Ensuring renewable energy utilization with quality of service guarantee for energy-efficient data center operations

Abstract The reduction of greenhouse emissions is becoming a major goal of energy-intensive industries, such as data centers, and there have been significant efforts to achieve sustainable operations by meeting electricity consumption using renewable energy generations. Specifically, it has been a common practice for data centers to use renewable energy via on-site solar power generation to directly offset electricity consumption by renewable energy to contribute to environmental sustainability. However, the introduction of intermittent and non-dispatchable renewable energy generations for powering data centers that generally host time-varying workloads presents a significant challenge, and thus, this study mainly focuses on how to improve renewable energy utilization for data center operations considering the integration of co-located solar power generation and battery energy storage. The main objective is to develop a mathematical optimization model for energy-efficient and sustainable data center operations to minimize energy cost while ensuring the desired level of renewable energy utilization and the required quality of service guarantee. In particular, this study proposes a two-stage stochastic program integrated with an expected-value constraint and a chance constraint, and an integer programming and sampling-based approach are adopted to solve the problem to investigate optimal data center operations. The comprehensive numerical experiments are conducted to evaluate the proposed model compared with benchmark models for various parameter settings, and the results show that the proposed model can be successfully implemented to enable data centers to achieve the desired renewable energy utilization while improving energy efficiency.

[1]  Wen-De Zhong,et al.  Demand Response in Data Centers Through Energy-Efficient Scheduling and Simple Incentivization , 2017, IEEE Systems Journal.

[2]  Tajana Simunic,et al.  Renewable Energy Prediction for Improved Utilization and Efficiency in Datacenters and Backbone Networks , 2016, Computational Sustainability.

[3]  Tajana Rosing,et al.  Utilizing green energy prediction to schedule mixed batch and service jobs in data centers , 2011, OPSR.

[4]  Jean-Marc Nicod,et al.  Negotiation game for joint IT and energy management in green datacenters , 2020, Future Gener. Comput. Syst..

[5]  Depei Qian,et al.  Managing Green Datacenters Powered by Hybrid Renewable Energy Systems , 2014, ICAC.

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

[7]  Jordi Torres,et al.  GreenSlot: Scheduling energy consumption in green datacenters , 2011, 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

[8]  Victor M. Zavala,et al.  Data Centers as Dispatchable Loads to Harness Stranded Power , 2016, IEEE Transactions on Sustainable Energy.

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

[10]  Yuguang Fang,et al.  Energy and Network Aware Workload Management for Sustainable Data Centers with Thermal Storage , 2014, IEEE Transactions on Parallel and Distributed Systems.

[11]  Rajkumar Buyya,et al.  Short-term prediction model to maximize renewable energy usage in cloud data centers , 2018 .

[12]  Yi Liu,et al.  Coordinating workload balancing and power switching in renewable energy powered data center , 2015, Frontiers of Computer Science.

[13]  Jose Renau,et al.  ReRack: power simulation for data centers with renewable energy generation , 2011, PERV.

[14]  Natarajan Gautam,et al.  Demand Response in Data Centers: Integration of Server Provisioning and Power Procurement , 2019, IEEE Transactions on Smart Grid.

[15]  Georges Da Costa,et al.  Green IT scheduling for data center powered with renewable energy , 2018, Future Gener. Comput. Syst..

[16]  Natarajan Gautam,et al.  Guaranteeing performance based on time-stability for energy-efficient data centers , 2016 .

[17]  Paul Renaud-Goud,et al.  IT Optimization for Datacenters Under Renewable Power Constraint , 2018, Euro-Par.

[18]  Tao Zhang,et al.  A multi-objective co-evolutionary algorithm for energy-efficient scheduling on a green data center , 2016, Comput. Oper. Res..

[19]  Anand Sivasubramaniam,et al.  Optimal power cost management using stored energy in data centers , 2011, PERV.

[20]  Zhi Chen,et al.  Electric Demand Response Management for Distributed Large-Scale Internet Data Centers , 2014, IEEE Transactions on Smart Grid.

[21]  Sergio Nesmachnow,et al.  Holistic multiobjective planning of datacenters powered by renewable energy , 2015, Cluster Computing.

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

[23]  Rajkumar Buyya,et al.  Renewable-aware geographical load balancing of web applications for sustainable data centers , 2017, J. Netw. Comput. Appl..

[24]  Natarajan Gautam,et al.  Meeting Inelastic Demand in Systems With Storage and Renewable Sources , 2017, IEEE Trans. Smart Grid.

[25]  Ariel Oleksiak,et al.  Reducing energy costs in data centres using renewable energy sources and energy storage , 2016, E2DC@e-Energy.

[26]  Jean-Marc Nicod,et al.  DATAZERO: Datacenter With Zero Emission and Robust Management Using Renewable Energy , 2019, IEEE Access.

[27]  A. Shapiro,et al.  Solving Chance-Constrained Stochastic Programs via Sampling and Integer Programming , 2008 .

[28]  Natarajan Gautam,et al.  Optimal Day-Ahead Power Procurement With Renewable Energy and Demand Response , 2017, IEEE Transactions on Power Systems.