Skip to main content
SpringerLink
Log in

Online Bi-Objective Scheduling for IaaS Clouds Ensuring Quality of Service

  • Published:
Journal of Grid Computing Aims and scope Submit manuscript

Abstract

This paper focuses on a bi-objective experimental evaluation of online scheduling in the Infrastructure as a Service model of Cloud computing regarding income and power consumption objectives. In this model, customers have the choice between different service levels. Each service level is associated with a price per unit of job execution time, and a slack factor that determines the maximal time span to deliver the requested amount of computing resources. The system, via the scheduling algorithms, is responsible to guarantee the corresponding quality of service for all accepted jobs. Since we do not consider any optimistic scheduling approach, a job cannot be accepted if its service guarantee will not be observed assuming that all accepted jobs receive the requested resources. In this article, we analyze several scheduling algorithms with different cloud configurations and workloads, considering the maximization of the provider income and minimization of the total power consumption of a schedule. We distinguish algorithms depending on the type and amount of information they require: knowledge free, energy-aware, and speed-aware. First, to provide effective guidance in choosing a good strategy, we present a joint analysis of two conflicting goals based on the degradation in performance. The study addresses the behavior of each strategy under each metric. We assess the performance of different scheduling algorithms by determining a set of non-dominated solutions that approximate the Pareto optimal set. We use a set coverage metric to compare the scheduling algorithms in terms of Pareto dominance. We claim that a rather simple scheduling approach can provide the best energy and income trade-offs. This scheduling algorithm performs well in different scenarios with a variety of workloads and cloud configurations.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Ahmad, I., Ranka, S.: Handbook of energy-aware and green computing. Chapman & Hall/CRC (2012)

  2. Zomaya, Y., Lee, Y.: Energy efficient distributed computing systems. Wiley-IEEE Computer Society Press (2012)

  3. Lezama, A., Tchernykh, A., Yahyapour, R.: Performance evaluation of infrastructure as a service clouds with SLA constraints. Computación y Sistemas 17(3), 401–411 (2013)

    Google Scholar 

  4. Schwiegelshohn, U., Tchernykh, A.: Online scheduling for cloud computing and different service levels, pp 1067–1074. 26th Int. Parallel and Distributed Processing Symposium, Los Alamitos (2012)

    Google Scholar 

  5. Tchernykh, A., Pecero, J., Barrondo, A., Schaeffer, E.: Adaptive Energy Efficient Scheduling in Peer-to-Peer Desktop Grids. Futur. Gener. Comput. Syst. 36, 209–220 (2014)

    Article  Google Scholar 

  6. Raycroft, P., Jansen, R., Jarus, M., Brenner, P.: Performance bounded energy efficient virtual machine allocation in the global cloud. Sustainable Computing: Informatics and Systems 4(1), 1–9 (2014)

    Google Scholar 

  7. Khan, S., Ahmad, I.: A cooperative game theoretical technique for joint optimization of power consumption and response time in computational grids. Trans. Parallel Distrib. Syst. 20(3), 346–360 (2009)

    Article  MathSciNet  Google Scholar 

  8. Lee, Y., Zomaya, A.: Energy conscious scheduling for distributed computing systems under different operating conditions. IEEE Trans. Parallel Distrib. Syst. 22(8), 1374–1381 (2011)

    Article  Google Scholar 

  9. Mezmaz, M., Melab, N., Kessaci, Y., Lee, Y., Talbi, E., Zomaya, A., Tuyttens, D.: A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. J. Parallel Distrib. Comput. 71(11), 1497–1508 (2011)

    Article  Google Scholar 

  10. Pecero, J., Bouvry, P., Fraire, H., Khan, S.: A multi-objective GRASP algorithm for joint optimization of power consumption and schedule length of precedence-constrained applications. International Conference on Cloud and Green Computing, 1–8 (2011)

  11. Lindberg, P., Leingang, J., Lysaker, D., Khan, S., Li, J.: Comparison and analysis of eight scheduling heuristics for the optimization of power consumption and makespan in large-scale distributed systems. J. Supercomput. 59(1), 323–360 (2012)

    Article  Google Scholar 

  12. Nesmachnow, S., Dorronsoro, B., Pecero, J., Bouvry, P.: Energy-aware scheduling on multicore heterogeneous grid computing systems. J. Grid Comput. 11(4), 653–680 (2013)

    Article  Google Scholar 

  13. Iturriaga, S., Nesmachnow, S., Dorronsoro, B., Bouvry, P.: Energy efficient scheduling in heterogeneous systems with a parallel multiobjective local search. Comput. Inf. 32(2), 273–294 (2013)

    MathSciNet  Google Scholar 

  14. DasGupta, B., Palis, M.: Online real-time preemptive scheduling of jobs with deadlines on multiple machines. Scheduling 4(6), 297–312 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  15. Parallel Workload Archive [Online, November 2014]. Available at http://www.cs.huji.ac.il/labs/parallel/workload

  16. Grid Workloads Archive [Online, November 2014]. Available at http://gwa.ewi.tudelft.nl

  17. Ramírez, J.M., Tchernykh, A., Yahyapour, R., Schwiegelshohn, U., Quezada, A., González, J., Hirales, A.: Job allocation strategies with user run time estimates for online scheduling in hierarchical grids. J. Grid Comput. 9, 95–116 (2011)

    Article  Google Scholar 

  18. Quezada, A., Tchernykh, A., González, J., Hirales, A., Ramírez, J.-M., Schwiegelshohn, U., Yahyapour, R., Miranda, V.: Adaptive parallel job scheduling with resource admissible allocation on two-level hierarchical grids. Futur. Gener. Comput. Syst. 28(7), 965–976 (2012)

    Article  Google Scholar 

  19. Tchernykh, A., Lozano, L., Schwiegelshohn, U., Bouvry, P., Pecero, J. E., Nesmachnow, S.: Energy-aware online scheduling: ensuring quality of service for IaaS clouds, pp 911–918. International Conference on High Performance Computing & Simulation (HPCS 2014), Bologna (2014)

    Google Scholar 

  20. Zitzler, E.: Evolutionary algorithms for multiobjective optimization: methods and applications, PhD thesis. Swiss Federal Institute of Technology, Zurich (1999)

    Google Scholar 

  21. Tchernykh, A., Lozano, L., Schwiegelshohn, U., Bouvry, P., Pecero, J.E., Nesmachnow, S.: Bi-objective online scheduling with quality of service for IaaS clouds. In: 3rd IEEE international conference on cloud networking, Luxembourg (2014)

  22. Tchernykh, A., Schwiegelsohn, U., Yahyapour, R., Kuzjurin, N.: Online hierarchical job scheduling on grids with admissible allocation. J. Sched. 13(5), 545–552 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  23. Tchernykh, A., Ramírez, J., Avetisyan, A., Kuzjurin, N., Grushin, D., Zhuk, S.: Two level job-scheduling strategies for a computational grid. In: Wyrzykowski, R. et al. (eds.) Parallel processing and applied mathematics, 6th International conference on parallel processing and applied mathematics, LNCS 3911, pp 774–781. Springer, Poznan (2006)

  24. Tsafrir, D., Etsion, Y., Feitelson, D.: Backfilling using system-generated predictions rather than user runtime estimates. IEEE Trans. Parallel Distrib. Syst. 18(6), 789–803 (2007)

    Article  Google Scholar 

  25. Nesmachnow, S., Perfumo, C., Goiri, I.: Controlling datacenter power consumption while maintaining temperature and QoS levels. In: 3rd IEEE international conference on cloud networking. Luxembourg (2014)

  26. Dorronsoro, B., Nesmachnow, S., Taheri, J., Zomaya, A., Talbi, E-G., Bouvry, P.: A hierarchical approach for energy-efficient scheduling of large workloads in multicore distributed systems. Sustainable Computing: Informatics Systems 4, 252–261 (2014)

    Google Scholar 

  27. Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley, New York (2001)

    MATH  Google Scholar 

  28. Hirales, A., Tchernykh, A., Roblitz, T., Yahyapour, R.: A Grid simulation framework to study advance scheduling strategies for complex workflow applications. Parallel Distributed Processing. 2010 IEEE International Symposium on Workshops and Phd Forum (IPDPSW), pp 1–8 (2010)

Download references

Author information

Authors and Affiliations

  1. CICESE Research Center, Ensenada, Baja California, México

    Andrei Tchernykh & Luz Lozano

  2. TU Dortmund University, Dortmund, Germany

    Uwe Schwiegelshohn

  3. University of Luxembourg, Luxembourg, Belgium

    Pascal Bouvry & Johnatan E. Pecero

  4. Universidad de la República, Montevideo, Uruguay

    Sergio Nesmachnow

  5. Moscow Institute of Physics and Technology, Dolgoprudny, Russia

    Alexander Yu. Drozdov

Authors
  1. Andrei Tchernykh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luz Lozano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Uwe Schwiegelshohn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pascal Bouvry
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Johnatan E. Pecero
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sergio Nesmachnow
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Alexander Yu. Drozdov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrei Tchernykh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tchernykh, A., Lozano, L., Schwiegelshohn, U. et al. Online Bi-Objective Scheduling for IaaS Clouds Ensuring Quality of Service. J Grid Computing 14, 5–22 (2016). https://doi.org/10.1007/s10723-015-9340-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10723-015-9340-0

Keywords

Search

Navigation