This article presents the evaluation of multicriteria planning heuristics for demand response in datacenters and supercomputing facilities. This is a relevant problem for science nowadays, when the growing application of cutting-edge technologies (numerical methods, big data processing, artificial intelligence, smart systems, etc.) has raised the energy demands in datacenters. The proposed approach involves a negotiation mechanism for colocation datacenters, where the datacenter operator agrees prices and quality of service with a group of tenants. Twelve different multicriteria heuristics are proposed for planning using both local and global information at tenants and datacenter operator levels. The proposed approach is evaluated applying simulations over realistic scenarios considering different tenant sizes and heterogeneity levels that model different business models for datacenters. Several metrics are computed and Pareto analysis is provided. The main results indicate that accurate trade-off values between the problem objectives are obtained, offering different options for decision making. The proposed approach provides a useful and applicable method for demand response planning in modern datacenters.
KhanSZomayaA (eds.) Handbook on data centers. New York: Springer, 2015.
3.
Armenta-CanoFTchernykhACortes-MendozaJ, et al. Min c: Heterogeneous concentration policy for energy-aware scheduling of jobs with resource contention. Program Comput Software2017; 43: 204–215.
4.
DorronsoroBNesmachnowSTaheriJ, et al. A hierarchical approach for energy-efficient scheduling of large workloads in multicore distributed systems. Sustain Comput Inform Syst2014; 4: 252–261.
5.
IturriagaSNesmachnowS.Scheduling energy efficient data centers using renewable energy. Electronics2016; 5: 71.
6.
NesmachnowSPerfumoCGoiriÍ.Holistic multiobjective planning of datacenters powered by renewable energy. Cluster Comput2015; 18: 1379–1397.
7.
LiuZWiermanAChenY, et al. Data center demand response: Avoiding the coincident peak via workload shifting and local generation. Perform Eval2013; 70: 770–791.
8.
OoTZTranNRenS, et al. A Survey on coordinated power management in multi-tenant data centers. Springer, 2018.
9.
DelforgePWhitneyJ.Scaling up energy efficiency across the data center industry: Evaluating key drivers and barriers. Technical Report 14-08-a, Natural Resources Defense Council and Anthesis, 2014.
10.
MallaSChristensenK. A survey on power management techniques for oversubscription of multi-tenant data centers. ACM Comput Surv2019; 52(1): 1–31.
11.
ChenNRenXRenS, et al. Greening multi-tenant data center demand response. Perform Eval2015; 91: 229–254.
12.
NesmachnowSDorronsoroBPeceroJ, et al. Energy-aware scheduling on multicore heterogeneous grid computing systems. J Grid Comput2013; 11: 653–680.
13.
IturriagaSDorronsoroBNesmachnowS.Multiobjective evolutionary algorithms for energy and service level scheduling in a federation of distributed datacenters. Int Trans Oper Res2016; 24: 199–228.
14.
MurañaJNesmachnowSIturriagaS, et al. Power consumption characterization of synthetic benchmarks in multicores. In: High Performance Computing. Communications in Computer and Information Science. Springer. 2017, pp.21–37.
15.
MurañaJNesmachnowSArmentaF, et al. Characterization, modeling and scheduling of power consumption of scientific computing applications in multicores. Cluster Comput2019; 22: 839–859.
16.
MurañaJNesmachnowSIturriagaS, et al. Negotiation approach for the participation of datacenters and supercomputing facilities in smart electricity markets. Program Comput Software2020; 46: 636–651.
17.
DemskiJ.Joint costs and cost allocation. In: Managerial uses of accounting information. Boston: Springer, 1997, pp.169–200.
18.
KlempererPMeyerM.Supply function equilibria in oligopoly under uncertainty. Econometrica1989; 57: 1243–1277.
19.
TijsSDriessenT.Game theory and cost allocation problems. Manag Sci1986; 32: 1015–1028.
20.
JohariRTsitsiklisJ.Parameterized supply function bidding: Equilibrium and efficiency. Oper Res2011; 59: 1079–1089.
21.
TranNHDoCRenS, et al. Incentive mechanisms for economic and emergency demand responses of colocation datacenters. IEEE J Sel Area Comm2015; 33: 2892–2905.
22.
WangYZhangFChiC, et al. A market-oriented incentive mechanism for emergency demand response in colocation data centers. Sustain Comput Inform Syst2019; 22: 13–25.
23.
TranNOoTRenS, et al. Reward-to-reduce: An incentive mechanism for economic demand response of colocation datacenters. IEEE J Sel Area Comm2016; 34: 3941–3953.
24.
NesmachnowSIturriagaS. Cluster-UY: Scientific HPC in Uruguay. In: International supercomputing in México. Springer, pp. 1–15.
25.
NesmachnowS.An overview of metaheuristics: Accurate and efficient methods for optimisation. IJMHeur2014; 3: 320–347.
26.
MamunANarayananIWangD, et al. Multi-objective optimization of demand response in a datacenter with lithium-ion battery storage. J Energ Stor2016; 7: 258–269.
27.
SadhukhanASivasubramaniS. Multi-objective load scheduling in a smart grid environment. In: 20th National Power Systems Conference. IEEE.
28.
JeyaraniRVasanthRNagaveniN. Design and implementation of an efficient two-level scheduler for cloud computing environment. In: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing. IEEE.
29.
TchernykhALozanoLSchwiegelshohnU, et al. Online bi-objective scheduling for IaaS clouds ensuring quality of service. J Grid Comput2015; 14: 5–22.
30.
DaghighiAKavousiM. Scheduling for data centers with multi-level data locality. In: Iranian Conference on Electrical Engineering. IEEE.
31.
JainAKumarR. A multi stage load balancing technique for cloud environment. In: 2016 International Conference on Information Communication and Embedded Systems. IEEE.
32.
KliazovichDBouvryPKhanSU.DENS: Data center energy-efficient network-aware scheduling. Cluster Comput2011; 16: 65–75.
33.
HernándezSFabraJÁlvarezP, et al. Simulation and realistic workloads to support the meta-scheduling of scientific workflows. In: Simulation and Modeling Methodologies, Technologies and Applications. Cham, Springer, pp.155–167.
34.
BakSKrystekMKurowskiK, et al. GSSIM–a tool for distributed computing experiments. Sci Program2011; 19: 231–251.
35.
CalheirosRRanjanRBeloglazovA, et al. CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software Pract Ex2011; 41: 23–50.
36.
KurowskiKOleksiakAPiatekW, et al. DCworms–a tool for simulation of energy efficiency in distributed computing infrastructures. Simulat Model Pract Theor2013; 39: 135–151.
37.
DurilloJNebroA.jmetal: A java framework for multi-objective optimization. Adv Eng Software2011; 42: 760–771.
RepkoA.Interdisciplinary research: process and theory. Los Angeles: SAGE Publishing, 2008.
40.
GammaEHelmRJohnsonR, et al. Design patterns. Elements of reusable object-oriented software. Boston: Addison-Wesley, 1995.
41.
ShujaJBilalKMadaniSA, et al. Data center energy efficient resource scheduling. Cluster Comput2014; 17: 1265–1277.
42.
IturriagaSGarcíaSNesmachnowS. An empirical study of the robustness of energy-aware schedulers for high performance computing systems under uncertainty. In: High Performance Computing. Springer, pp.143–157.
43.
AnghelAVasilescuLMarianiG, et al. An instrumentation approach for hardware-agnostic software characterization. Int J Parallel Program2016; 44: 924–948.
44.
LuoPLüKShiZ.A revisit of fast greedy heuristics for mapping a class of independent tasks onto heterogeneous computing systems. J Parallel Distr Comput2007; 67: 695–714.
45.
CoelloCVan VeldhuizenDLamontG.Evolutionary algorithms for solving multi-objective problems. New York: Kluwer Academic, 2002.
46.
Montes de Oca, MurañaJMonzonP, et al. Demand response program for supercomputing and datacenters providing ancillary services in the electricity market. In: PES Transmission and Distribution Conference and Exposition – Latin American. IEEE, pp.1–5.
