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.
. [J]. Frontiers of Computer Science, 2017, 11(4): 661-674.
Hui DOU, Yong QI. An online electricity cost budgeting algorithm for maximizing green energy usage across data centers. Front. Comput. Sci., 2017, 11(4): 661-674.
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 https://doi.org/10.1145/2168836.2168842
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 https://doi.org/10.1145/2451116.2451124
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 https://doi.org/10.1145/2694344.2694373
4
MankoffJ, Kravets R, BlevisE . Some computer science issues in creating a sustainable world. Computer, 2008, 41(8): 102–105 https://doi.org/10.1109/MC.2008.307
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 https://doi.org/10.1007/978-3-642-25821-3_8
6
QureshiA, WeberR, BalakrishnanH , GuttagJ, MaggsB. Cutting the electric bill for internet-scale systems. ACM SIGCOMM Computer Communication Review, 2009, 39(4): 123–134 https://doi.org/10.1145/1594977.1592584
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 https://doi.org/10.1109/IPDPS.2014.42
10
NeelyM J. Stochastic network optimization with application to communication and queueing systems. Synthesis Lectures on Communication Networks, 2010, 3(1): 1–211 https://doi.org/10.2200/S00271ED1V01Y201006CNT007
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 https://doi.org/10.1109/INFCOM.2010.5461933
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 https://doi.org/10.1109/TSG.2012.2211386
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 https://doi.org/10.1145/1993744.1993766
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 https://doi.org/10.1145/2366231.2337218
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 https://doi.org/10.1145/2451116.2451123
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 https://doi.org/10.1109/icdcs.2013.59
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 https://doi.org/10.1145/1993744.1993767
20
GaoP X, CurtisA R, WongB, Keshav S. It’s not easy being green. ACM SIGCOMM Computer Communication Review, 2012, 42(4): 211–222 https://doi.org/10.1145/2377677.2377719
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 https://doi.org/10.1109/mascots.2012.51
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 https://doi.org/10.1145/1795194.1795220
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 https://doi.org/10.1109/wisa.2013.81
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 https://doi.org/10.1007/3-540-36612-1_12
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 https://doi.org/10.1109/JPROC.2011.2161236
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 https://doi.org/10.1145/2318857.2254779
27
GeoffrionA M. Generalized benders decomposition. Journal of Optimization Theory and Applications, 1972, 10(4): 237–260 https://doi.org/10.1007/BF00934810
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 https://doi.org/10.1109/cacsd.2004.1393890
