Skip to main content
SpringerLink
Log in

Cloud Allocation and Consolidation Based on a Scalability Metric

  • Conference paper
  • First Online:
Algorithms and Architectures for Parallel Processing (ICA3PP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12454))

  • 1771 Accesses

Abstract

In this paper, we present a new allocation and resource consolidation system based on a scalability metric. According to cloud computing principles, the end users rent computing and big data analytic services with a pay-as-you-go cost model. However, when users’ data size increases or when the application stresses the memory or requires more computing power, they need to scale their rental to achieve approximately the same performance, such as task completion time and normalized system throughput. In this paper, we propose to delegate the responsibility to scale-up and scale-out the cloud system to a new component of the cloud orchestrator. The decision is taken on a metric that quantifies the scalability of the cloud system consistently under different system expansion configurations. The scalability metric is defined as a ratio between the new and the current situation, over the size of the i-th workload in terms of CPU cores and a performance metric. The considered metrics are the waiting time in the queue and the average resource (cores) usage rate during task execution. To validate our approach, we conduct experiments by emulation, on a real platform. The experimental results demonstrate the validity of the proposed general automatic strategies for cloud allocation and consolidation.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ahmad, A.A.-S., Andras, P.: Measuring the scalability of cloud-based software services. In: 2018 IEEE World Congress on Services (SERVICES), pp. 5–6. IEEE (2018)

    Google Scholar 

  2. Ala’Anzy, M., Othman, M.: Load balancing and server consolidation in cloud computing environments: a meta-study. IEEE Access 7, 141868–141887 (2019)

    Article  Google Scholar 

  3. Behzadian, M., Kazemzadeh, R., Albadvi, A., Aghdasi, M.: PROMETHEE: a comprehensive literature review on methodologies and applications. Eur. J. Oper. Res. 200(1), 198–215 (2010)

    Article  Google Scholar 

  4. Beloglazov, A., Buyya, R.: OpenStack Neat: a framework for dynamic and energy-efficient consolidation of virtual machines in OpenStack clouds. Concurr. Comput. Pract. Exp. 27(5), 1310–1333 (2015)

    Article  Google Scholar 

  5. Cérin, C., Menouer, T., Lebbah, M.: Accelerating the computation of multi-objectives scheduling solutions for cloud computing. In: 8th IEEE International Symposium on Cloud and Service Computing, SC2 2018, Paris, France, 18–21 November 2018, pp. 49–56. IEEE (2018)

    Google Scholar 

  6. de Oliveira e Silva, J.N., Veiga, L., Ferreira, P.: A\({}^{\text{2}}\)HA - automatic and adaptive host allocation in utility computing for bag-of-tasks. J. Internet Serv. Appl. 2(2), 171–185 (2011)

    Google Scholar 

  7. Elmubarak, S.A., Yousif, A., Bashir, M.B.: Performance based ranking model for cloud SaaS services. Int. J. Inf. Technol. Comput. Sci. 9(1), 65–71 (2017)

    Google Scholar 

  8. Ferdaus, M.H., Murshed, M., Calheiros, R.N., Buyya, R.: Virtual machine consolidation in cloud data centers using ACO metaheuristic. In: Silva, F., Dutra, I., Santos Costa, V. (eds.) Euro-Par 2014. LNCS, vol. 8632, pp. 306–317. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09873-9_26

    Chapter  Google Scholar 

  9. Gao, J., et al.: A cloud-based TaaS infrastructure with tools for SaaS validation, performance and scalability evaluation. In: 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings, pp. 464–471. IEEE (2012)

    Google Scholar 

  10. Gao, J., Pattabhiraman, P., Bai, X., Tsai, W.-T.: SaaS performance and scalability evaluation in clouds. In: Proceedings of 2011 IEEE 6th International Symposium on Service Oriented System (SOSE), pp. 61–71. IEEE (2011)

    Google Scholar 

  11. Grama, A., Gupta, A., Kumar, V.: Isoefficiency function: a scalability metric for parallel algorithms and architectures. IEEE Trans. Parallel Distrib. Syst. 4(8), 02 (1996)

    Google Scholar 

  12. Grama, A.Y., Gupta, A., Kumar, V.: Isoefficiency: measuring the scalability of parallel algorithms and architectures. IEEE Parallel Distrib. Technol. Syst. Appl. 1(3), 12–21 (1993)

    Article  Google Scholar 

  13. Grid500: Grid5000. https://www.grid5000.fr/. Accessed 08 May 2020

  14. Gunther, N., Puglia, P., Tomasette, K.: Hadoop superlinear scalability. Queue 13(5), 20 (2015)

    Article  Google Scholar 

  15. Hwang, K., Bai, X., Shi, Y., Li, M., Chen, W.-G., Wu, Y.: Cloud performance modeling with benchmark evaluation of elastic scaling strategies. IEEE Trans. Parallel Distrib. Syst. 27(1), 130–143 (2015)

    Article  Google Scholar 

  16. Brans, J.-P., Mareschal, B.: PROMETHEE methods - multiple criteria decision analysis: state of the art surveys. In: International Series in Operations Research & Management Science, vol. 78. Springer, New York (2005). https://www.springer.com/gp/book/9780387230818#

  17. Jin, X., Zhang, F., Wang, L., Hu, S., Zhou, B., Liu, Z.: Joint optimization of operational cost and performance interference in cloud data centers. IEEE Trans. Cloud Comput. 5(4), 697–711 (2017)

    Article  Google Scholar 

  18. Khan, M.A., Paplinski, A.P., Khan, A.M., Murshed, M., Buyya, R.: Exploiting user provided information in dynamic consolidation of virtual machines to minimize energy consumption of cloud data centers. In: 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC), pp. 105–114, April 2018

    Google Scholar 

  19. kubernetes: kubernetes. https://kubernetes.io/. Accessed 08 May 2020

  20. Meena, M., Bharadi, V.A.: Performance analysis of cloud based software as a service (SaaS) model on public and hybrid cloud. In: 2016 Symposium on Colossal Data Analysis and Networking (CDAN), pp. 1–6. IEEE (2016)

    Google Scholar 

  21. Menouer, T., Cérin, C., Darmon, P.: Accelerated PROMETHEE algorithm based on dimensionality reduction. In: Hsu, C.-H., Kallel, S., Lan, K.-C., Zheng, Z. (eds.) IOV 2019. LNCS, vol. 11894, pp. 190–203. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38651-1_17

    Chapter  Google Scholar 

  22. Menouer, T., Cérin, C., Hsu, C.-H.R.: Opportunistic scheduling and resources consolidation system based on a new economic model. J. Supercomput. (2020)

    Google Scholar 

  23. Deshmukh, S.C.: Preference ranking organization method of enrichment evaluation (PROMETHEE). Int. J. Eng. Sci. Inven. 2, 28–34 (2013)

    Google Scholar 

  24. Simão, J., Veiga, L.: QoE-JVM: an adaptive and resource-aware Java runtime for cloud computing. In: Meersman, R., et al. (eds.) OTM 2012. LNCS, vol. 7566, pp. 566–583. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33615-7_8

    Chapter  Google Scholar 

  25. Simão, J., Veiga, L.: Partial utility-driven scheduling for flexible SLA and pricing arbitration in clouds. IEEE Trans. Cloud Comput. 4(4), 467–480 (2016)

    Article  Google Scholar 

  26. Singh, S., Chana, I., Buyya, R.: STAR: SLA-aware autonomic management of cloud resources. IEEE Trans. Cloud Comput., p. 1 (2018)

    Google Scholar 

  27. Sun, X.-H., Rover, D.T.: Scalability of parallel algorithm-machine combinations. IEEE Trans. Parallel Distrib. Syst. 5(6), 599–613 (1994)

    Article  Google Scholar 

  28. Taillandier, P., Stinckwich, S.: Using the PROMETHEE multi-criteria decision making method to define new exploration strategies for rescue robots. In: 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics, pp. 321–326, November 2011

    Google Scholar 

  29. Tsai, W.-T., Huang, Y., Shao, Q.: Testing the scalability of SaaS applications. In: 2011 IEEE International Conference on Service-Oriented Computing and Applications (SOCA), pp. 1–4. IEEE (2011)

    Google Scholar 

  30. Ullman, J.: Np-complete scheduling problems. J. Comput. Syst. Sci. 10(3), 384–393 (1975)

    Article  MathSciNet  Google Scholar 

  31. Usmani, Z., Singh, S.: A survey of virtual machine placement techniques in a cloud data center. Procedia Comput. Sci. 78, 491–498 (2016). 1st International Conference on Information Security & Privacy 2015

    Article  Google Scholar 

  32. Witanto, J.N., Lim, H., Atiquzzaman, M.: Adaptive selection of dynamic VM consolidation algorithm using neural network for cloud resource management. Future Gener. Comput. Syst. 87, 35–42 (2018)

    Article  Google Scholar 

Download references

Acknowledgments.

We thank the Grid5000 team for their help to use the testbed. Grid5000 is supported by a scientific interest group (GIS) hosted by INRIA and including CNRS, RENATER and several Universities as well as other organizations.

Author information

Authors and Affiliations

  1. Umanis Research & Innovation, 7 Rue Paul Vaillant Couturier, 92300, Levallois-Perret, France

    Tarek Menouer

  2. Université Sorbonne Paris Nord, LIPN/CNRS UMR 7030, 93430, Villetaneuse, France

    Amina Khedimi & Christophe Cérin

  3. Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China

    Congfeng Jiang

Authors
  1. Tarek Menouer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amina Khedimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christophe Cérin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Congfeng Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tarek Menouer .

Editor information

Editors and Affiliations

  1. Columbia University, New York, NY, USA

    Meikang Qiu

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Menouer, T., Khedimi, A., Cérin, C., Jiang, C. (2020). Cloud Allocation and Consolidation Based on a Scalability Metric. In: Qiu, M. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science(), vol 12454. Springer, Cham. https://doi.org/10.1007/978-3-030-60248-2_26

Download citation

Publish with us

Policies and ethics

Search

Navigation