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The Journal of Supercomputing

Erschienen in:

01.03.2015

Energy-efficient allocation of computing node slots in HPC clusters through parameter learning and hybrid genetic fuzzy system modeling

verfasst von: Alberto Cocaña-Fernández, Jose Ranilla, Luciano Sánchez

Erschienen in: The Journal of Supercomputing | Ausgabe 3/2015

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Abstract

Decision-making mechanisms for online allocation of computer node slots in HPC clusters are commonly based on simple knowledge-based systems comprised of individual sets of if–then rules. In contrast with previous works where these rules were designed using expert knowledge, two different types of evolutionary learning algorithms are compared in this paper. In the first case, some of the numerical parameters defining a human-designed knowledge base are tuned. In the second case, a genetic fuzzy system evolves a partial rule set that, after being combined with some expert rules, conforms the most appropriate knowledge base for a given load scenario. In both cases, the proposed approaches optimize the quality of service and the number of node reconfigurations along with the energy consumption. An experimental study has been made using actual workloads from the Scientific Modeling Cluster at Oviedo University, and statistical evidence was found supporting the adoption of the new learning system.

Vorheriger Artikel A novel metaheuristic algorithm and utility function for QoS based scheduling in user-centric grid systems

Nächster Artikel Erratum to: Supercomputing and grid computing on the verification of covering arrays

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June 2014 | TOP500 Supercomputer Sites, http://www.top500.org/lists/2014/06/.

VMware Distributed Power Management Concepts and Use. http://www.vmware.com/files/pdf/Distributed-Power-Management-vSphere.

Citrix XenServer—Efficient Server Virtualization Software. http://www.citrix.com/products/xenserver/overview.html.

Alonso P, Badia RM, Labarta J, Barreda M, Dolz MF, Mayo R, Quintana-Orti ES, Reyes R (2012) Tools for power-energy modelling and analysis of parallel scientific applications. In: 2012 41st international conference on parallel processing. IEEE, New Jersey, pp 420–429

Alvarruiz F, de Alfonso C, Caballer M, Hernández V (2012) An energy manager for high performance computer clusters. In: 2012 IEEE 10th international symposium on parallel and distributed processing with applications. IEEE, New Jersey, pp 231–238

Bash C, Forman G (2007) Cool job allocation: measuring the power savings of placing jobs at cooling-efficient locations in the data center. USENIX Association, Berkeley, p 29

Berral JL, Goiri Í, Nou R, Julià F, Guitart J, Gavaldà R, Torres J (2010) Towards energy-aware scheduling in data centers using machine learning. In: Proceedings of the 1st international conference on energy-efficient computing and networking—e-energy ’10. ACM Press, New York, p 215

Buyya R, Jin H, Cortes R (2002) Cluster computing. Future Gener Comput Syst 18(3):v–viii

Cheng Y, Zeng Y (2011) Automatic energy status controlling with dynamic voltage scaling in power-aware high performance computing cluster. In: 2011 12th international conference on parallel and distributed computing, applications and technologies. IEEE, New York, pp 412–416

Chetsa GLT, Lefrvre L, Pierson JM, Stolf P, Da Costa G (2012) A runtime framework for energy efficient HPC systems without a priori knowledge of applications. In: 2012 IEEE 18th international conference on parallel and distributed systems. IEEE, New York, pp 660–667

Cocaña Fernández A, Ranilla J, Sánchez L (2014) Energy-efficient allocation of computing node slots in hpc clusters through evolutionary multi-criteria decision making. In: Proceedings of the 14th international conference on computational and mathematical methods in science and engineering, CMMSE 2014, pp 318–330

Das R, Kephart JO, Lefurgy C, Tesauro G, Levine DW, Chan H (2008) Autonomic multi-agent management of power and performance in data centers, pp 107–114

10.

Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRef

11.

Dolz MF, Fernández JC, Iserte S, Mayo R, Quintana-Ortí ES, Cotallo ME, Díaz G (2011) Energy saving cluster experience in CETA-CIEMAT. In: 5th Iberian GRID infrastructure conference, Santander

12.

Matthew E, Mike A, Felipe FC, da Fonseca M, Para GV, Michael S (2011) Smarter data centers: achieving greater efficiency. Technical report, IBM Redpaper

13.

EIA. Electric Power Monthly—Energy Information Administration

14.

Elnozahy EN, Kistler M, Rajamony R (2002) Energy-efficient server clusters, pp 179–197

15.

Emerson Network Power (2009) Energy logic: reducing data center energy consumption by creating savings that cascade across systems. Technical report

16.

Eurostat (2013) Electricity and natural gas price statistics—statistics explained

17.

Freeh VW, Lowenthal DK (2005) Using multiple energy gears in MPI programs on a power-scalable cluster. In: Proceedings of the tenth ACM SIGPLAN symposium on principles and practice of parallel programming—PPoPP ’05. ACM Press, New York, p 164

18.

Freeh VW, Lowenthal DK, Pan F, Kappiah N, Springer R, Rountree BL, Femal ME (2007) Analyzing the energy–time trade-off in high-performance computing applications. IEEE Trans Parallel Distrib Syst 18(6):835–848CrossRef

19.

Garcia DF, Entrialgo J, Garcia J, Garcia M (2010) A self-managing strategy for balancing response time and power consumption in heterogeneous server clusters. In: 2010 international conference on electronics and information engineering, vol 1. IEEE, New York, pp V1–537-V1-541

20.

Gartner (2007) Gartner estimates ICT industry accounts for 2 percent of global CO2 emissions

21.

Ge R, Feng X, Feng W, Cameron KW (2007) CPU MISER: a performance-directed, run-time system for power-aware clusters. In: 2007 international conference on parallel processing (ICPP 2007). IEEE, New York, pp 18–18

22.

Ruud H (2011) The blue gene/Q compute chip. Technical report, IBM Corporation

23.

Hsu CH, Feng W (2005) A power-aware run-time system for high-performance computing. In: ACM/IEEE SC 2005 conference (SC’05). IEEE, New York, pp 1–1

24.

Hsu CH, Kremer U (2003) The design, implementation, and evaluation of a compiler algorithm for CPU energy reduction. ACM SIGPLAN Not 38(5):38CrossRef

25.

Huang S, Feng W (2009) Energy-efficient cluster computing via accurate workload characterization. In: 2009 9th IEEE/ACM international symposium on cluster computing and the grid. IEEE, New York, pp 68–75

26.

IBM Systems and Technology Group (2011) IBM system blue gene/Q—DCD12345USEN.pdf. Technical report, IBM, Somers, NY

27.

Ishibuchi H, Nakashima T, Nii M (2004) Classification and modeling with linguistic information granules: advanced approaches to linguistic data mining. Adv Inf Process

28.

Lang W, Patel JM, Naughton JF (2010) On energy management, load balancing and replication. ACM SIGMOD Record 38(4):35CrossRef

29.

Li D, Nikolopoulos DS, Cameron K, de Supinski BR, Schulz M (2010) Power-aware MPI task aggregation prediction for high-end computing systems. In: 2010 IEEE international symposium on parallel & distributed processing (IPDPS). IEEE, New York, pp 1–12

30.

Lim M, Freeh V, Lowenthal D (2006) Adaptive, transparent frequency and voltage scaling of communication phases in MPI programs. In: ACM/IEEE SC 2006 conference (SC’06). IEEE, New York, p 14

31.

Llamas RM, Garcia DF, Entrialgo J (2012) A technique for self-optimizing scalable and dependable server clusters under QoS constraints. In: 2012 IEEE 11th international symposium on network computing and applications. IEEE, New York, pp 61–66

32.

Pinheiro E, Bianchini R, Carrera EV, Heath T (2001) Load balancing and unbalancing for power and performance in cluster-based systems. In: Workshop on compilers and operating systems for low power, vol 180. Barcelona, Spain, pp 182–195

33.

Schubert S, Kostic D, Zwaenepoel W, Shin KG (2012) Profiling software for energy consumption. In: 2012 IEEE international conference on green computing and communications. IEEE, New York, pp 515–522

34.

Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern, SMC-15(1):116–132

35.

Tang G, Gupta Q, Varsamopoulos SKS (2008) Energy-efficient thermal-aware task scheduling for homogeneous high-performance computing data centers: a cyber-physical approach. IEEE Trans Parallel Distrib Syst 19(11):1458–1472

36.

Unit The Economist Intelligence (2007) IT and the environment A new item on the CIOGs agenda? Technical report, The Economist

37.

U.S. Environmental Protection Agency (2007) Report to congress on server and data center energy efficiency public law. Technical report, ENERGY STAR Program, pp 109–431

38.

Lassonde W, Khan SU, Min-Allah N, Madani SA, Li J, Zhang L, Wang L, Ghani N, Kolodziej J, Li H, Zomaya AY, Xu CZ, Balaji P, Vishnu A, Pinel F, Pecero JE, Kliazovich D, Bouvry P (2011) An overview of energy efficiency techniques in cluster computing systems. Cluster Comput 16(1):3–15

39.

Xian C, Lu YH, Li Z (2007) A programming environment with runtime energy characterization for energy-aware applications. In: Proceedings of the 2007 international symposium on low power electronics and design—ISLPED ’07. ACM Press, New York, pp 141–146

40.

Xue Z, Dong X, Ma S, Fan S, Mei Y (2007) An energy-efficient management mechanism for large-scale server clusters. In: The 2nd IEEE Asia-Pacific service computing conference (APSCC 2007). IEEE, New York, pp 509–516

41.

Yeo CS, Buyya R, Pourreza H, Rasit Eskicioglu M, Graham P, Pourreza P, Sommers F (2006) Cluster computing: high-performance, high-availability, and high-throughput processing on a network of computers. In: Zomaya AY (ed) Handbook of nature-inspired and innovative computing. Springer, Berlin, pp 521–551

42.

Zong Z, Nijim M, Manzanares A, Qin X (2007) Energy efficient scheduling for parallel applications on mobile clusters. Cluster Comput 11(1):91–113CrossRef

43.

Zong Z, Ruan X, Manzanares A, Bellam K, Qin X (2010) Improving energy-efficiency of computational grids via scheduling. In: Antonopoulos N, Exarchakos G, Li M, Liotta A (eds) Handbook of research on P2P and grid systems for service-oriented computing, chap. 22. IGI Global, Hershey

Titel: Energy-efficient allocation of computing node slots in HPC clusters through parameter learning and hybrid genetic fuzzy system modeling
verfasst von: Alberto Cocaña-Fernández
Jose Ranilla
Luciano Sánchez
Publikationsdatum: 01.03.2015
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 3/2015
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-014-1320-9

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