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Power reduction in HPC data centers: a joint server placement and chassis consolidation approach

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Abstract

Size and number of high-performance data centers are rapidly growing all around the world in recent years. The growth in the leakage power consumption of servers along with its exponential dependence on the ever increasing process variation in nanometer technologies has made it inevitable to move toward variation-aware power reduction strategies in data centers. In this paper, we address the problem of joint server placement and chassis consolidation to minimize power consumption of high-performance computing data centers under process variation. To this end, we introduce two variation-aware server placement heuristics as well as an integer linear programming (ILP)-based server placement method to find the best location of each server in the data center based on its power consumption and the data center heat recirculation model. We then incorporate a novel ILP-based variation-aware chassis consolidation technique to find the optimum task assignment solution under the obtained server placement approach to minimize total power consumption. Experimental results show that by applying the proposed joint variation-aware server placement and chassis consolidation techniques, up to 14.6 % improvement can be obtained at common data center utilization rates compared to state-of-the-art variation-unaware approaches.

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References

  1. Sawyer R (2004) Calculating total power requirements for data centers. White paper

  2. Beloglazov A, Buyya R (2013) Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Trans Parallel Distrib Syst 24:1366–1379

    Article  Google Scholar 

  3. Pakbaznia E, Pedram M (2009) Minimizing data center cooling and server power costs. In: Proceedings of the 14th ACM/IEEE international symposium on low power electronics and design (ISLPED09), pp 145–150

  4. Tang Q, Gupta SKS, Stanzione D, Cayton P (2006) Thermal-aware task scheduling to minimize energy usage of blade server based datacenters. In: 2nd IEEE international symposium on dependable: autonomic and secure computing, pp 195–202

  5. Choi J, Kim Y, Sivaubramaniam A, Srebric J, Wang Q, Lce J (2008) A CFD-based tool for studying temperature in rack-mounted server. IEEE Trans Comput 57:1129–1142

    Article  MathSciNet  Google Scholar 

  6. Beitelmal AH, Patel CD (2007) Thermo-fluids provisioning of a high performance high density data center. Distrib Parallel Datab 21:227–238

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Tang Q, Mukherjee T, Gupta SKS, Cayton P (2006) Sensor-based fast thermal evaluation model for energy efficient high-performance datacenters. In: Proceedings of international conference on intelligent sensing and information process, pp 203–208

  9. Kuhn KJ (2010) CMOS transistor scaling past 32nm and implications on variation. Advanced semiconductor manufacturing conference (ASMC), pp 241–246

  10. International technology roadmap for semiconductors overview. ITRS. http://www.itrs.net/Links/2012ITRS/Home2012.htm. Accessed 2013

  11. Pahlavan A, Momtazpour M, Goudarzi M (2012) Variation-aware server placement and task assignment for data center power minimization. The 10th IEEE international symposium on parallel and distributed processing with applications (ISPA), Madrid, Spain, July 2012

  12. Pahlavan A, Momtazpour M, Goudarzi M (2012) Data center power reduction by heuristic variation-aware server placement and chassis consolidation. The 16th CSI international symposium on computer architecture and digital systems (CADS), Shiraz, Iran, May 2012

  13. Beloglazov A, Buyya R, Lee Y, Zomaya A (2011) A taxonomy and survey of energy-efficient data centers and cloud computing systems. In: Zelkowitz M (ed) Advances in computers, vol 47. Elsevier, Amsterdam, pp 47–111

  14. Wang L, Laszewski G, Dayal J, Furlani TR (2009) Thermal aware workload scheduling with backfilling for green data centers. IEEE 28th performance computing and communications conference (IPCCC), pp 289–296

  15. Jonas M, Gilbert, RR, Ferguson J, Varsamopoulos G, Gupta SKS (2012) A transient model for data center thermal prediction. Green computing conference (IGCC), pp 1–10

  16. Tang Q, Gupta SKS, Varsamopoulos G (2012) A unified methodology for scheduling in distributed cyber-physical systems. ACM Transactions on embedded computing systems, special issue on the verification of cyber-physical software systems, 25 pages

  17. Mukherjee T, Banerjee A, Varsamopoulos G, Gupta SKS, Rungta S (2009) Spatio-temporal thermal-aware job scheduling to minimize energy consumption in virtualized heterogeneous data centers. Elsevier J Comput Netw 53:2888–2904

    Article  MATH  Google Scholar 

  18. Banerjee A, Mukherjee T, Varsamopoulos G, Gupta SKS (2011) Integrating cooling awareness with thermal aware workload placement for HPC data centers. Elsevier Sustain Comput: Inform Syst 1:134–150

    Google Scholar 

  19. Varsamopoulos G, Banerjee A, Gupta SKS (2009) Energy efficiency of thermal-aware job scheduling algorithms under various cooling models. International conference on contemporary computing (IC3), August 2009

  20. Gupta SKS, Gilbert RR, Banerjee A, Abbasi Z, Mukherjee T, Varsamopoulos G (2011) GDCSim: a tool for analyzing green data center design and resource management techniques. Green computing conference and workshops (IGCC), pp 1–8

  21. Moore J, Chase J, Ranganathan P, Sharma R (2005) Making scheduling ‘Cool’: Temperature-aware resource assignment in data centers. Usenix annual technical conference

  22. Wang L, Khan SU, Dayal J (2012) Thermal aware workload placement with task-temperature profiles in a data center. J Supercomput Springer 61:780–803

    Article  Google Scholar 

  23. Wang F, Nicopoulos C, Wu X, Xie Y, Vijaykrishnan N (2007) Variation-aware task allocation and scheduling for MPSoC. IEEE/ACM international conference on computer-aided design (ICCAD’07), November 2007

  24. Momtazpour M, Goudarzi M, Sanaei E (2010) Variation-aware task and communication scheduling in MPSoCs for power-yield maximization. IEICE transaction fundamentals of electronics, communications and computer sciences (special section on VLSI design and CAD algorithms). E93-A(12):2542–2550

  25. Momtazpour M, Goudarzi M, Sanaei E (2013) Static statistical MPSoC power optimization by variation-aware task and communication scheduling. Elsevier J Microproc Microsyst 37:953–963

  26. Chang H (2006) Circuit timing leakage power analysis under process variations. A Ph.D. Dissertation of University of Minnesota, February 2006

  27. Borkar S, Karnik T, Narendra S, Tschanz J, Keshavarzi A, De V (2003) Parameter variations and impact on circuits and microarchitectures. Design automation conference (DAC’03), pp 338–342

  28. Balaji B, McCullough J, Gupta RK, Agarwal Y (2012) Accurate characterization of the variability in power consumption in modern mobile processors. In: Proceedings of the 2012 USENIX conference on power-aware computing and systems (HotPower’12)

  29. Wanner L, Balani R, Zahedi S, Apte C, Gupta P, Srivastava M (2011) Variability-aware duty cycle scheduling in long running embedded sensing systems. Design, Automation and test in Europe conference and exhibition (DATE11), pp 14–18

  30. Sirsi GB (2004) Leakage power optimization flow. International cadence usergroup conference

  31. Kuhn KJ, Giles MD, Becher D, Kolar P, Kornfeld A, Kotlyar R, Ma ST, Maheshwari A, Mudanai S (2011) Process technology variation. IEEE Trans Electron Dev 58:2197–2208

    Article  Google Scholar 

  32. Ghosh S, Roy K (2010) Parameter variation tolerance and error resilency: new design paradigm for the nanoscale era. Proc IEEE 98:1718–1751

    Article  Google Scholar 

  33. Pedram M (2012) Energy-efficient datacenters. IEEE Trans Comput-Aided Des Integr Circuits Syst 31:1465–1484

    Article  Google Scholar 

  34. Pakbaznia E, Ghasemazar M, Pedram M (2010) Temperature-aware dynamic resource provisioning in a power-optimized datacenter. Design, automation and test in Europe conference and exhibition (DATE10), pp 124–129

  35. Cengel YA (2003) Heat transfer: a practical approach, 2nd edn. McGraw-Hill, New York

    Google Scholar 

  36. Barroso L, Holzle U (2007) The case for energy-proportional computing. J IEEE Comput 40(12):33–37

  37. Rosenthal RE (2010) GAMS-a user guide. GAMS Development Corporation

  38. Rao R, Srivastava A, Blaauw D, Sylvester D (2003) Statistical estimation of leakage current considering inter- and intra-die process variation. Low power electronics and design (ISLPED), pp 84–89

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Correspondence to Maziar Goudarzi.

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Pahlavan, A., Momtazpour, M. & Goudarzi, M. Power reduction in HPC data centers: a joint server placement and chassis consolidation approach. J Supercomput 70, 845–879 (2014). https://doi.org/10.1007/s11227-014-1265-z

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