Toward the Datacenter: Scaling Simulation Up and Out

  • Eduardo Argollo
  • Ayose Falcón
  • Paolo Faraboschi
  • Daniel Ortega


The computing industry is changing rapidly, pushing strongly to consolidation into large “cloud computing” datacenters. New power, availability, and cost constraints require installations that are better optimized for their intended use. The problem of right-sizing large datacenters requires tools that can characterize both the target workloads and the hardware architecture space. Together with the resurgence of variety in industry standard CPUs, driven by very ambitious multi-core roadmaps, this is making the existing modeling techniques obsolete. In this chapter we revisit the basic computer architecture simulation concepts toward enabling fast and reliable datacenter simulation. Speed, full system, and modularity are the fundamental characteristics of a datacenter-level simulator. Dynamically trading off speed/accuracy, running an unmodified software stack, and leveraging existing “component” simulators are some of the key aspects that should drive next generation simulator’s design. As a case study, we introduce the COTSon simulation infrastructure, a scalable full-system simulator developed by HP Labs and AMD, targeting fast and accurate evaluation of current and future computing systems.


Virtual Machine Cloud Provider Timing Feedback Simulation Speed Functional Simulator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    AFCOM’s Data Center Institute, Five Bold Predictions for the Data Center Industry that will Change Your Future. March (2006).Google Scholar
  2. 2.
    Argollo, E., Falcón, A., Faraboschi, P., Monchiero, M., Ortega, D.: COTSon: Infrastructure for full system simulation. SIGOPS Oper Syst Rev 43(1), 52–61, (2009).CrossRefGoogle Scholar
  3. 3.
    Asanovic, K., Bodik, R., Christopher Catanzaro, B., Gebis, J.J., Husbands, P., Keutzer, K., Patterson, D.A., Plishker, W.L., Shalf, J., Williams, S.W., Yelick, K.A.: The landscape of parallel computing research: A view from Berkeley. In: Technical Report UCB/EECS-2006-183, EECS Department, University of California, Berkeley, December (2006).Google Scholar
  4. 4.
    Bedicheck, R.: SimNow: Fast platform simulation purely in software. In: Hot Chips 16, August (2004).Google Scholar
  5. 5.
    Bellard, F.: QEMU, a fast and portable dynamic translator. In: USENIX 2005 Annual Technical Conference, FREENIX Track, Anaheim, CA, pp. 41–46, April (2005).Google Scholar
  6. 6.
    Box, G., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis: Forecasting and Control, 3rd ed. Prentice-Hall, Upper Saddle River, NJ (1994).MATHGoogle Scholar
  7. 7.
    Bucy, J.S., Schindler, J., Schlosser, S.W., Ganger, G.R., Contributors.: The Disksim simulation environment version 4.0 reference manual. In: Carnegie Mellon University Parallel Data Lab Technical Report CMU-PDL-08-101, May (2008).Google Scholar
  8. 8.
    Falcón, A., Faraboschi, P., Ortega, D.: Combining simulation and virtualization through dynamic sampling. In: Proceedings of the IEEE International Symposium on Performance Analysis of Systems & Software (ISPASS), San Jose, CA, April (2007).Google Scholar
  9. 9.
    Falcón, A., Faraboschi, P., Ortega, D.: An adaptive synchronization technique for parallel simulation of networked clusters. In: Proceedings of the IEEE International Symposium on Performance Analysis of Systems & Software (ISPASS), Austin, TX, April (2008).Google Scholar
  10. 10.
    Karkhanis, T.S., Smith, J.E.: A first-order superscalar processor model. In: Proceedings of the 31st Annual International Symposium on Computer Architecture, München, Germany, June 19–23, (2004).Google Scholar
  11. 11.
    Luk, C.-K., Cohn, R., Muth, R., Patil, H., Klauser, A., Lowney, G., Wallace, S., Reddi, V.J., Hazelwood, K.: Pin: Building customized program analysis tools with dynamic instrumentation. In: Proceedings of the ACM Conference on Programming Language Design and Implementation (PLDI), Chicago, IL, June (2005).Google Scholar
  12. 12.
    Mauer, C.J., Hill, M.D., Wood, D.A.: Full-system timing-first simulation. In: SIGMETRICS ’02: Proceedings of the 2002 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, Marina Del Rey, CA, June (2002).Google Scholar
  13. 13.
    Ould-Ahmed-Vall, E., Woodlee, J., Yount, C., Doshi, K.A., Abraham, S. Using model trees for computer architecture performance analysis of software applications. In: Proceedings of the IEEE International Symposium on Performance Analysis of Systems & Software (ISPASS), San Jose, CA, April (2007).Google Scholar
  14. 14. data March (2009).Google Scholar
  15. 15.
    Rosenblum, M.: VMware’s virtual platform: A virtual machine monitor for commodity PCs. In: Hot Chips 11, August (1999).Google Scholar
  16. 16.
    Rosenblum, M., Herrod, S.A., Witchel, E., Gupta, A.: Complete computer system simulation: The SimOS approach. IEEE Parallel Distrib Technol 3(4), 34–43, (1995).CrossRefGoogle Scholar
  17. 17.
    Srivastava, A., Eustace, A.: ATOM–-a system for building customized program analysis tools. In: Proceedings of the ACM Conference on Programming Language Design and Implementation (PLDI), Orlando, FL, June (1994).Google Scholar
  18. 18.
    Yi, J.J., Kodakara, S.V., Sendag, R., Lilja, D.J., Hawkins, D.M.: Characterizing and comparing prevailing simulation techniques. In: Proceedings of the 11th International Conference on High Performance Computer Architecture, pp. 266–277, San Francisco, CA, February (2005).Google Scholar

Copyright information

© Springer Science+business Media, LLC 2010

Authors and Affiliations

  • Eduardo Argollo
    • 1
  • Ayose Falcón
    • 1
  • Paolo Faraboschi
    • 2
  • Daniel Ortega
    • 3
  1. 1.HP LabsBarcelonaSpain
  2. 2.HP LabsBarcelonaSpain
  3. 3.Intel LabsBarcelonaSpain

Personalised recommendations