Contract-Based Demand Response Model for High Performance Computing Systems

High-performance computing (HPC) systems can consume substantial electricity during their operation, and consequently incur significant energy cost. Demand response is a scheme adopted by energy consumers to reduce energy use during the high load periods upon request from the power grid. In this paper, we propose a demand response model to allow both HPC operators and HPC users to jointly reduce the energy consumption. We apply the contract theory, originated from economics for studying the contractual arrangements among economic actors, and design a reward mechanism to ensure participation of both HPC operators and HPC users in the demand response program. We perform both analytical and simulation studies of our proposed approach. Our analyses show that our contract-based demand response model is both feasible (in ensuring both individual rationality and incentive compatibility) and optimal. The trace-based simulation demonstrates the practicality of the proposed approach.

[1]  Xinbing Wang,et al.  Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach , 2011, IEEE Journal on Selected Areas in Communications.

[2]  Michael Dumbser,et al.  Seismic wave field modelling using high performance computing , 2008 .

[3]  Al Geist,et al.  A survey of high-performance computing scaling challenges , 2017, Int. J. High Perform. Comput. Appl..

[4]  Hao Wang,et al.  Proactive Demand Response for Data Centers: A Win-Win Solution , 2015, IEEE Transactions on Smart Grid.

[5]  Torsten Wilde,et al.  A Case Study of Energy Aware Scheduling on SuperMUC , 2014, ISC.

[6]  M. J. D. Powell,et al.  A fast algorithm for nonlinearly constrained optimization calculations , 1978 .

[7]  Mingyan Liu,et al.  Profit Incentive in Trading Nonexclusive Access on a Secondary Spectrum Market Through Contract Design , 2014, IEEE/ACM Transactions on Networking.

[8]  Jason Liu,et al.  An Energy Efficient Demand-Response Model for High Performance Computing Systems , 2017, 2017 IEEE 25th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS).

[9]  Xu Yang,et al.  Integrating dynamic pricing of electricity into energy aware scheduling for HPC systems , 2013, 2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

[10]  Zhiling Lan,et al.  Reducing Energy Costs for IBM Blue Gene/P via Power-Aware Job Scheduling , 2013, JSSPP.

[11]  Abbas Jamalipour,et al.  Cooperative communication and relay selection under asymmetric information , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[12]  Zancanella Paolo,et al.  Demand response status in EU Member States , 2016 .

[13]  K. J. Ray Liu,et al.  A contract-based approach for ancillary services in V2G networks: Optimality and learning , 2013, 2013 Proceedings IEEE INFOCOM.

[14]  Roberto Guerrieri,et al.  Power-Aware Job Scheduling on Heterogeneous Multicore Architectures , 2015, IEEE Transactions on Parallel and Distributed Systems.

[15]  Jean C. Walrand,et al.  Motivating Smartphone Collaboration in Data Acquisition and Distributed Computing , 2014, IEEE Transactions on Mobile Computing.

[16]  Prakash Murali,et al.  Metascheduling of HPC Jobs in Day-Ahead Electricity Markets , 2018, IEEE Transactions on Parallel and Distributed Systems.

[17]  N. Rubén,et al.  The Market for Lemons , 2011 .

[18]  Ulrich Rüde,et al.  Lehrstuhl Für Informatik 10 (systemsimulation) Walberla: Hpc Software Design for Computational Engineering Simulations Walberla: Hpc Software Design for Computational Engineering Simulations , 2010 .

[19]  Rob Simmonds,et al.  Electrical cost savings and clean energy usage potential for HPC workloads , 2011, Proceedings of the 2011 IEEE International Symposium on Sustainable Systems and Technology.

[20]  Özalp Babaoglu,et al.  Predicting system-level power for a hybrid supercomputer , 2016, 2016 International Conference on High Performance Computing & Simulation (HPCS).

[21]  Cong Liu,et al.  Optimal Contract Design for Cooperative Relay Incentive Mechanism under Moral Hazard , 2015, J. Electr. Comput. Eng..

[22]  Jason Liu,et al.  Enabling Demand Response for HPC Systems through Power Capping and Node Scaling , 2018, 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS).

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

[24]  Walid Saad,et al.  Contract-Based Incentive Mechanisms for Device-to-Device Communications in Cellular Networks , 2015, IEEE Journal on Selected Areas in Communications.

[25]  Jean-Jacques Laffont,et al.  A complete solution to a class of principal-agent problems with an application to the control of a self-managed firm , 1984 .

[26]  J. Mirrlees An Exploration in the Theory of Optimum Income Taxation an Exploration in the Theory of Optimum Income Taxation L Y 2 , 2022 .

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

[28]  Xingfu Wu,et al.  Using Performance-Power Modeling to Improve Energy Efficiency of HPC Applications , 2016, Computer.

[29]  Zhu Han,et al.  A Contract Game for Direct Energy Trading in Smart Grid , 2018, IEEE Transactions on Smart Grid.

[30]  Xin Yuan,et al.  A comparative study of high-performance computing on the cloud , 2013, HPDC.

[31]  V. Springel The Cosmological simulation code GADGET-2 , 2005, astro-ph/0505010.

[32]  Panayotis G. Cottis,et al.  A Contract-Based Spectrum Trading Scheme for Cognitive Radio Networks Enabling Hybrid Access , 2015, IEEE Access.

[33]  Stefano de Gironcoli,et al.  QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials , 2009, Journal of physics. Condensed matter : an Institute of Physics journal.

[34]  Zhu Han,et al.  Game-theoretic resource allocation methods for device-to-device communication , 2014, IEEE Wireless Communications.

[35]  Xue Liu,et al.  Comprehensive understanding of operation cost reduction using energy storage for IDCs , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[36]  Ajay Mohindra,et al.  Efficient Contracting in Cloud Service Markets with Asymmetric Information - A Screening Approach , 2011, 2011 IEEE 13th Conference on Commerce and Enterprise Computing.

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

[38]  Christos G. Cassandras,et al.  Provision of Regulation Service by Smart Buildings , 2016, IEEE Transactions on Smart Grid.

[39]  Hoay Beng Gooi,et al.  Demand response program in Singapore’s wholesale electricity market☆ , 2017 .