An online electricity cost budgeting algorithm for maximizing green energy usage across data centers

Abstract

With the sky-rocketing development of Internet services, the power usage in data centers has been significantly increasing. This ever increasing energy consumption leads to negative environmental impact such as global warming. To reduce their carbon footprints, large Internet service operators begin to utilize green energy. Since green energy is currently more expensive than the traditional brown one, it is important for the operators to maximize the green energy usage subject to their desired long-term (e.g., a month) cost budget constraint. In this paper, we propose an online algorithm GreenBudget based on the Lyapunov optimization framework. We prove that our algorithm is able to achieve a delicate tradeoff between the green energy usage and the enforcement of the cost budget constraint, and a control parameter V is the knob to arbitrarily tune such a tradeoff. We evaluate GreenBudget utilizing real-life traces of user requests, cooling efficiency, electricity price and green energy availability. Experimental results demonstrate that under the same cost budget constraint, GreenBudget can increase the green energy usage by 11.55% compared with the state-of-the-art work, without incurring any performance violation of user requests.

Keywords

electricity cost budgeting / green energy / cooling efficiency / data center / Lyapunov optimization

References

[1] ChenY P, Alspaugh S, BorthakurD , KatzR. Energy efficiency for large-scale MapReduce workloads with significant interactive analysis. In: Proceedings of the 7th ACM European Conference on Computer Systems. 2012, 43–56

[2] ShenK, Shriraman A, DwarkadasS , ZhangX, ChenZ. Power containers: an OS facility for fine-grained power and energy management on multicore servers. In: Proceedings of the 18th International Conference on Architectural Support for Programming Languages and Operating Systems. 2013, 65–76

[3] MishraN, ZhangH Z, LaffertyJ D , HoffmannH. A probabilistic graphical model-based approach for minimizing energy under performance constraints. In: Proceedings of the 20th International Conference on Architectural Support for Programming Languages and Operating Systems. 2015, 267–281

[4] MankoffJ, Kravets R, BlevisE . Some computer science issues in creating a sustainable world. Computer, 2008, 41(8): 102–105

[5] ZhangY W, WangY F, WangX R. Greenware: greening cloud-scale data centers to maximize the use of renewable energy. In: Proceedings of the 12th ACM/IFIP/USENIX International Middleware Conference. 2011, 143–164

[6] QureshiA, WeberR, BalakrishnanH , GuttagJ, MaggsB. Cutting the electric bill for internet-scale systems. ACM SIGCOMM Computer Communication Review, 2009, 39(4): 123–134

[7] ZhangY W, WangY F, WangX R. Electricity bill capping for cloudscale data centers that impact the power markets. In: Proceedings of the 41st International Conference on Parallel Processing. 2012, 440–449

[8] ZhouZ, LiuF M, XuY, ZouR L, XuH, LuiJ C S, JinH. Carbonaware load balancing for geo-distributed cloud services. In: Proceedings of the 21st IEEE International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems. 2013, 232–241

[9] AbbasiZ, PoreM, GuptaS K. Online server and workload management for joint optimization of electricity cost and carbon footprint across data centers. In: Proceedings of the 28th IEEE International Parallel and Distributed Processing Symposium. 2014, 317–326

[10] NeelyM J. Stochastic network optimization with application to communication and queueing systems. Synthesis Lectures on Communication Networks, 2010, 3(1): 1–211

[11] RaoL, LiuX, XieL, Liu W Y. Minimizing electricity cost: optimization of distributed internet data centers in a multi-electricity-market environment. In: Proceedings of IEEE Conference on INFOCOM. 2010, 1–9

[12] XuH, FengC, LiB C. Temperature aware workload management in geo-distributed datacenters. ACM SIGMETRICS Performance Evaluation Review, 2015, 26(6): 1743–1753

[13] WangP J, RaoL, LiuX, Qi Y. D-Pro: dynamic data center operations with demand-responsive electricity prices in smart grid. IEEE Transactions on Smart Grid, 2012, 3(4): 1743–1754

[14] UrgaonkarR, Urgaonkar B, NeelyM J , SivasubramaniamA. Optimal power cost management using stored energy in data centers. In: Proceedings of the ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems. 2011, 221–232

[15] StewartC, ShenK. Some joules are more precious than others: managing renewable energy in the datacenter. In: Proceedings of theWorkshop on Power Aware Computing and Systems. 2009

[16] LiC, QounehA, LiT. iSwitch: coordinating and optimizing renewable energy powered server clusters. In: Proceedings of the 39th Annual International Symposium on Computer Architecture. 2012, 512–523

[17] GoiriÍ, KatsakW, LeK, NguyenT D, BianchiniR. Parasol and greenswitch: managing datacenters powered by renewable energy. In: Proceedings of the 18th International Conference on Architectural Support for Programming Languages and Operating Systems. 2013, 51–64

[18] DengW, LiuF M, JinH, Wu C. SmartDPSS: cost-minimizing multisource power supply for datacenters with arbitrary demand. In: Proceedings of the 33rd IEEE International Conference on Distributed Computing Systems. 2013, 420–429

[19] LiuZ H, LinM H, WiermanA, Low S H, AndrewL L . Greening geographical load balancing. In: Proceedings of the ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems. 2011, 233–244

[20] GaoP X, CurtisA R, WongB, Keshav S. It’s not easy being green. ACM SIGCOMM Computer Communication Review, 2012, 42(4): 211–222

[21] RenC G, WangD, UrgaonkarB, Sivasubramaniam A. Carbon-aware energy capacity planning for datacenters. In: Proceedings of the 20th IEEE International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems. 2012, 391–400

[22] RaoL, LiuX, IlicM, Liu J. MEC-IDC: joint load balancing and power control for distributed internet data centers. In: Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems. 2010, 188–197

[23] DouH, QiY, WangP J. Hybrid power control and electricity cost management for distributed internet data centers in cloud computing. In: Proceedings of the 10thWeb Information System and Application Conference. 2013, 394–399

[24] ElnozahyE M, Kistler M, RajamonyR . Energy-efficient server clusters. In: Falsafi B, Vijaykumar T N, eds. Power-Aware Computer Systems. Lecture Notes in Computer Science, Vol 2325. Berlin: Springer, 2002, 179–197

[25] RaoL, LiuX, IlicM D, Liu J. Distributed coordination of internet data centers under multiregional electricity markets. Proceedings of the IEEE, 2012, 100(1): 269–282

[26] LiuZ H, ChenY, BashC, Wierman A, GmachD , WangZ K, MarwahM, HyserC. Renewable and cooling aware workload management for sustainable data centers. ACM SIGMETRICS Performance Evaluation Review. 2012, 40(1): 175–186

[27] GeoffrionA M. Generalized benders decomposition. Journal of Optimization Theory and Applications, 1972, 10(4): 237–260

[28] LöfbergJ. Yalmip: a toolbox for modeling and optimization in matlab. In: Proceedings of the IEEE International Symposium on Computer Aided Control Systems Design. 2004, 284–289

RIGHTS & PERMISSIONS

2016 Higher Education Press and Springer-Verlag Berlin Heidelberg