The Journal of Supercomputing

, Volume 73, Issue 7, pp 2896–2918 | Cite as

Dealing with structural changes on provisioning resources for deadline-constrained workflow

  • Fairouz Fakhfakh
  • Hatem Hadj Kacem
  • Ahmed Hadj Kacem
Article
  • 168 Downloads

Abstract

Cloud computing has received an increasing attention in the past years thanks to its new model of resources provisioning. One well-known challenge in this context is to make an appropriate decision when mapping tasks to resources considering multiple objectives that are often contradictory. This problem has become more complex, mainly for workflow applications which impose dependencies and order constraints between tasks. The resources provisioning problem for workflow applications has been widely studied in the literature. Nevertheless, the existing works consider only static workflows. They neglect the need to change workflow instances while they are being executed. This functionality has become a major requirement to deal with unusual situations and evolution. In fact, the strong competition in which companies are involved often lead them to adapt their workflows to face new regulation laws, changes in the customer behavior and some exceptional situations. In this paper, we present a new provisioning algorithm based on the meta-heuristic optimization technique, particle swarm optimization. It takes into account dynamic structural changes of a workflow, while satisfying some performance criteria defined by the user. We address general flow structures including sequential, parallel, choice and loop patterns. We conducted our experiments using CloudSim and various well-known scientific workflows of different sizes. Experimental results show that our approach has a promising performance.

Keywords

Workflow Structural changes Need to change Cloud computing Provisioning 

References

  1. 1.
    Abrishami S, Naghibzadeh M, Epema D (2012) Cost-driven scheduling of grid workflows using partial critical paths. IEEE Trans Parallel Distrib Syst 23(8):1400–1414CrossRefGoogle Scholar
  2. 2.
    Abrishami S, Naghibzadeh M, Epema DJ (2013) Deadline-constrained workflow scheduling algorithms for infrastructure as a service clouds. Future Gener Comput Syst 29(1):158–169CrossRefGoogle Scholar
  3. 3.
    Adams M (2010) Dynamic workflow. In: Modern business process automation. Springer, pp 123–145Google Scholar
  4. 4.
    Adams M, ter Hofstede AM, van der Aalst WP, Edmond D (2007) Dynamic, extensible and context-aware exception handling for workflows. OTM Conf 1:95–112Google Scholar
  5. 5.
    Bessai K, Youcef S, Oulamara A, Godart C, Nurcan S (2012) Bi-criteria workflow tasks allocation and scheduling in cloud computing environments. In: IEEE Cloud, pp 638–645Google Scholar
  6. 6.
    Byun E, Kee Y, Kim J, Maeng S (2011) Cost optimized provisioning of elastic resources for application workflows. Future Gener Comput Syst 27(8):1011–1026CrossRefGoogle Scholar
  7. 7.
    Cai Z, Li X, Chen L, Gupta JND (2013) Bi-direction Adjust Heuristic for Workflow Scheduling in Clouds. In: Proceedings of the 19th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, Seoul, Korea, pp 94–101Google Scholar
  8. 8.
    Calheiros RN, Ranjan R, Beloglazov A, De Rose CAF, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50CrossRefGoogle Scholar
  9. 9.
    Caron E, Desprez F, Muresan A, Suter F (2012) Budget constrainedresource allocation for non-deterministic workflows on an iaascloud. In: Proceedings of the 12th International Conference on Algorithms and Architectures for Parallel Processing-Volume Part I, ICA3PP’12, Springer-Verlag, Berlin, Heidelberg, pp 186–201Google Scholar
  10. 10.
    Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evolut Comput 6(2):182–197CrossRefGoogle Scholar
  11. 11.
    Deelman E (2010) Grids and clouds: making workflow applications work in heterogeneous distributed environments. Int J High Perform Comput Appl 24(3):284–298CrossRefGoogle Scholar
  12. 12.
    Deelman E, Gannon D, Shields M, Taylor I (2009) Workflows and e-science: an overview of workflow system features and capabilities. Future Gener Comput Syst 25(5):528–540CrossRefGoogle Scholar
  13. 13.
    Fakhfakh F, Hadj-Kacem H, Hadj-Kacem A (2014) Workflow schedulingin cloud computing: a survey. In: Proceedings of the 18th International Conference on Enterprise Distributed Object Computing Conference Workshops, EDOC, IEEE, Ulm, Germany, pp 372–378Google Scholar
  14. 14.
    Fakhfakh F, Hadj-Kacem H, Hadj-Kacem A (2015) A provisioning approach of cloud resources for dynamic workflows. In: Proceedings of the 8th IEEE International Conference on Cloud Computing (IEEE CLOUD), IEEE, New York City, USA, pp 469–476Google Scholar
  15. 15.
    Juve G, Chervenak A, Deelman E, Bharathi S, Mehta G, Vahi K (2013) Characterizing and profiling scientific workflows. Future Gener Comput Syst 29(3):682–692CrossRefGoogle Scholar
  16. 16.
    Juve G, Deelman E, Vahi K, Mehta G, Berriman B (2009) Scientific workflow applications on Amazon EC2. In: Cloud Computing Workshop in Conjunction with e-Science. IEEEGoogle Scholar
  17. 17.
    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol 4, pp 1942–1948Google Scholar
  18. 18.
    Kim H, el Khamra Y, Jha S, Parashar M (2010) Exploring application and infrastructure adaptation on hybrid grid-cloud infrastructure.In: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing, HPDC ’10. ACM, NewYork, NY, USA, pp 402–412Google Scholar
  19. 19.
    Lee YC, Han H, Zomaya AY, Yousif M (2015) Resource-efficient workflow scheduling in clouds. Knowl Based Syst 80:153–162CrossRefGoogle Scholar
  20. 20.
    Malawski M, Figiela K, Bubak M, Deelman E, Nabrzyski J (2015) Scheduling multilevel deadline-constrained scientific workflows on clouds based on cost optimization. Scientific ProgrammingGoogle Scholar
  21. 21.
    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, USAGoogle Scholar
  22. 22.
    Müller R, Greiner U, Rahm E (2004) AGENTWORK: a workflow system supporting rule-based workflow adaptation. Data Knowl Eng 51(2):223–256CrossRefGoogle Scholar
  23. 23.
    Pandey S, Wu L, Guru SM, Buyya R (2010) A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments. In: Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications, AINA ’10. Washington, DC, USA, pp 400–407Google Scholar
  24. 24.
    Poola D, Garg SK, Buyya R, Yang Y, Ramamohanarao K (2014) Robust Scheduling of Scientific Workflows with Deadline and Budget Constraints in Clouds. In: Proceedings of the 28th International Conference on Advanced Information Networking and Applications, AINA. IEEE, Victoria, BC, Canada, pp 858–865Google Scholar
  25. 25.
    Rahman M, Li X, Palit HN (2011) Hybrid heuristic for scheduling data analytics workflow applications in hybrid cloud environment. In: Proceedings of the 25th IEEE International Symposium on Parallel and Distributed, IPDPS Workshops. IEEE, Anchorage, Alaska, USA, pp 966–974Google Scholar
  26. 26.
    Rodriguez MA, Buyya R (2014) Deadline based resource provisioningand scheduling algorithm for scientific workflows on clouds. IEEE Trans Cloud Comput 2(2):222–235CrossRefGoogle Scholar
  27. 27.
    Salman A, Ahmad I, Al-Madani S (2002) Particle swarm optimization for task assignment problem. Microprocess Microsyst 26(8):363–371CrossRefGoogle Scholar
  28. 28.
    Urban S, Gao L, Shrestha R, Courter A (2011) The dynamics ofprocess modeling: new directions for the use of events and rules inservice-oriented computing. In: Kaschek R, Delcambre L (eds) The evolution of conceptual modeling, lecture notes in computerscience, vol 6520. Springer, Berlin Heidelberg, pp 205–224Google Scholar
  29. 29.
    Wu Z, Ni Z, Gu L, Liu X (2010) A revised discrete particle swarm optimization for cloud workflow scheduling. In: Proceedings of the Sixth International Conference on Computational Intelligence and Security, CIS’2010. IEEE, Nanning, China, pp 184–188Google Scholar
  30. 30.
    Yu J, Sheng QZ, Swee JKY, Han J, Liu C, Noor TH (2015) Model-driven development of adaptive web service processes with aspects and rules. J Comput Syst Sci 81(3):533–552CrossRefGoogle Scholar
  31. 31.
    Zhou C, Garg SK (2015) Performance analysis of scheduling algorithms for dynamic workflow applications. In: Proceedings of the 4th International Congress on Big Data, Big Data’4. IEEE, New York City, USA, pp 222–229Google Scholar
  32. 32.
    Zhu Q, Agrawal G (2012) Resource provisioning with budget constraints for adaptive applications in cloud environments. IEEE Trans Serv Comput 5(4):497–511CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Fairouz Fakhfakh
    • 1
  • Hatem Hadj Kacem
    • 1
  • Ahmed Hadj Kacem
    • 1
  1. 1.ReDCAD LaboratoryFSEGS, University of SfaxSfaxTunisia

Personalised recommendations