Skip to main content

On understanding the economics and elasticity challenges of deploying business applications on public cloud infrastructure


The exposure of business applications to the web has considerably increased the variability of its workload patterns and volumes as the number of users/customers often grows and shrinks at various rates and times. Such application characteristics have increasingly demanded the need for flexible yet inexpensive computing infrastructure to accommodate variable workloads. The on-demand and per-use cloud computing model, specifically that of public Cloud Infrastructure Service Offerings (CISOs), has quickly evolved and adopted by majority of hardware and software computing companies with the promise of provisioning utility-like computing resources at massive economies of scale. However, deploying business applications on public cloud infrastructure does not lead to achieving desired economics and elasticity gains, and some challenges block the way for realizing its real benefits. These challenges are due to multiple differences between CISOs and application’s requirements and characteristics. This article introduces a detailed analysis and discussion of the economics and elasticity challenges of business applications to be deployed and operate on public cloud infrastructure. This includes analysis of various aspects of public CISOs, modeling and measuring CISOs’ economics and elasticity, application workload patterns and its impact on achieving elasticity and economics, economics-driven elasticity decisions and policies, and SLA-driven monitoring and elasticity of cloud-based business applications. The analysis and discussion are supported with motivating scenarios for cloud-based business applications. The paper provides a multi-lenses overview that can help cloud consumers and potential business application’s owners to understand, analyze, and evaluate important economics and elasticity capabilities of different CISOs and its suitability for meeting their business application’s requirements.


  1. 1.

    Amazon auto scaling documentation (2011), March 2011

  2. 2.

    Amazon web services case studies (2011), March 2011

  3. 3.

    Armbrust M, Fox A, Rean G, Joseph A, Katz R, Konwinski A, Gunho L, David P, Rabkin A, Stoica I, Zaharia M (2009) Above the clouds: a Berkeley view of cloud computing. Technical report UCB/EECS-2009-28, EECS Department, University of California, Berkeley, Feb 2009

  4. 4.

    Beitch A, Liu B, Yung T, Griffith R, Fox A, Patterson D (2010) Rain: a workload generation toolkit for cloud computing applications. Technical report UCB/EECS-2010-14, EECS Department, University of California, Berkeley, Feb 2010

  5. 5.

    Bibi S, Katsaros D, Bozanis P (2010) Cloud computing economics. Advanced design approaches to emerging software systems: principles, methodology and tools. IGI Global Publishing, Hershey

    Google Scholar 

  6. 6.

    Bodík P, Fox A, Franklin M, Jordan M, Patterson D (2010) Characterizing, modeling, and generating workload spikes for stateful services. In: Proceedings of the 1st ACM symposium on cloud computing (SoCC), pp 241–252. doi:10.1145/1807128.1807166

    Chapter  Google Scholar 

  7. 7.

    Bonvin N, Papaioannou T, Aberer K (2010) An economic approach for scalable and highly-available distributed applications. In: Proceedings of the IEEE international conference on cloud computing (IEEE CLOUD), pp 498–505. doi:10.1109/CLOUD.2010.45

    Google Scholar 

  8. 8.

    Bouchenak S (2010) Automated control for SLA-aware elastic clouds. In: Proceedings of the 5th international workshop on feedback control implementation and design in computing systems and networks, pp 27–28. doi:10.1145/1791204.1791210

    Google Scholar 

  9. 9.

    Breitgand D, Henis E, Shehory O, Lake J (2007) Derivation of response time service level objectives for business services. In: Proceedings of the 2nd IEEE/IFIP international workshop on business-driven IT management (BDIM), pp 29–38. doi:10.1109/BDIM.2007.375009

    Google Scholar 

  10. 10.

    Chazalet A (2010) Service level checking in the cloud computing context. In: Proceedings of the IEEE international conference on cloud computing (IEEE CLOUD), pp 297–304. doi:10.1109/CLOUD.2010.15

    Google Scholar 

  11. 11.

    Chen Y, Iyer S, Liu X, Milojicic D, Sahai A (2008) Translating service level objectives to lower level policies for multi-tier services. Clust Comput 11(3): 299–311. doi:10.1007/s10586-008-0059-6

    Article  Google Scholar 

  12. 12.

    Cooper B, Silberstein A, Tam E, Ramakrishnan R, Sears R (2010) Benchmarking cloud serving systems with YCSB. In: Proceedings of the 1st ACM symposium on cloud computing (SoCC), pp 143–154. doi:10.1145/1807128.1807152

    Chapter  Google Scholar 

  13. 13.

    Durkee D (2010) Why cloud computing will never be free. Commun ACM 53(5): 62–69. doi:10.1145/1735223.1735242

    Article  Google Scholar 

  14. 14.

    Ferretti S, Ghini V, Panzieri F, Pellegrini M, Turrini E (2010) QoS-aware clouds. In: Proceedings of the IEEE international conference on cloud computing (EuroSys), pp 237–250. doi:10.1145/1755913.1755938

    Google Scholar 

  15. 15.

    Gartner (2008) Gartner top 10 disruptive technologies for 2008 to 2012. Technical report, Emerging trends and technologies roadshow, Gartner

  16. 16.

    GoGrid (2011) Gogrid cloud server user manual.

  17. 17.

    GoGrid (2011) Scale your internet business white paper., March 2011

  18. 18.

    Goyal P, Mikkilineni R (2009) Policy-based event-driven services-oriented architecture for cloud services operation & management. In: Proceedings of the IEEE international conference on cloud computing (IEEE CLOUD), pp 135–138. doi:10.1109/CLOUD.2009.76

    Google Scholar 

  19. 19.

    Gray J (2008) Distributed computing economics. ACM Queue 6(3): 63–68. doi:10.1145/1394127.1394131

    Article  Google Scholar 

  20. 20.

    Gregory A (2000) Foundations of multithreaded, parallel, and distributed programming. Addison-Wesley, Reading

    Google Scholar 

  21. 21.

    Hilley D (2009) Cloud computing: a taxonomy of platform and infrastructure-level offerings. Technical report, College of Computing, Center for Experimental Research in Computer Systems, Georgia Institute of Technology, April 2009

  22. 22.

    Rackspace Hosting (2011) Cloudkick cloud management.

  23. 23.

    Accenture in collaboration with WSP (2010) Cloud computing and sustainability: the environmental benefits of moving to the cloud. Technical report, Accenture, November 2010

  24. 24.

    Iosup A, Yigitbasi N, Epema D (2010) On the performance variability of production cloud services. Technical report 1387-2109, Parallel and Distributed Systems Report Series, Delft University of Technology, Jan 2010 (CCGRID), pp 104–113. doi:10.1109/CCGrid.2011.22

  25. 25.

    Guitart J, Oriol Fitó J, Goiri Í (2010) SLA-driven elastic cloud hosting provider. In: Proceedings of the 18th Euromicro conference on parallel, distributed and network-based processing (PDP), pp 111–118. doi:10.1109/PDP.2010.16

    Google Scholar 

  26. 26.

    JupiterResearch (2006) Retail web site performance: consumer reaction to a poor online shopping experience. Technical report, Akamai and JupiterKagan, June 2006

  27. 27.

    Kertesz A, Kecskemeti G, Brandic I (2009) An SLA-based resource virtualization approach for on-demand service provision. In: Proceedings of the 3rd international workshop on virtualization technologies in distributed computing, VTDC ’09, pp 27–34. doi:10.1145/1555336.1555341

    Chapter  Google Scholar 

  28. 28.

    Khajeh-Hosseini A, Greenwood D, Sommerville I (2010) Cloud migration: a case study of migrating an enterprise IT system to IaaS. In: Proceedings of the IEEE international conference on cloud computing (IEEE CLOUD), pp 450–457. doi:10.1109/CLOUD.2010.37

    Google Scholar 

  29. 29.

    Li A, Yang X, Kandula S, Zhang M (2010) CloudCMP: comparing public cloud providers. In: Proceedings of the 10th annual conference on internet measurement, pp 1–14. doi:10.1145/1879141.1879143

    Chapter  Google Scholar 

  30. 30.

    Li X, Li Y, Liu T, Qiu J, Wang F (2009) The method and tool of cost analysis for cloud computing. In: Proceedings of the IEEE international conference on cloud computing (IEEE CLOUD), pp 93–100. doi:10.1109/CLOUD.2009.84

    Google Scholar 

  31. 31.

    Marshall P, Keahey K, Freeman T (2010) Elastic site: using clouds to elastically extend site resources. In: Proceedings of the IEEE international symposium on cluster computing and the grid (CCGRID), pp 43–52. doi:10.1109/CCGRID.2010.80

    Google Scholar 

  32. 32.

    Mell P, Grance T (2009) Definition of cloud computing. Technical report, National Institute of Standards and Technologies (NIST), July 2009

  33. 33.

    Michael M, Moreira J, Shiloach D, Wisniewski R (2007) Scale-up x scale-out: a case study using Nutch/Lucene. In: International symposium on parallel and distributed processing, pp 1–8. doi:10.1109/IPDPS.2007.370631

    Google Scholar 

  34. 34.

    PassMark (2010) CPU Benchmarks.

  35. 35.

    Pujol J, Siganos G, Erramilli V, Rodriguez P (2009) Scaling online social networks without pains. In: Proceedings of the 5th international workshop on networking meets databases (NetDB), co-located with SOSP

    Google Scholar 

  36. 36.

    Reese G (2009) Cloud application architectures: building applications and infrastructure on the cloud. O’Reilly, Sebastopol

    Google Scholar 

  37. 37.

    RightScale (2011) Adaptable automation engine., March 2011

  38. 38.

    Sakr S, Liu A, Batista D, Alomari M (2011) A survey of large scale data management approaches in cloud environments. In: IEEE communications surveys and tutorials (IEEE COMST), pp 311–336

    Google Scholar 

  39. 39.

    Schad J, Dittrich J, Quiané-Ruiz J (2010) Runtime measurements in the cloud: observing, analyzing, and reducing variance. Proc VLDB Endow, 3(1): 460–471

    Article  Google Scholar 

  40. 40.

    Amazon Web Services (2011) AWS economic center.

  41. 41.

    Amazon Web Services (2011) Elastic load balancing.

  42. 42.

    Weaver E (2009) Improving running components.

  43. 43.

    Witty R (2011) Best practice in business continuity planning, Gartner, 2001., March 2011

  44. 44.

    Yang J, Qiu J, Li Y (2009) A profile-based approach to just-in-time scalability for cloud applications. In: Proceedings of the IEEE international conference on cloud computing (IEEE CLOUD), pp 9–16. doi:10.1109/CLOUD.2009.87

    Google Scholar 

  45. 45.

    ZDNet (2011) AWS disrupted by us east coast failure.

Download references

Author information



Corresponding author

Correspondence to Sherif Sakr.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Suleiman, B., Sakr, S., Jeffery, R. et al. On understanding the economics and elasticity challenges of deploying business applications on public cloud infrastructure. J Internet Serv Appl 3, 173–193 (2012).

Download citation


  • Cloud computing
  • Cost
  • Elasticity
  • Scaling
  • Economics
  • Business applications
  • SLA
  • Cloud infrastructure service offerings
  • IaaS