Modeling Energy-Aware Cloud Federations with SRNs

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7400)


Cloud computing is a challenging technology that promises to strongly modify the way computing and storage resources will be accessed in the near future. However, it may demand huge amount of energy if adequate management policies are not put in place. In particular, in the context of Infrastructure as a Service (IaaS) Cloud, optimization strategies are needed in order to allocate, migrate, consolidate virtual machines, and manage the switch on/switch off period of a data centre. In this paper, we present a methodology based on stochastic reward nets (SRNs) to investigate the more convenient strategies to manage a federation of two or more private or public IaaS Clouds. Several policies are presented and their impact is evaluated, thus contributing to a rational and efficient adoption of the Cloud computing paradigm.


Cloud computing Energy saving Quality of Service Performance evaluation Stochastic reward nets 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Murugesan, S.: Harnessing green it: Principles and practices. IT Professional 10(1), 24–33 (2008)CrossRefGoogle Scholar
  2. 2.
    Foster, I., Zhao, Y., Raicu, I., Lu, S.: Cloud computing and grid computing 360-degree compared. In: Grid Computing Environments Workshop, GCE 2008, pp. 1–10 (2008)Google Scholar
  3. 3.
    Eucalyptus official site,
  4. 4.
  5. 5.
  6. 6.
    IBM Cloud Computing,
  7. 7.
    Eucalyptus official site,
  8. 8.
    openqrm official site,
  9. 9.
    Rochwerger, B., Breitgand, D., Epstein, A., Hadas, D., Loy, I., Nagin, K., Tordsson, J., Ragusa, C., Villari, M., Clayman, S., Levy, E., Maraschini, A., Massonet, P., Muñoz, H., Tofetti, G.: Reservoir - when one cloud is not enough. Computer 44(3), 44–51 (2011)CrossRefGoogle Scholar
  10. 10.
    Ye, K., Huang, D., Jiang, X., Chen, H., Wu, S.: Virtual machine based energy-efficient data center architecture for cloud computing: A performance perspective. In: Proceedings of the 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber, Physical and Social Computing, pp. 171–178 (2010)Google Scholar
  11. 11.
    Eugen Feller, D.L., Morin, C.: State of the art of power saving in clusters + results from the EDF case study. Tech. Rep. (2010)Google Scholar
  12. 12.
    Takeda, S., Takemura, T.: A rank-based VM consolidation method for power saving in datacenters. IPSJ Online Transactions 3, 88–96 (2010)CrossRefGoogle Scholar
  13. 13.
    Liu, L., Wang, H., Liu, X., Jin, X., He, W.B., Wang, Q.B., Chen, Y.: GreenCloud: a new architecture for green data center. In: Proceedings of the 6th International Conference Industry Session on Autonomic Computing and Communications Industry Session, pp. 29–38 (2009)Google Scholar
  14. 14.
    Bruneo, D., Scarpa, M., Puliafito, A.: Performance evaluation of glite grids through gspns. IEEE Transactions on Parallel and Distributed Systems 21(11), 1611–1625 (2010)CrossRefGoogle Scholar
  15. 15.
    Yigitbasi, N., Iosup, A., Epema, D., Ostermann, S.: C-Meter: A framework for performance analysis of computing Clouds. In: Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, pp. 472–477 (2009)Google Scholar
  16. 16.
    Buyya, R., Ranjan, R., Calheiros, R.N.: Modeling and simulation of scalable cloud computing environments and the cloudsim toolkit: Challenges and opportunities. In: HPCS (2009)Google Scholar
  17. 17.
    Bruneo, D., Longo, F., Puliafito, A.: Evaluating energy consumption in a cloud infrastructure. In: IEEE WoWMoM, pp. 1–6 (June 2011)Google Scholar
  18. 18.
    Ciardo, G., Blakemore, A., Chimento, P.F., Muppala, J.K., Trivedi, K.S.: Automated generation and analysis of Markov reward models using stochastic reward nets. IMA Volumes in Mathematics and its Applications: Linear Algebra, Markov Chains, and Queueing Models 48, 145–191 (1993)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Rochwerger, B., Breitgand, D., Levy, E., Galis, A., Nagin, K., Llorente, I.M., Montero, R., Wolfsthal, Y., Elmroth, E., Cáceres, J., Ben-Yehuda, M., Emmerich, W., Galán, F.: The reservoir model and architecture for open federated cloud computing. IBM J. Res. Dev. 53, 535–545 (2009)CrossRefGoogle Scholar
  20. 20.
    Marsan, M.A., Balbo, G., Conte, G.: A class of generalized stochastic Petri nets for the performance evaluation of multiprocessor systems. ACM Transactions on Computer Systems 2, 93–122 (1984)CrossRefGoogle Scholar
  21. 21.
    Machida, F., Kim, D.S., Trivedi, K.: Modeling and analysis of software rejuvenation in a server virtualized system. In: 2010 IEEE Second International Workshop on Software Aging and Rejuvenation, WoSAR, pp. 1–6 (November 2010)Google Scholar
  22. 22.
    Bruneo, D., Distefano, S., Longo, F., Puliafito, A., Scarpa, M.: Evaluating wireless sensor node longevity through markovian techniques. Computer Networks 56(2), 521–532 (2012)CrossRefGoogle Scholar
  23. 23.
    Ciardo, G., Lüttgen, G., Siminiceanu, R.I.: Efficient Symbolic State-Space Construction for Asynchronous Systems. In: Nielsen, M., Simpson, D. (eds.) ICATPN 2000. LNCS, vol. 1825, pp. 103–122. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  24. 24.
    Ciardo, G., Marmorstein, R., Siminiceanu, R.: The saturation algorithm for symbolic state space exploration. Software Tools for Technology Transfer 8(1), 4–25 (2006)CrossRefGoogle Scholar
  25. 25.
    Miner, A.S., Ciardo, G.: Efficient Reachability Set Generation and Storage Using Decision Diagrams. In: Donatelli, S., Kleijn, J. (eds.) ICATPN 1999. LNCS, vol. 1639, pp. 6–25. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  26. 26.
    Ghosh, R., Trivedi, K., Naik, V., Kim, D.S.: End-to-end performability analysis for infrastructure-as-a-service cloud: An interacting stochastic models approach. In: 2010 IEEE 16th Pacific Rim International Symposium on Dependable Computing, PRDC, pp. 125–132 (December 2010)Google Scholar
  27. 27.
    Bobbio, A., Puliafito, A., Scarpa, M., Telek, M.: Webspn: A web-accessible Petri net tool. In: Conference on Web-Based Modeling & Simulation (1998)Google Scholar
  28. 28.
    Buyya, R., Ranjan, R., Calheiros, R.: Modeling and simulation of scalable cloud computing environments and the cloudsim toolkit: Challenges and opportunities. In: International Conference on High Performance Computing Simulation, HPCS 2009, pp. 1–11 (June 2009)Google Scholar
  29. 29.
    Kim, J.H., Lee, S.M., Kim, D.S., Park, J.S.: Performability analysis of iaas cloud. In: 2011 Fifth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS, June 30-July 2, pp. 36–43 (2011)Google Scholar
  30. 30.
    Iosup, A., Yigitbasi, N., Epema, D.: On the performance variability of production cloud services. In: 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid, pp. 104–113 (May 2011)Google Scholar
  31. 31.
    Iosup, A., Ostermann, S., Yigitbasi, M., Prodan, R., Fahringer, T., Epema, D.: Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Transactions on Parallel and Distributed Systems 22(6), 931–945 (2011)CrossRefGoogle Scholar
  32. 32.
    Stantchev, V.: Performance evaluation of cloud computing offerings. In: Third International Conference on Advanced Engineering Computing and Applications in Sciences, ADVCOMP 2009, pp. 187–192 (October 2009)Google Scholar
  33. 33.
    Ostermann, S., Iosup, A., Yigitbasi, N., Prodan, R., Fahringer, T., Epema, D.: A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing. In: Avresky, D.R., Diaz, M., Bode, A., Ciciani, B., Dekel, E. (eds.) Cloudcom 2009. LNICST, vol. 34, pp. 115–131. Springer, Heidelberg (2010)Google Scholar
  34. 34.
    Koeppe, F., Schneider, J.: Do you get what you pay for? using proof-of-work functions to verify performance assertions in the cloud. In: 2010 IEEE Second International Conference on Cloud Computing Technology and Science, CloudCom, November 30-December 3, pp. 687–692 (2010)Google Scholar
  35. 35.
    Ghosh, R., Naik, V.K., Trivedi, K.S.: Power-performance trade-offs in iaas cloud: A scalable analytic approach. In: Dependable Systems and Networks Workshops, pp. 152–157 (2011)Google Scholar
  36. 36.
    Khazaei, H., Misic, J., Misic, V.: Performance analysis of cloud computing centers using m/g/m/m + r queueing systems. IEEE Transactions on Parallel and Distributed Systems PP(99), 1 (2011)Google Scholar
  37. 37.
    Verma, A., Dasgupta, G., Nayak, T.K., De, P., Kothari, R.: Server workload analysis for power minimization using consolidation. In: Proceedings of the 2009 Conference on USENIX Annual Technical Conference, USENIX 2009, p. 28. USENIX Association, Berkeley (2009), Google Scholar
  38. 38.
    Pacheco-Sanchez, S., Casale, G., Scotney, B., McClean, S., Parr, G., Dawson, S.: Markovian workload characterization for qos prediction in the cloud. In: IEEE International Conference on Cloud Computing, pp. 147–154 (2011)Google Scholar
  39. 39.
    Yigitbasi, N., Iosup, A., Epema, D.: C-meter: A framework for performance analysis of computing clouds. In: International Workshop on Cloud Computing (2009)Google Scholar
  40. 40.
    Voorsluys, W., Broberg, J., Venugopal, S., Buyya, R.: Cost of Virtual Machine Live Migration in Clouds: A Performance Evaluation. In: Jaatun, M.G., Zhao, G., Rong, C. (eds.) CloudCom 2009. LNCS, vol. 5931, pp. 254–265. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  41. 41.
    Wu, Y., Zhao, M.: Performance modeling of virtual machine live migration. In: 2011 IEEE International Conference on Cloud Computing, CLOUD, pp. 492–499 (July 2011)Google Scholar
  42. 42.
    Gmach, D., Rolia, J., Cherkasova, L.: Resource and virtualization costs up in the cloud: Models and design choices. In: 2011 IEEE/IFIP 41st International Conference on Dependable Systems Networks, DSN, pp. 395–402 (June 2011)Google Scholar
  43. 43.
    Govindan, S., Liu, J., Kansal, A., Sivasubramaniam, A.: Cuanta: quantifying effects of shared on-chip resource interference for consolidated virtual machines. In: Proceedings of the 2nd ACM Symposium on Cloud Computing, SOCC 2011, pp. 22:1–22:14. ACM, New York (2011)Google Scholar
  44. 44.
    Rhoden, B., Klues, K., Zhu, D., Brewer, E.: Improving per-node efficiency in the datacenter with new os abstractions. In: Proceedings of the 2nd ACM Symposium on Cloud Computing, SOCC 2011, pp. 25:1–25:8. ACM, New York (2011)Google Scholar
  45. 45.
    Goudarzi, H., Pedram, M.: Multi-dimensional sla-based resource allocation for multi-tier cloud computing systems. In: 2011 IEEE International Conference on Cloud Computing, CLOUD, pp. 324–331 (July 2011)Google Scholar
  46. 46.
    Lenk, A., Menzel, M., Lipsky, J., Tai, S., Offermann, P.: What are you paying for? performance benchmarking for infrastructure-as-a-service offerings. In: 2011 IEEE International Conference on Cloud Computing, CLOUD, pp. 484–491 (July 2011)Google Scholar
  47. 47.
    Berl, A., Gelenbe, E., Di Girolamo, M., Giuliani, G., De Meer, H., Dang, M.Q., Pentikousis, K.: Energy-efficient cloud computing. The Computer Journal 53(7), 1045–1051 (2010), CrossRefGoogle Scholar
  48. 48.
    Chase, J.S., Anderson, D.C., Thakar, P.N., Vahdat, A.M., Doyle, R.P.: Managing energy and server resources in hosting centers. In: Proceedings of the Eighteenth ACM Symposium on Operating Systems Principles, SOSP 2001, pp. 103–116. ACM, New York (2001), CrossRefGoogle Scholar
  49. 49.
    Mitrani, I.: Service center trade-offs between customer impatience and power consumption. Perform. Eval. 68, 1222–1231 (2011), CrossRefGoogle Scholar
  50. 50.
    Artalejo, J.R., Economou, A., Lopez-Herrero, M.J.: Analysis of a multiserver queue with setup times. Queueing Syst. Theory Appl. 51, 53–76 (2005), MathSciNetzbMATHCrossRefGoogle Scholar
  51. 51.
    Gandhi, A., Harchol-Balter, M., Adan, I.: Server farms with setup costs. Perform. Eval. 67, 1123–1138 (2010), CrossRefGoogle Scholar
  52. 52.
    Mazzucco, M., Dyachuk, D., Dikaiakos, M.: Profit-aware server allocation for green internet services. In: Proceedings of the 2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2010, pp. 277–284. IEEE Computer Society, Washington, DC (2010), CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Dipartimento di MatematicaUniversità degli Studi di MessinaMessinaItalia

Personalised recommendations