Skip to main content

Evaluation of Cloud Systems

  • Chapter
  • First Online:
Modeling and Simulation in HPC and Cloud Systems

Part of the book series: Studies in Big Data ((SBD,volume 36))

Abstract

Modelling and simulation represent suitable instruments for evaluation of distributed system. These essential tools in science are used in Cloud systems design and performance evaluation. The chapter covers the fundamental skills for a practitioner working in the field of Cloud Systems to have, for the development of a correct methodology for the evaluation using simulation of Cloud services and components. We concentrate on subjects related to tasks scheduling and resource allocation with the focus on scalability and elasticity, the constraints imposed by SLA and the use of CloudSim for performance evaluation of Cloud Systems. Several metrics used in modelling and simulation are presented in this chapter.

This work was presented during the event cHiPSet Training School “New Trends in Modeling and Simulation in HPC Systems” Bucharest, Romania, 21–23 September 2016, supported by cHiPSet ICT COST Action IC1406.

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

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover 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. Mell, P., Grance, T.: The NIST definition of cloud computing. Commun. ACM 53(6), 50 (2010)

    Google Scholar 

  2. Leinberger, W., Karypis, G., Kumar, V.: Multi-capacity bin packing algorithms with applications to job scheduling under multiple constraints. In: Proceedings of 1999 International Conference on Parallel Processing, pp. 404–412. IEEE (1999)

    Google Scholar 

  3. Song, W., Xiao, Z., Chen, Q., Luo, H.: Adaptive resource provisioning for the cloud using online bin packing. IEEE Trans. Comput. 63(11), 2647–2660 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  4. Kamali, S.: Efficient bin packing algorithms for resource provisioning in the cloud. In: Algorithmic Aspects of Cloud Computing, pp. 84–98. Springer (2016)

    Google Scholar 

  5. Qu, C., Calheiros, R.N., Buyya, R.: A reliable and cost-efficient auto-scaling system for web applications using heterogeneous spot instances. J. Netw. Comput. Appl. 65, 167–180 (2016)

    Article  Google Scholar 

  6. Herbst, N.R., Kounev, S., Reussner, R.: Elasticity in cloud computing: what it is, and what it is not. In: Proceedings of the 10th International Conference on Autonomic Computing (ICAC 13), pp. 23–27 (2013)

    Google Scholar 

  7. Vasile, M.A., Pop, F., Tutueanu, R.I., Cristea, V., Kołodziej, J.: Resource-aware hybrid scheduling algorithm in heterogeneous distributed computing. Future Gener. Comput. Syst. 51, 61–71 (2015)

    Article  Google Scholar 

  8. Vasile, M.A., Pop, F., Tutueanu, R.I., Cristea, V.: HySARC2: hybrid scheduling algorithm based on resource clustering in cloud environments. In: International Conference on Algorithms and Architectures for Parallel Processing, pp. 416–425. Springer (2013)

    Google Scholar 

  9. Sfrent, A., Pop, F.: Asymptotic scheduling for many task computing in big data platforms. Inf. Sci. 319, 71–91 (2015)

    Article  MathSciNet  Google Scholar 

  10. 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 (2016)

    Article  Google Scholar 

  11. Bardsiri, A.K., Hashemi, S.M.: Qos metrics for cloud computing services evaluation. Int. J. Intell. Syst. Appl. 6(12), 27 (2014)

    Google Scholar 

  12. Kan, S.H.: Metrics and Models in Software Quality Engineering. Addison-Wesley Longman Publishing Co., Inc. (2002)

    Google Scholar 

  13. Iosup, A., Ostermann, S., Yigitbasi, M.N., Prodan, R., Fahringer, T., Epema, D.: Performance analysis of cloud computing services for many-tasks scientific computing. IEEE Trans. Parallel Distrib. Syst. 22(6), 931–945 (2011)

    Article  Google Scholar 

  14. Topcuoglu, H., Hariri, S., Wu, M.Y.: Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002)

    Article  Google Scholar 

  15. Feitelson, D.G., Rudolph, L.: Metrics and benchmarking for parallel job scheduling. In: Workshop on Job Scheduling Strategies for Parallel Processing, pp. 1–24. Springer (1998)

    Google Scholar 

  16. Pop, F., Cristea, V., Bessis, N., Sotiriadis, S.: Reputation guided genetic scheduling algorithm for independent tasks in inter-clouds environments. In: 27th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 772–776. IEEE (2013)

    Google Scholar 

  17. Wu, L., Buyya, R.: Service Level Agreement (SLA) in Utility Computing Systems. IGI Global (2012)

    Google Scholar 

  18. Debusmann, M., Keller, A.: SLA-driven management of distributed systems using the common information model. In: Integrated Network Management VIII, pp. 563–576. Springer (2003)

    Google Scholar 

  19. Alhamad, M., Dillon, T., Chang, E.: SLA-based trust model for cloud computing. In: 13th International Conference on Network-Based Information Systems (NBIS), pp. 321–324. IEEE (2010)

    Google Scholar 

  20. Venticinque, S., Aversa, R., Di Martino, B., Rak, M., Petcu, D.: A cloud agency for SLA negotiation and management. In: European Conference on Parallel Processing, pp. 587–594. Springer (2010)

    Google Scholar 

  21. Sahai, A., Machiraju, V., Sayal, M., Van Moorsel, A., Casati, F.: Automated SLA monitoring for web services. In: International Workshop on Distributed Systems: Operations and Management, pp. 28–41. Springer (2002)

    Google Scholar 

  22. Goudarzi, H., Ghasemazar, M., Pedram, M.: SLA-based optimization of power and migration cost in cloud computing. In: 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID 2012), pp. 172–179. May 2012

    Google Scholar 

  23. Goudarzi, H., Pedram, M.: Multi-dimensional SLA-based resource allocation for multi-tier cloud computing systems. In: 2011 IEEE International Conference on Cloud Computing (CLOUD), pp. 324–331. IEEE (2011)

    Google Scholar 

  24. Dastjerdi, A.V., Tabatabaei, S.G.H., Buyya, R.: A dependency-aware ontology-based approach for deploying service level agreement monitoring services in cloud. Softw Pract. Experience 42(4), 501–518 (2012)

    Google Scholar 

  25. CCMBenchmark: JDN, CloudScreener, Cedexis US. Cloud benchmark website. http://www.journaldunet.com/us-cloud-benchmark (2016)

  26. Alhamad, M., Dillon, T., Chang, E.: Conceptual SLA framework for cloud computing. In: 4th IEEE International Conference on Digital Ecosystems and Technologies, pp. 606–610. IEEE (2010)

    Google Scholar 

  27. Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Experience 41(1), 23–50 (2011)

    Google Scholar 

Download references

Acknowledgements

The research presented in this paper is supported by the projects: DataWay: Real-time Data Processing Platform for Smart Cities: Making sense of Big Data—PN-II-RU-TE-2014-4-2731; MobiWay: Mobility Beyond Individualism: an Integrated Platform for Intelligent Transportation Systems of Tomorrow - PN-II-PT-PCCA-2013-4-0321; and cHiPSet: High-Performance Modelling and Simulation for Big Data Applications, ICT COST Action IC1406.

We would like to thank the reviewers for their time and expertise, constructive comments and valuable insight.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Florin Pop .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Vasile, MA., Iordache, GV., Tudorica, A., Pop, F. (2018). Evaluation of Cloud Systems. In: Kołodziej, J., Pop, F., Dobre, C. (eds) Modeling and Simulation in HPC and Cloud Systems. Studies in Big Data, vol 36. Springer, Cham. https://doi.org/10.1007/978-3-319-73767-6_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-73767-6_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73766-9

  • Online ISBN: 978-3-319-73767-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics