Skip to main content
Log in

Operational cost-aware resource provisioning for continuous write applications in cloud-of-clouds

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The emergence of cloud computing has made it become an attractive solution for large-scale data processing and storage applications. Cloud infrastructures provide users a remote access to powerful computing capacity, large storage space and high network bandwidth to deploy various applications. With the support of cloud computing, many large-scale applications have been migrated to cloud infrastructures instead of running on in-house local servers. Among these applications, continuous write applications (CWAs) such as online surveillance systems, can significantly benefit due to the flexibility and advantages of cloud computing. However, with specific characteristics such as continuous data writing and processing, and high level demand of data availability, cloud service providers prefer to use sophisticated models for provisioning resources to meet CWAs’ demands while minimizing the operational cost of the infrastructure. In this paper, we present a novel architecture of multiple cloud service providers (CSPs) or commonly referred to as Cloud-of-Clouds. Based on this architecture, we propose two operational cost-aware algorithms for provisioning cloud resources for CWAs, namely neighboring optimal resource provisioning algorithm and global optimal resource provisioning algorithm, in order to minimize the operational cost and thereby maximizing the revenue of CSPs. We validate the proposed algorithms through comprehensive simulations. The two proposed algorithms are compared against each other to assess their effectiveness, and with a commonly used and practically viable round-robin approach. The results demonstrate that NORPA and GORPA outperform the conventional round-robin algorithm by reducing the operational cost by up to 28 and 57 %, respectively. The low complexity of the proposed cost-aware algorithms allows us to apply it to a realistic Cloud-of-Clouds environment in industry as well as academia.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. Camba: http://www.camba.tv.

  2. Ivideon: http://www.ivideon.com.

  3. Iveda: https://www.iveda.com.

  4. Smartvue: http://smartvue.com.

  5. MATLAB: http://www.mathworks.com/products/matlab.

References

  1. Abramson, D., Buyya, R., Giddy, J.: A computational economy for grid computing and its implementation in the Nimrod-G resource broker. Futur. Gener. Comput. Syst. 18(8), 1061–1074 (2002)

    Article  MATH  Google Scholar 

  2. Albanesius, C.: Service Disruption Hits Google’s Gmail, Chrome. http://www.pcmag.com/article2/0,2817,2413035,00.asp (2012). Accessed 20 Nov 2015

  3. Almorsy, M., Grundy, J., Ibrahim, A.S.: Collaboration-based cloud computing security management framework. In: 2011 IEEE International Conference on Cloud Computing (CLOUD), pp. 364–371, Washington, DC, July 2011

  4. Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)

    Article  MATH  Google Scholar 

  5. AWS: Elastic Compute Cloud (EC2) Cloud Server and Hosting. http://aws.amazon.com/ec2 (2015). Accessed 20 Nov 2015

  6. Bertsekas, D.P., Gallager, R.G.: Data Networks. Prentice-Hall Inc, New Jersey (1992)

    MATH  Google Scholar 

  7. Buyya, R., Abramson, D., Giddy, J., Stockinger, H.: Economic models for resource management and scheduling in grid computing. J. Concurr. Comput. 14(13–15), 1507–1542 (2002)

    Article  MATH  Google Scholar 

  8. Cachin, C., Keidar, I., Shraer, A.: Trusting the cloud. ACM SIGACT News 40(2), 81–86 (2009)

    Article  Google Scholar 

  9. Cao, J., Hwang, K., Keqin, L., Zomaya, A.Y.: Optimal multiserver configuration for profit maximization in cloud computing. IEEE Trans. Parallel Distrib. Syst. 24(6), 1087–1096 (2013)

    Article  Google Scholar 

  10. Das, A., Grosu, D.: Combinatorial auction-based protocols for resource allocation in grids. In: IEEE IPDPS’05, pp. 251–258, Denver, Colorado, April 2005

  11. Díaz, M., Juan, G., Lucas, O., Ryuga, A.: Big data on the internet of things: an example for the E-health. In: 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 898–900, Palermo, July 2012

  12. Gera, A., Xia, C.H.: Learning curves and stochastic models for pricing and provisioning cloud computing services. Serv. Sci. 3(1), 99–109 (2011)

    Article  Google Scholar 

  13. Goldberg, A.V., Harrelson, C.: Computing the shortest path: a search meets graph theory. In: 16th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 156–165, Vancouver, January 2005

  14. Gu, H., Diao, Y., Liu, W., Zhang, X.: The design of smart home platform based on cloud computing. In: 2011 International Conference on Electronic Mechanical Engineering and Information Technology, pp. 3919–3922, Harbin, August 2011

  15. Houidi, I., Mechtri, M., Louati, W., Zeghlache, D.: Cloud service delivery across multiple cloud platforms. In: 2011 IEEE International Conference on Services Computing (SCC ’11), pp. 741–742, Washington, DC, July 2011

  16. IBM: Cloud Computing for Builders and Innovators. http://www.ibm.com/cloud-computing/ (2015). Accessed 20 Nov 2015

  17. May, J.M.: Parallel I/O for High Performance Computing. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  18. Mell, P., Grance, T.: The NIST definition of cloud computing. Technical Report SP800-145, National Institute of Standards and Technology, September 2011

  19. Microsoft: Microsoft Azure: Cloud Computing Platform and Services. https://azure.microsoft.com/en-us/ (2015). Accessed 20 Nov 2015

  20. Pourebrahimi, B., Bertels, K., Kandru, G.M., Vassiliadis, S.: Market-based resource allocation in grids. In: IEEE eScience 2006, pp. 80–87, Amsterdam, December 2006

  21. Radhakrishnan, A., Kalmadi, K.: Big data medical engine in the cloud (BDMEiC): your new health doctor. Infosys Labs Brief. 11(1), 41–47 (2013)

    Google Scholar 

  22. Roselli, D., Lorch, J.R. Anderson, T.E.: A comparison of file system workloads. In: 2000 USENIX Annual Technical Conference, pp. 1–4, San Diego, June 2000

  23. Silva, J.N., Veiga, L., Ferreira, P.: Heuristic for resources allocation on utility computing infrastructures. In: 6th International Workshop on Middleware for Grid Computing (MGC 2008), pp. 1–6, Leuven, December 2008

  24. Subashini, S., Kavitha, V.: A survey on security issues in service delivery models of cloud computing. J. Netw. Comput. Appl. 34(1), 1–11 (2011)

    Article  Google Scholar 

  25. Swan, M.: Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking. Int. J. Environ. Res. Public Health 6(2), 492–525 (2009)

    Article  Google Scholar 

  26. Truong-Huu, T., Koslovski, G., Anhalt, F., Montagnat, J., Primet, P.V.-B.: Joint elastic cloud and virtual network framework for application performance-cost optimization. J. Grid Comput. 9(1), 27–47 (2011)

    Article  Google Scholar 

  27. Truong-Huu, T., Tham, C.-K.: A novel model for competition and cooperation among cloud providers. IEEE Trans. Cloud Comput. 2(3), 251–265 (2014)

    Article  Google Scholar 

  28. Veeravalli, B., Ghose, D., Mani, V., Robertazzi, T.G.: Scheduling Divisible Loads in Parallel and Distributed Systems. IEEE Computer Society Press, Los Alamitos (1996)

    Google Scholar 

  29. Xiao, L., Zhu, Y., Ni, L.M., Xu, Z.: Incentive-based scheduling for market-like computational grids. IEEE Trans. Parallel Distrib. Syst. 19(7), 903–913 (2008)

    Article  Google Scholar 

  30. Zeng, Z., Veeravalli, B.: Design and performance evaluation of queue-and-rate-adjustment dynamic load balancing policies for distributed networks. IEEE Trans. Comput. 55(11), 1410–1422 (2006)

    Article  Google Scholar 

  31. Zeng, Z., Veeravalli, B.: On the design of distributed object placement and load balancing strategies in large-scale networked multimedia storage systems. IEEE Trans. Knowl. Data Eng. 20(3), 369–382 (2008)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by A*STAR, Singapore under the Research Thematic Programme on Future Data Centre Technology, Grant No. 1122804009.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tram Truong-Huu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zeng, Z., Truong-Huu, T., Veeravalli, B. et al. Operational cost-aware resource provisioning for continuous write applications in cloud-of-clouds. Cluster Comput 19, 601–614 (2016). https://doi.org/10.1007/s10586-016-0543-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-016-0543-3

Keywords

Navigation