et al.  Exploring smart grid and data center interactions for electric power load balancing , 2014, PERV.

[2]  S. Ong,et al.  Impacts of Commercial Electric Utility Rate Structure Elements on the Economics of Photovoltaic Systems , 2010 .

[3]  P. Klemperer,et al.  Supply Function Equilibria in Oligopoly under Uncertainty , 1989 .

[4]  Adam Wierman,et al.  Pricing data center demand response , 2014, SIGMETRICS '14.

[5]  R. Green,et al.  Competition in the British Electricity Spot Market , 1992, Journal of Political Economy.

[6]  Lachlan L. H. Andrew,et al.  Power-aware speed scaling in processor sharing systems: Optimality and robustness , 2012, Perform. Evaluation.

[7]  Hamed Mohsenian Rad,et al.  Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments , 2010, IEEE Transactions on Smart Grid.

[8]  Hamidreza Zareipour,et al.  Data centres in the ancillary services market , 2012, 2012 International Green Computing Conference (IGCC).

[9]  Shaolei Ren,et al.  A truthful incentive mechanism for emergency demand response in colocation data centers , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[10]  Girish Ghatikar,et al.  Demand Response Opportunities and Enabling Technologies for Data Centers: Findings From Field Studies , 2012 .

[11]  J. Pang,et al.  Oligopolistic Competition in Power Networks: A Conjectured Supply Function Approach , 2002, IEEE Power Engineering Review.

[12]  A. Wierman,et al.  Optimality, fairness, and robustness in speed scaling designs , 2010, SIGMETRICS '10.

[13]  Wolf-Dietrich Weber,et al.  Power provisioning for a warehouse-sized computer , 2007, ISCA '07.

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

[15]  Jie Liu,et al.  Underprovisioning backup power infrastructure for datacenters , 2014, ASPLOS.

[16]  Wei Zhang,et al.  Data center power control for frequency regulation , 2013, 2013 IEEE Power & Energy Society General Meeting.

[17]  Michael C. Caramanis,et al.  Real-time power control of data centers for providing Regulation Service , 2013, 52nd IEEE Conference on Decision and Control.

[18]  Adam Wierman,et al.  Data center demand response: Avoiding the coincident peak via workload shifting and local generation , 2013, Perform. Evaluation.

[19]  Luiz André Barroso,et al.  The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, Second Edition , 2013, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, Second Edition.

[20]  Austin Donnelly,et al.  Sierra: a power-proportional, distributed storage system , 2009 .

[21]  Lachlan L. H. Andrew,et al.  Power-Aware Speed Scaling in Processor Sharing Systems , 2009, IEEE INFOCOM 2009.

[22]  Luiz André Barroso,et al.  The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines , 2009, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines.

[23]  Guillaume Pierre,et al.  Wikipedia workload analysis for decentralized hosting , 2009, Comput. Networks.

[24]  Lachlan L. H. Andrew,et al.  Dynamic Right-Sizing for Power-Proportional Data Centers , 2011, IEEE/ACM Transactions on Networking.

[25]  R. F. Brown,et al.  PERFORMANCE EVALUATION , 2019, ISO 22301:2019 and business continuity management – Understand how to plan, implement and enhance a business continuity management system (BCMS).

[26]  Yang Li,et al.  Towards dynamic pricing-based collaborative optimizations for green data centers , 2013, 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW).

[27]  Hamed Mohsenian Rad,et al.  Data centers to offer ancillary services , 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).

[28]  R. Green,et al.  Increasing Competition in the British Electricity Spot Market , 1996 .

[29]  Adam Wierman,et al.  Greening multi-tenant data center demand response , 2015, Perform. Evaluation.

[30]  Mor Harchol-Balter,et al.  AutoScale: Dynamic, Robust Capacity Management for Multi-Tier Data Centers , 2012, TOCS.

[31]  R. Baldick,et al.  Theory and Application of Linear Supply Function Equilibrium in Electricity Markets , 2004 .

[32]  Jie Liu,et al.  Power Budgeting for Virtualized Data Centers , 2011, USENIX Annual Technical Conference.

[33]  Shaolei Ren,et al.  Colocation Demand Response: Why Do I Turn Off My Servers? , 2014, ICAC.

[34]  John N. Tsitsiklis,et al.  Parameterized Supply Function Bidding: Equilibrium and Efficiency , 2011, Oper. Res..

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

[36]  Edward J. Anderson,et al.  Finding Supply Function Equilibria with Asymmetric Firms , 2008, Oper. Res..

[37]  X. Vives.  Strategic Supply Function Competition with Private Information , 2008, SSRN Electronic Journal.