Incentive Mechanisms for Economic and Emergency Demand Responses of Colocation Datacenters

Demand response programs have been considered critical for power grid reliability and efficiency. Especially, the demand response of datacenters has recently received encouraging efforts due to huge demands and flexible power control knobs of datacenters. However, most current efforts focus on owner-operated datacenters, omitting another critical segment of datacenter business: multitenant colocation. In colocation datacenters, while there exist multiple tenants who manage their own servers, the colocation operator only provides facilities such as cooling, reliable power, and network connectivity. Therefore, colocation has a unique feature that challenges any attempts to design a demand response program: uncoordinated power management among tenants. To tackle this challenge, two incentive mechanisms are proposed to coordinate tenant power consumption for demand response under two different scenarios. First, in the case of economic demand response where the operator can adjust an elastic energy reduction target, we show that there is an interaction between the operator and tenant strategies, where each side maximizes its own benefit. Hence, we apply a two-stage Stackelberg game to analyze this scenario and derive this game's equilibria. However, computing these equilibria can be intractable with exhaustive search; therefore, we propose an algorithm to find the Stackelberg equilibria with linear complexity. Second, in the case of emergency demand response where a fixed energy reduction target must be fulfilled, we devise two incentive schemes with the distributed algorithms that can achieve the same optimal social cost. While the first algorithm is based on the dual-decomposition method that is suitable for nonstrategic tenants, the second one is designed for strategic tenants to achieve a unique Nash equilibrium of a bidding game. Finally, trace-based simulations are also provided to illustrate the efficacy of our proposed incentive schemes.

[1]  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).

[2]  Zhu Han,et al.  Demand Response of Data Centers: A Real-time Pricing Game Between Utilities in Smart Grid , 2014, Feedback Computing.

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

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

[5]  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.

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

[7]  A. Robert Calderbank,et al.  Layering as Optimization Decomposition: A Mathematical Theory of Network Architectures , 2007, Proceedings of the IEEE.

[8]  Na Li,et al.  Two Market Models for Demand Response in Power Networks , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[9]  Robert H. Bier,et al.  LEADERSHIP IN ENERGY AND ENVIRONMENTAL DESIGN , 2007 .

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

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

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

[13]  Adam Wierman,et al.  Data center demand response: avoiding the coincident peak via workload shifting and local generation , 2013, SIGMETRICS '13.

[14]  Zhu Han,et al.  Incentive Mechanism for Demand Side Management in Smart Grid Using Auction , 2014, IEEE Transactions on Smart Grid.

[15]  Moghaddam Rad Farham,et al.  Leadership in Energy and Environmental Design , 2014 .

[16]  Mor Harchol-Balter,et al.  Optimal power allocation in server farms , 2009, SIGMETRICS '09.

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

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

[19]  Lachlan L. H. Andrew,et al.  Geographical load balancing with renewables , 2011, PERV.

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

[21]  Vincent W. S. Wong,et al.  Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid , 2010, IEEE Transactions on Smart Grid.

[22]  Na Li,et al.  Optimal demand response based on utility maximization in power networks , 2011, 2011 IEEE Power and Energy Society General Meeting.

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

[24]  Lachlan L. H. Andrew,et al.  Greening Geographical Load Balancing , 2015, IEEE/ACM Transactions on Networking.

[25]  L H AndrewLachlan,et al.  Dynamic right-sizing for power-proportional data centers , 2013 .

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

[27]  Xue Liu,et al.  D-Pro: Dynamic Data Center Operations With Demand-Responsive Electricity Prices in Smart Grid , 2012, IEEE Transactions on Smart Grid.

[28]  John N. Tsitsiklis,et al.  Parallel and distributed computation , 1989 .

[29]  Hamed Mohsenian Rad,et al.  Profit maximization and power management of green data centers supporting multiple slas , 2013, 2013 International Conference on Computing, Networking and Communications (ICNC).