A Truthful and Efficient Incentive Mechanism for Demand Response in Green Datacenters

Datacenter demand response is envisioned as a promising tool for mitigating operational stability issues faced by smart grids. It enables significant potentials in peak load reduction and facilitates the incorporation of distributed generation. Monetary refund from the smart grid can also alleviate the cloud's burden in escalating electricity cost. However, the current demand response paradigm is inefficient towards incentivizing a cloud service provider (CSP) that operates geo-distributed datacenters. To incentivize CSP participation, this work presents an auction mechanism that enables smart grids to voluntarily submit bids to the CSP to procure diverse amounts of demand response with different payments. To maximize the social welfare of the auction, the CSP that acts as the auctioneer needs to solve the winner determination problem at large-scale. By applying the proximal Jacobian alternating direction method of multipliers, we propose a distributed algorithm for each datacenter to solve a small-scale problem in a parallel fashion. Desirable properties of the proposed auction, such as social welfare maximization and truthfulness are achieved through Vickrey-Clarke-Groves (VCG) payment. Through extensive evaluations based on real datacenter workload traces and IEEE 14-bus test systems, we demonstrate that our incentive mechanism constitutes a win-win mechanism for both the geo-distributed cloud and the smart grid.

[1]  Shaolei Ren,et al.  An online incentive mechanism for emergency demand response in geo-distributed colocation data centers , 2016, e-Energy.

[2]  Zongpeng Li,et al.  Designing Truthful Spectrum Auctions for Multi-hop Secondary Networks , 2015, IEEE Transactions on Mobile Computing.

[3]  Bruce Hajek,et al.  VCG-Kelly Mechanisms for Allocation of Divisible Goods: Adapting VCG Mechanisms to One-Dimensional Signals , 2006 .

[4]  Bo Li,et al.  Harnessing renewable energy in cloud datacenters: opportunities and challenges , 2014, IEEE Network.

[5]  Baochun Li,et al.  Temperature Aware Workload Managementin Geo-Distributed Data Centers , 2013, IEEE Transactions on Parallel and Distributed Systems.

[6]  Hai Jin,et al.  When smart grid meets geo-distributed cloud: An auction approach to datacenter demand response , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

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

[8]  Guoqiang Hu,et al.  A Cooperative Demand Response Scheme Using Punishment Mechanism and Application to Industrial Refrigerated Warehouses , 2014, IEEE Transactions on Industrial Informatics.

[9]  John V. Guttag,et al.  Power-demand routing in massive geo-distributed systems , 2010 .

[10]  Lachlan L. H. Andrew,et al.  Optimal sleeping: models and experiments for energy-delay tradeoff , 2017 .

[11]  Zhu Han,et al.  How Geo-Distributed Data Centers Do Demand Response: A Game-Theoretic Approach , 2016, IEEE Transactions on Smart Grid.

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

[13]  Zhi Zhou,et al.  Bilateral Electricity Trade Between Smart Grids and Green Datacenters: Pricing Models and Performance Evaluation , 2016, IEEE Journal on Selected Areas in Communications.

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

[15]  Sangtae Ha,et al.  Interdatacenter Job Routing and Scheduling With Variable Costs and Deadlines , 2015, IEEE Transactions on Smart Grid.

[16]  Hai Jin,et al.  Carbon-Aware Online Control of Geo-Distributed Cloud Services , 2016, IEEE Transactions on Parallel and Distributed Systems.

[17]  S. Parsons,et al.  Everything you wanted to know about double auctions , but were afraid to ( bid or ) ask , 2006 .

[18]  Adam Wierman,et al.  This Paper Is Included in the Proceedings of the 11th Usenix Symposium on Networked Systems Design and Implementation (nsdi '14). Grass: Trimming Stragglers in Approximation Analytics Grass: Trimming Stragglers in Approximation Analytics , 2022 .

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

[20]  Bruce M. Maggs,et al.  The Internet at the Speed of Light , 2014, HotNets.

[21]  Wotao Yin,et al.  Parallel Multi-Block ADMM with o(1 / k) Convergence , 2013, Journal of Scientific Computing.

[22]  Tamer Basar,et al.  Efficient signal proportional allocation (ESPA) mechanisms: decentralized social welfare maximization for divisible resources , 2006, IEEE Journal on Selected Areas in Communications.

[23]  Guido Carpinelli,et al.  Short-term industrial load forecasting: A case study in an Italian factory , 2017, 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe).

[24]  H. T. Kung,et al.  BranchyNet: Fast inference via early exiting from deep neural networks , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).

[25]  Baochun Li,et al.  Joint request mapping and response routing for geo-distributed cloud services , 2013, 2013 Proceedings IEEE INFOCOM.

[26]  Bo Li,et al.  On arbitrating the power-performance tradeoff in SaaS clouds , 2013, 2013 Proceedings IEEE INFOCOM.

[27]  Bingsheng He,et al.  The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent , 2014, Mathematical Programming.

[28]  Zongpeng Li,et al.  An Online Auction Mechanism for Dynamic Virtual Cluster Provisioning in Geo-Distributed Clouds , 2017, IEEE Transactions on Parallel and Distributed Systems.

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

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

[31]  Zongpeng Li,et al.  Core-selecting combinatorial auction design for secondary spectrum markets , 2013, 2013 Proceedings IEEE INFOCOM.

[32]  Jie Li,et al.  Modeling Demand Response Capability by Internet Data Centers Processing Batch Computing Jobs , 2015, IEEE Transactions on Smart Grid.

[33]  Carlo Curino,et al.  Global Analytics in the Face of Bandwidth and Regulatory Constraints , 2015, NSDI.