, Volume 96, Issue 11, pp 1059–1086 | Cite as

On the efficiency of several VM provisioning strategies for workflows with multi-threaded tasks on clouds

  • Marc E. FrincuEmail author
  • Stéphane Genaud
  • Julien Gossa


Cloud computing promises the delivery of on-demand pay-per-use access to unlimited resources. Using these resources requires more than a simple access to them as most clients have certain constraints in terms of cost and time that need to be fulfilled. Therefore certain scheduling heuristics have been devised to optimize the placement of client tasks on allocated virtual machines. The applications can be roughly divided in two categories: independent bag-of-tasks and workflows. In this paper we focus on the latter and investigate a less studied problem, i.e., the effect the virtual machine allocation policy has on the scheduling outcome. For this we look at how workflow structure, execution time, virtual machine instance type affect the efficiency of the provisioning method when cost and makespan are considered. To aid our study we devised a mathematical model for cost and makespan in case single or multiple instance types are used. While the model allows us to determine the boundaries for two of our extreme methods, the complexity of workflow applications calls for a more experimental approach to determine the general relation. For this purpose we considered synthetically generated workflows that cover a wide range of possible cases. Results have shown the need for probabilistic selection methods in case small and heterogeneous execution times are used, while for large homogeneous ones the best algorithm is clearly noticed. Several other conclusions regarding the efficiency of powerful instance types as compared to weaker ones, and of dynamic methods against static ones are also made.


Workflow scheduling Virtual machine provisioning Cloud computing Cost and makespan modeling 

Mathematics Subject Classification (2010)

68M14 68M20 



Work partially supported by the French ANR project SONGS 11-INFRA-13.


  1. 1.
    Bittencourt L, Madeira E (2011) Hcoc: a cost optimization algorithm for workflow scheduling in hybrid clouds. J Internet Serv Appl 2:207–227CrossRefGoogle Scholar
  2. 2.
    Bittencourt LF, Madeira ERM (2008) A performance-oriented adaptive scheduler for dependent tasks on grids. Concurr Comput Pract Exp 20(9):1029–1049CrossRefGoogle Scholar
  3. 3.
    Bobroff N, Kochut A, Beaty K (2007) Dynamic placement of virtual machines for managing sla violations. In: 10th IFIP/IEEE international symposium on integrated network management. IEEE, pp. 119–128Google Scholar
  4. 4.
    den Bossche RV, Vanmechelen K, Broeckhove J (2010) Cost-optimal scheduling in hybrid IaaS clouds for deadline constrained workloads. In: IEEE CLOUD, pp 228–235Google Scholar
  5. 5.
    Byun EK, Kee YS, Kim JS, Maeng S (2011) Cost optimized provisioning of elastic resources for application workflows. Future Gener Comput Syst 27(8):1011–1026CrossRefGoogle Scholar
  6. 6.
    Caron E, Desprez F, Muresan A, Suter F (2012) Budget constrained resource allocation for non-deterministic workflows on an iaas cloud. In: Xiang Y, Stojmenovic I, Apduhan B, Wang G, Nakano K, Zomaya A (eds) Algorithms and architectures for parallel processing. Lecture notes in computer science, vol 7439. Springer, Berlin, pp 186–201Google Scholar
  7. 7.
    Casanova H, Legrand A, Quinson M (2008) Simgrid: a generic framework for large-scale distributed experiments. In: Proceedings of the tenth international conference on computer modeling and simulation. UKSIM ’08IEEE Computer Society, Washington, DC, USA, pp 126–131Google Scholar
  8. 8.
    Deelman E, Singh G, Livny M, Berriman GB, Good J (2008) The cost of doing science on the cloud: the montage example. In: SuperComputing’08, p 50Google Scholar
  9. 9.
    Doğan A, Özgüner F (2005) Biobjective scheduling algorithms for execution time-reliability trade-off in heterogeneous computing systems*. Comput J 48(3):300–314CrossRefGoogle Scholar
  10. 10.
    Frincu Marc E, Genaud S, Gossa J (2014) On the efficiency of several VM provisioning strategies for workflows with multi-threaded tasks on clouds. Rapport de recherche RR-8449, INRIA.
  11. 11.
    Frincu M, Genaud S, Gossa J (2013) Comparing provisioning and scheduling strategies for workflows on clouds. In: IEEE workshop proceedings of 28th IEEE international parallel & distributed processing symposium, pp 2101–2110Google Scholar
  12. 12.
    Frîncu ME (2014) Scheduling highly available applications on cloud environments. Future Gener Comput Syst 32:138–153CrossRefGoogle Scholar
  13. 13.
    Google: Google compute engine pricing. Accessed 20 June 2013
  14. 14.
    Gu J, Hu J, Zhao T, Sun G (2012) A new resource scheduling strategy based on genetic algorithm in cloud computing environment. JCP 7(1):42–52Google Scholar
  15. 15.
    Gutierrez-Garcia JO, Sim KM (2012) A family of heuristics for agent-based elastic cloud bag-of-tasks concurrent scheduling. Future Gener Comput Syst 29(7):1682–1699CrossRefGoogle Scholar
  16. 16.
    Hwang E, Kim KH (2012) Minimizing cost of virtual machines for deadline-constrained mapreduce applications in the cloud. In: 2012 ACM/IEEE 13th international conference on grid computing (GRID), pp 130–138Google Scholar
  17. 17.
    Lin C, Lu S (2011) Scheduling scientific workflows elastically for cloud computing. In: 2011 IEEE international conference on cloud computing (CLOUD), pp 746–747Google Scholar
  18. 18.
    Liu K (2009) Scheduling algorithms for instance-intensive cloud workflows. Ph.D. thesis, University of Swinburne AustraliaGoogle Scholar
  19. 19.
    Lucas-Simarro JL, Moreno-Vozmediano R, Montero RS, Llorente IM (2013) Scheduling strategies for optimal service deployment across multiple clouds. Future Gener Comput Syst 29(6):1431–1441CrossRefGoogle Scholar
  20. 20.
    Mao M, Humphrey M (2011) Auto-scaling to minimize cost and meet application deadlines in cloud workflows. In: Proceedings of 2011 international conference for high performance computing, networking, storage and analysis, SC ’11. ACM, New York, NY, USA, pp 49:1–49:12Google Scholar
  21. 21.
    Mao M, Humphrey M (2012) A performance study on the vm startup time in the cloud. In: IEEE CLOUD’12, pp 423–430Google Scholar
  22. 22.
    Michon E, Gossa J, Genaud S (2012) Free elasticity and free CPU power for scientific workloads on IaaS clouds. In: 18th IEEE international conference on parallel and distributed systems. IEEE, Singapore.
  23. 23.
    Michon E, Gossa J, Genaud S, Frincu M, Burel A (2013) Porting grid applications to the cloud with schlouder. In: 2013 IEEE 5th international conference on Cloud computing technology and science (CloudCom), vol 1, pp 505–512Google Scholar
  24. 24.
    Mohammadi Fard H, Prodan R, Fahringer T (2012) A truthful dynamic workflow scheduling mechanism for commercial multi-cloud environments. IEEE Trans Parallel Distrib Syst(99), 1Google Scholar
  25. 25.
    Pandey S, Wu L, Guru S, Buyya R (2010) A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In: 2010 24th IEEE international conference on advanced information networking and applications (AINA), pp 400–407Google Scholar
  26. 26.
    Radulescu A, van Gemund A (2001) A low-cost approach towards mixed task and data parallel scheduling. In: International conference on parallel processing, pp 69–76Google Scholar
  27. 27.
    Sakellariou R, Zhao H, Tsiakkouri E, Dikaiakos MD (2007) Scheduling workflows with budget constraints. In: Gorlatch S, Danelutto M (eds) Integrated research in grid computing: CoreGrid series. Springer, BerlinGoogle Scholar
  28. 28.
    Tobita T, Kasahara H (2002) A standard task graph set for fair evaluation of multiprocessor scheduling algorithms. J Sched 5(5):379–394MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Tordsson J, Montero RS, Moreno-Vozmediano R, Llorente IM (2012) Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers. Future Gener Comput Syst 28(2):358–367CrossRefGoogle Scholar
  30. 30.
    Villegas D, Antoniou A, Sadjadi SM, Iosup A (2012) An analysis of provisioning and allocation policies for infrastructure-as-a-service clouds. Proceedings of the 2012 12th IEEE/ACM international symposium on cluster, cloud and grid computing (ccgrid 2012), CCGRID ’12IEEE Computer Society, Washington, DC, USA, pp 612–619Google Scholar
  31. 31.
    Wu Z, Liu X, Ni Z, Yuan D, Yang Y (2013) A market-oriented hierarchical scheduling strategy in cloud workflow systems. J Supercomput 63:256–293CrossRefGoogle Scholar
  32. 32.
    Zaman S, Grosu D (2013) Combinatorial auction-based allocation of virtual machine instances in clouds. J Parallel Distrib Comput 73(4):495–508CrossRefGoogle Scholar
  33. 33.
    Zhao H, Sakellariou R (2003) An experimental investigation into the rank function of the heterogeneous earliest finish time scheduling algorithm. In: Kosch H, Bszrmnyi L, Hellwagner H (eds) Euro-Par 2003 parallel processing, vol 2790. Lecture notes in computer scienceSpringer, Berlin, pp 189–194Google Scholar

Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  • Marc E. Frincu
    • 1
    Email author
  • Stéphane Genaud
    • 1
  • Julien Gossa
    • 1
  1. 1.ICube, UMR 7357, Université de Strasbourg, CNRSIllkirchFrance

Personalised recommendations