Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Cost optimal scheduling in IaaS for dependent workload with particle swarm optimization

Abstract

Optimizing cloud provisioning for scientific workflow applications is a challenging problem, since the workflows generally contain dependency between tasks and require specific deadlines. Usually, cloud providers offer many options to the consumers. These options include the number of virtual machines, the type of each virtual machine and the purchasing method for each machine. Currently, cloud provisioning cost optimization is an active research topic. Most of this literature is concerned with task scheduling, cloud option selection, and cloud option selection for scientific workflow applications. However, research that attempts to find solutions which cover both cloud option selection and workflow task scheduling is very limited. In this paper, we focus on optimizing the cost of purchasing infrastructure-as-a-service cloud capabilities to achieve scientific work flow execution within the specific deadlines. The proposed system considers the number of purchased instances, instance types, purchasing options, and task scheduling as constraints in an optimization process. Particle swarm optimization augmented with a variable neighborhood search technique is used to find the optimal solution. Our approach finds the configurations of purchasing options with the optimum budget for a specified workflow application based on the required performance. The solutions from the proposed system show promising performance from the perspectives of the total cost and fitness convergence when compared with other state-of-the-art algorithms.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

References

  1. 1.

    Buyya R, Yeo C, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25(6):599–616

  2. 2.

    Bharathi S, Chervenak A, Deelman E, Mehta G, Su MH, Vahi K (2008) Characterization of scientific workflows. In: Proceedings of the 3rd workshop on workflows in support of large scale science, WORKS 08

  3. 3.

    Amazon Elastic Compute Cloud [Online]. http://aws.amazon.com/ec2/. 1 September 2011

  4. 4.

    Wu Q, Yun D, Lin W, Gu Y, Lin W, Liu Y (2013) On workflow scheduling for end-to-end performance optimization in distributed network environments. In: Job scheduling strategies for parallel processing. Springer, Berlin, pp 76–95

  5. 5.

    Liu K, Jin H, Chen J, Liu X, Yuan D, Yang Y (2010) A compromised-time-cost scheduling algorithm in SwinDeW-C for instance-intensive cost-constrained workflows on a cloud computing platform. Int J High Perform Comput Appl 24(4):445–456

  6. 6.

    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

  7. 7.

    Wu Z, Ni Z, Gu L, Liu X (2010) A revised discrete particle swarm optimization for cloud workflow scheduling. In: Proceedings of the 2010 international conference on computational intelligence and security, CIS

  8. 8.

    Kllapi H, Sitaridi E, Tsangaris MM, Ioannidis Y (2011) Schedule optimization for data processing flows on the cloud. In: Proceedings of the 2011 international conference on management of data, SIGMOD ’11

  9. 9.

    Byun E, Kee Y, Kim J, Maeng S (2011) Cost optimized provisioning of elastic resources for application workflows. Future Gener Comput Syst 27(8):1011–1026

  10. 10.

    Van den Bossche R, Vanmechelen K, Broeckhove J (2010) Cost-optimal scheduling in hybrid IaaS clouds for deadline constrained workloads. In: Proceedings of the 2010 IEEE 3rd international conference on cloud computing, CLOUD

  11. 11.

    Rahman M, Li X, Palit H (2011) Hybrid heuristic for scheduling data analytics workflow applications in hybrid cloud environment. In: Proceedings of the 2011 IEEE international symposium on parallel and distributed processing workshops and Phd forum, IPDPSW

  12. 12.

    Wu Z, Liu X, Ni Z, Yuan D, Yang Y (2012) A market-oriented hierarchical scheduling strategy in cloud workflow systems. J Supercomput 63(1):256–293

  13. 13.

    Abrishami S, Naghibzadeh M, Epema D (2013) Deadline-constrained workflow scheduling algorithms for infrastructure as a service clouds. Future Gener Comput Syst 29(1):158–169

  14. 14.

    Amazon EC2 Reserved Instances [Online]. http://aws.amazon.com/ec2/reserved-instances/. 12 September 2013

  15. 15.

    Li Q, Guo Y (2010) Optimization of resource scheduling in cloud computing. In: Proceedings of the 2010 12th international symposium on symbolic and numeric algorithms for scientific computing, SYNASC

  16. 16.

    Chaisiri S, Lee B, Niyato D (2012) Optimization of resource provisioning cost in cloud computing. IEEE Trans Serv Comput 5(2):164–177

  17. 17.

    Netjinda N, Sirinaovakul B, Achalakul T (2012) Cost optimization in cloud provisioning using particle swarm optimization. In: Proceedings of the 2012 9th international conference on electrical engineering/electronics, computer, telecommunications and information technology, ECTI-CON

  18. 18.

    Netjinda N, Achalakul T, Sirinaovakul B (2013) Cloud provisioning for workflow application with deadline using discrete PSO. ECTI Trans Comput Inf Technol 7(1):43–51

  19. 19.

    Chen R, Wang C (2011) Project scheduling heuristics-based standard PSO for task-resource assignment in heterogeneous grid. Abstr Appl Anal 2011:1–20

  20. 20.

    Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks

  21. 21.

    Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of the IEEE world congress on computational intelligence. The 1998 IEEE international conference on evolutionary computation

  22. 22.

    Clerc M, Kennedy J (2002) The particle swarm–explosion. Stability, and convergence in a multidimensional complex space. IEEE Trans Evolut Comput 6(1):58–73

  23. 23.

    Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of the 2002 congress on evolutionary computation, CEC ’02

  24. 24.

    Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of the 1999 congress on evolutionary computation

  25. 25.

    Hansen P, Mladenovic N, Perez JAM (2008) Variable neighborhood search: methods and applications. 4OR 6(4):319–360

Download references

Acknowledgments

This work was supported by the Thailand Research Fund through the Royal Golden Jubilee PhD Program (Grant No. PHD/0031/2553). The authors also acknowledge National e-Science Infrastructure Consortium for providing computing resources that have contributed to the research results reported in this paper (http://www.e-science.in.th).

Author information

Correspondence to Tiranee Achalakul.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Netjinda, N., Sirinaovakul, B. & Achalakul, T. Cost optimal scheduling in IaaS for dependent workload with particle swarm optimization. J Supercomput 68, 1579–1603 (2014). https://doi.org/10.1007/s11227-014-1126-9

Download citation

Keywords

  • Cloud computing
  • Cost optimization
  • Particle swarm optimization
  • Workflow scheduling
  • Deadline constraint
  • Variable neighborhood search