Optimizing a Cloud Contract Portfolio Using Genetic Programming-Based Load Models

  • Sean Stijven
  • Ruben Van den Bossche
  • Ekaterina Vladislavleva
  • Kurt Vanmechelen
  • Jan Broeckhove
  • Mark Kotanchek
Part of the Genetic and Evolutionary Computation book series (GEVO)


Infrastructure-as-a-Service (IaaS) cloud providers offer a number of different tariff structures. The user has to balance the flexibility of the often quoted pay-by-the-hour, fixed price (“on demand”) model against the lower-cost-per-hour rate of a “reserved contract”. These tariff structures offer a significantly reduced cost per server hour (up to 50 %), in exchange for an up-front payment by the consumer. In order to reduce costs using these reserved contracts, a user has to make an estimation of its future compute demands, and purchase reserved contracts accordingly. The key to optimizing these cost benefits is to have an accurate model of the customer’s future compute load – where that load can have a variety of trends and cyclic behaviour on multiple time scales. In this chapter, we use genetic programming to develop load models for a number of large-scale web sites based on real-world data. The predicted future load is subsequently used by a resource manager to optimize the amount of IaaS servers a consumer should allocate at a cloud provider, and the optimal tariff plans (from a cost perspective) for that allocation. Our results illustrate the benefits of load forecasting for cost-efficient IaaS portfolio selection. They also might be of interest for the Genetic Programming (GP) community as a demonstration that GP symbolic regression can be successfully used for modelling discrete time series and has a tremendous potential for time lag identification and model structure discovery.


Cloud computing Symbolic regression Time series Load prediction Variable selection Forecasting 


  1. Agapitos A, Dyson M, Kovalchuk J, Lucas SM (2008) On the genetic programming of time-series predictors for supply chain management. In: Keijzer M, Antoniol G, Congdon CB, Deb K, Doerr B, Hansen N, Holmes JH, Hornby GS, Howard D, Kennedy J, Kumar S, Lobo FG, Miller JF, Moore J, Neumann F, Pelikan M, Pollack J, Sastry K, Stanley K, Stoica A, Talbi EG, Wegener I (eds) GECCO’08: proceedings of the 10th annual conference on genetic and evolutionary computation, Atlanta. ACM, pp 1163–1170. doi:10.1145/1389095.1389327, http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1163.pdf
  2. Armbrust B, Griffith R, Joseph AD, KatzR, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I, Zaharia M (2010) A view of cloud computing. Commun ACM 53(4):50–58. http://dl.acm.org/citation.cfm?id=1721672
  3. Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25(6):599–616. http://dx.doi.org/10.1016/j.future.2008.12.001
  4. Chaisiri S, Kaewpuang R, Lee BS, Niyato D (2011) Cost minimization for provisioning virtual servers in Amazon elastic compute cloud. In: 2011 IEEE 19th annual international symposium on modelling, analysis, and simulation of computer and telecommunication systems, Singapore. IEEE, pp 85–95. doi:10.1109/MASCOTS.2011.30, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6005371
  5. Evolved Analytics LLC (2011) DataModeler release 8.0 documentation. Evolved Analytics LLC. http://www.evolved-analytics.comwww.evolved-analytics.com
  6. Khatua S, Ghosh A, Mukherjee N (2010) Optimizing the utilization of virtual resources in cloud environment. In: 2010 IEEE international conference on virtual environments, human-computer interfaces and measurement systems, Taranto. IEEE, pp 82–87. doi:10.1109/VECIMS.2010.5609349, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5609349
  7. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT, CambridgeMATHGoogle Scholar
  8. Lopes R, Brasileiro F, Maciel P (2010) Business-driven capacity planning of a cloud-based it infrastructure for the execution of web applications. In: 2010 IEEE international symposium on parallel and distributed processing, workshops and Phd forum (IPDPSW), Atlanta, pp 1–8. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5470726
  9. Mell P, Grance T (2011) The NIST definition of cloud computing. NIST Spec Publ 800(145). http://pre-developer.att.com/home/learn/enablingtechnologies/The_NIST_Definition_of_Cloud_Computing.pdf
  10. Nikolaev N, Iba H (2001) Genetic programming of polynomial harmonic models using the discrete fourier transform. In: Proceedings of the 2001 Congress on evolutionary computation, 2001, vol 2, Samseong-dong, pp 902–909. doi:10.1109/ CEC.2001.934286Google Scholar
  11. Panyaworayan W, Wuetschner G (2002) Time series prediction using a recursive algorithm of a combination of genetic programming and constant optimization. Facta Universitatis Series: Electron Energ 15(2):265–279. http://factaee.elfak.ni.ac.yu/fu2k22/11wp.pdf
  12. Rodriguez-Vazquez K, Fleming PJ (1999) Genetic programming for dynamic chaotic systems modelling. In: Angeline PJ, Michalewicz Z, Schoenauer M, Yao X, Zalzala A (eds) Proceedings of the Congress on evolutionary computation, vol 1, Washington, DC. IEEE, pp 22–28Google Scholar
  13. Santini M, Tettamanzi A (2001) Genetic programming for financial time series prediction. In: Proceedings of the 4th European conference on genetic programming, EuroGP’01, London. Springer, pp 361–370. http://dl.acm.org/citation.cfm?id=646809.704093
  14. Schwaerzel R, Bylander T (2006) Predicting currency exchange rates by genetic programming with trigonometric functions and high-order statistics. In: Proceedings of the 8th annual conference on genetic and evolutionary computation, GECCO’06, Seattle. ACM, New York, pp 955–956. doi:10.1145/1143997.1144167, http://doi.acm.org/10.1145/1143997.1144167
  15. Tian C, Wang Y, Qi F, Yin B (2012) Decision model for provisioning virtual resources in Amazon EC2. In: Network and service management (cnsm), 2012 8th international conference and 2012 workshop on systems virtualiztion management (svm), Las Vegas, NV, U.S.A pp 159–163. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6380006
  16. Van den Bossche R, Vanmechelen K, Broeckhove J (2010) Cost-Optimal scheduling in hybrid IaaS clouds for deadline constrained workloads. In: 2010 IEEE 3rd international conference on cloud computing, Miami, pp 228–235. doi:10.1109/CLOUD.2010.58, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5557990
  17. Van den Bossche R, Vanmechelen K, Broeckhove J (2011) Cost-efficient scheduling heuristics for deadline constrained workloads on hybrid clouds. In: 2011 IEEE third international conference on cloud computing technology and science, Athens, pp 320–327. doi:10.1109/CloudCom.2011.50, http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6133159
  18. Van den Bossche R, Vanmechelen K, Broeckhove J (2013) Online cost-efficient scheduling of deadline-constrained workloads on hybrid clouds. Future Gener Comput Syst 29(4):973–985. doi:10.1016/j.future.2012.12.012, http://linkinghub.elsevier.com/retrieve/pii/S0167739X12002324
  19. Wagner N, Michalewicz Z, Khouja M, McGregor R (2007) Time series forecasting for dynamic environments: the DyFor genetic program model. IEEE Trans Evol Comput 11(4):433–452. doi:10.1109/TEVC.2006.882430CrossRefGoogle Scholar
  20. Yu T, Chen SH, Kuo TW (2004) Discovering financial technical trading rules using genetic programming with lambda abstraction. In: O’Reilly UM, Yu T, Riolo RL, Worzel B (eds) Genetic programming theory and practice II. Springer, Ann Arbor, chap 2, pp 11–30. doi:10.1007/0-387-23254-0-2Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Sean Stijven
    • 1
    • 2
  • Ruben Van den Bossche
    • 1
  • Ekaterina Vladislavleva
    • 3
  • Kurt Vanmechelen
    • 1
  • Jan Broeckhove
    • 1
  • Mark Kotanchek
    • 4
  1. 1.Universiteit AntwerpenAntwerpBelgium
  2. 2.Ghent University–iMindsGhentBelgium
  3. 3.Evolved Analytics EuropeBeerseBelgium
  4. 4.Evolved Analytics L.L.CMidlandUSA

Personalised recommendations