Cloud Adoption by Fine-Grained Resource Adaptation: Price Determination of Diagonally Scalable IaaS

  • Kevin Laubis
  • Viliam Simko
  • Alexander Schuller
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 567)


Cloud computing is a suitable solution for addressing the uncertainty of resource demand by allowing dynamic resource adjustment. However, most IaaS cloud providers offer their services with a limited granularity at rather slow scaling speeds and flat pricing schemes. Diagonal scaling techniques can offer a more adaptive and fine-grained service with a likewise granular pricing model. Before offering such an adaptive service, cloud providers need a comparison between horizontal and diagonal scaling models to estimate how resource prices can be increased while still staying competitive. In this paper we examine the resource reduction potential of diagonal scaling in comparison to conventional horizontal approaches. Given an empirical load pattern of a web application provider we find a CPU allocation reduction potential of 8.05 % compared to the conventional service. Given a more fine-grained pricing model, we find an additional revenue potential for diagonal scaling of 9.01 % when following a competitor based pricing regime.


Cloud computing IaaS Scaling Adoption Pricing 


  1. 1.
    Andrade, P., et al.: Improving business by migrating applications to the cloud using cloudstep. In: Proceedings of WAINA 2015, pp. 77–82, March 2015Google Scholar
  2. 2.
    Berndt, P., Maier, A.: Towards sustainable IaaS pricing. In: Altmann, J., Vanmechelen, K., Rana, O.F. (eds.) GECON 2013. LNCS, vol. 8193, pp. 173–184. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Chebrolu, S.B.: Assessing the relationships among cloud adoption, strategic alignment and information technology effectiveness. JITM 22(2), 13–29 (2011)Google Scholar
  4. 4.
    Chieu, T., Mohindra, A., Karve, A., Segal, A.: Dynamic scaling of web applications in a virtualized cloud computing environment. In: Proceedings of the ICEBE 2009, pp. 281–286, October 2009Google Scholar
  5. 5.
    Dawoud, W., Takouna, I., Meinel, C.: Elastic virtual machine for fine-grained cloud resource provisioning. In: Krishna, P.V., Babu, M.R., Ariwa, E. (eds.) ObCom 2011, Part I. CCIS, vol. 269, pp. 11–25. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Dutta, S., Gera, S., Verma, A., Viswanathan, B.: Smartscale: automatic application scaling in enterprise clouds. In: Proceedings of the IEEE CLOUD 2012, pp. 221–228 (2012)Google Scholar
  7. 7.
    Eckerson, W.W.: Three tier client/server architectures: achieving scalability, performance, and efficiency in client/server applications. Open Inf. Syst. 3(20), 46–50 (1995)Google Scholar
  8. 8.
    El Kihal, S., Schlereth, C., Skiera, B.: Price comparison for infrastructure-as-a-service. In: Proceedings of the ECIS 2012, June 2015, pp. 1–12 (2012)Google Scholar
  9. 9.
    Han, R., Guo, L., Ghanem, M.M., Guo, Y.: Lightweight resource scaling for cloud applications. In: Proceedings of IEEE/ACM CCGrid 2014, pp. 644–651 (2012)Google Scholar
  10. 10.
    Heinze, T., Pappalardo, V., Jerzak, Z., Fetzer, C.: Auto-scaling techniques for elastic data stream processing. In: Proceedings of ICDE 2014, pp. 296–302 (2014)Google Scholar
  11. 11.
    Iqbal, W., et al.: Adaptive resource provisioning for read intensive multi-tier applications in the cloud. Future Gener. Comput. Syst. 27, 871–879 (2011)CrossRefGoogle Scholar
  12. 12.
    Jamshidi, P., Ahmad, A., Pahl, C.: Cloud migration research: a systematic review. IEEE Trans. Cloud Comput. 1(2), 142–157 (2013)CrossRefGoogle Scholar
  13. 13.
    Jamshidi, P., Ahmad, A., Pahl, C.: Autonomic resource provisioning for cloud-based software. In: Proceedings of SEAMS 2014, pp. 95–104. ACM, New York, NY, USA (2014)Google Scholar
  14. 14.
    Jin, H., Wang, X., Wu, S., Di, S., Shi, X.: Towards optimized fine-grained pricing of IaaS cloud platform. IEEE Trans. Cloud Comput. 3(4), 1 (2014)Google Scholar
  15. 15.
    Kalyvianaki, E., Charalambous, T., Hand, S.: Self-adaptive and self-configured CPU resource provisioning for virtualized servers using kalman filters. In: Proceedings of the ICAC 2009, pp. 117–126. ACM, New York (2009)Google Scholar
  16. 16.
    Leavitt, N.: Is cloud computing really ready for prime time? Computer 42(1), 15–20 (2009)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Lin, G., Fu, D., Zhu, J., Dasmalchi, G.: Cloud computing: IT as a service. IT Prof. 11(2), 10–13 (2009)CrossRefGoogle Scholar
  18. 18.
    Mao, M., Humphrey, M.: A performance study on the VM startup time in the cloud. In: Proceedings of the IEEE CLOUD 2012, pp. 423–430 (2012)Google Scholar
  19. 19.
    Menascé, D.A.: QoS issues in web services. IEEE Internet Comput. 6(6), 72–75 (2002)CrossRefGoogle Scholar
  20. 20.
    Raza, M.H., Adenola, A.F., Nafarieh, A., Robertson, W.: The slow adoption of cloud computing and IT workforce. Procedia Comput. Sci. 52, 1114–1119 (2015)CrossRefGoogle Scholar
  21. 21.
    Sedaghat, M., Hernandez-Rodriguez, F., Elmroth, E.: A virtual machine re-packing approach to the horizontal vs. vertical elasticity trade-off for cloud autoscaling. In: CAC, p. 1 (2013)Google Scholar
  22. 22.
    Sperber, A.P.: Diagonale Skalierung verteilter Webanwendungen am Beispiel von gloveler. Ph.D. thesis, Karlsruhe Institute of Technology (KIT), Karlsruhe (2014)Google Scholar
  23. 23.
    Urgaonkar, B., et al.: An analytical model for multi-tier internet services and its applications. SIGMETRICS Perform. Eval. Rev. 33(1), 291–302 (2005)CrossRefGoogle Scholar
  24. 24.
    Vasić, N., Novaković, D., Miučin, S., Kostić, D., Bianchini, R.: Dejavu: accelerating resource allocation in virtualized environments. In: Proceedings of the ASPLOS 2012, pp. 423–436 (2012)Google Scholar
  25. 25.
    Yazdanov, L., Fetzer, C.: Vertical scaling for prioritized VMs provisioning. In: Proceedings of the CGC 2012, pp. 118–125. IEEE Computer Society, Washington, DC (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Kevin Laubis
    • 1
  • Viliam Simko
    • 1
  • Alexander Schuller
    • 1
  1. 1.FZI Research Center for Information TechnologyKarlsruheGermany

Personalised recommendations