An online incentive mechanism for emergency demand response in geo-distributed colocation data centers

Deferring batch workload in data centers is promising for demand response to enhance the efficiency and reliability of a power grid. Yet operators of multi-tenant colocation data centers still resort to eco-unfriendly diesel generators for demand response, because tenants lack incentives to defer their workloads. This work proposes an online auction mechanism for emergency demand response (EDR) in geo-distributed colocation data centers, which incentivizes tenants to delay and shuffle their workload across multiple data centers by providing monetary rewards. The mechanism, called BatchEDR, decides the tenants' workload deferment/reduction and diesel usage in each data center upon receiving an EDR signal, for cost minimization throughout the entire EDR event, considering that only a limited amount of batch workloads can be deferred throughout EDR as well as across multiple data centers. Without future information, BatchEDR achieves a good competitive ratio compared to an omniscient offline optimal algorithm, while ensuring truthfulness and individual rationality over the auction process. Trace-driven experiments show that BatchEDR outperforms the existing mechanisms and achieves good social cost.

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

[2]  Zongpeng Li,et al.  An online procurement auction for power demand response in storage-assisted smart grids , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

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

[4]  Amar Phanishayee,et al.  Safe and effective fine-grained TCP retransmissions for datacenter communication , 2009, SIGCOMM '09.

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

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

[7]  Hai Jin,et al.  Carbon-Aware Load Balancing for Geo-distributed Cloud Services , 2013, 2013 IEEE 21st International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems.

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

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

[11]  Moshe Babaioff,et al.  Auctions with online supply , 2015, Games Econ. Behav..

[12]  William Vickrey,et al.  Counterspeculation, Auctions, And Competitive Sealed Tenders , 1961 .

[13]  Adam Wierman,et al.  Greening Multi-Tenant Data Center Demand Response , 2015, PERV.

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

[15]  Zongpeng Li,et al.  An Online Auction Framework for Dynamic Resource Provisioning in Cloud Computing , 2016, IEEE/ACM Transactions on Networking.

[16]  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).

[17]  Stephen J. Wright Primal-Dual Interior-Point Methods , 1997, Other Titles in Applied Mathematics.

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

[19]  Niv Buchbinder,et al.  Online Job-Migration for Reducing the Electricity Bill in the Cloud , 2011, Networking.

[20]  Shaolei Ren,et al.  A First Look at Colocation Demand Response , 2014, PERV.

[21]  Randy H. Katz,et al.  Heterogeneity and dynamicity of clouds at scale: Google trace analysis , 2012, SoCC '12.

[22]  B. E. Eckbo,et al.  Appendix , 1826, Epilepsy Research.

[23]  Adam Wierman,et al.  Renewable and cooling aware workload management for sustainable data centers , 2012, SIGMETRICS '12.

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

[25]  Bruce M. Maggs,et al.  Cutting the electric bill for internet-scale systems , 2009, SIGCOMM '09.

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

[27]  George Kesidis,et al.  A Hierarchical Demand Response Framework for Data Center Power Cost Optimization under Real-World Electricity Pricing , 2014, 2014 IEEE 22nd International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems.

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

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