Skip to main content

Advertisement

Springer Nature Link
Log in

Deadline-Constrained and Cost-Effective Multi-Workflow Scheduling with Uncertainty in Cloud Control Systems

  • Published:
Journal of Systems Science and Complexity Aims and scope Submit manuscript

Abstract

In cloud control systems, generating an efficient and economical workflow scheduling strategy for deadline-constrained workflow applications, especially in uncertain multi-workflow dynamic scheduling processes, is a crucial challenge. To optimize the total cost of workflow scheduling, the authors propose a cost-driven heuristic scheduling algorithm F-MWSA which consists of two phases: Fuzzy deadline distribution and fuzzy task scheduling. In the fuzzy deadline distribution phase, a new workflow deadline distribution strategy with fuzziness is designed to obtain the sub-deadline constraint of each task. The fuzzy task scheduling phase focuses on a cost-effective strategy to assign tasks to cloud resources, reducing multi-workflow scheduling costs. Performance evaluations on five real-world workflows demonstrate that the proposed F-MWSA outperforms the baseline policy in terms of total cost, success ratio, resource utilization, and makespan.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Whaiduzzaman M, Sookhak M, Gani A, et al., A survey on vehicular cloud computing, Journal of Network and Computer Applications, 2014, 40: 325–344.

    Article  Google Scholar 

  2. Xia Y, Cloud control systems, IEEE/CAA Journal of Automatica Sinica, 2015, 2(2): 134–142.

    Article  MathSciNet  Google Scholar 

  3. Li X and Cai Z, Elastic resource provisioning for cloud workflow application, IEEE Transactions on Automation Science and Engineering, 2015, 14(2): 1195–1210.

    Article  Google Scholar 

  4. Mishra K, Rajareddy G N V, Ghugar U, et al., A collaborative computation and offloading for compute-intensive and latency-sensitive dependency-aware tasks in dew-enabled vehicular fog computing: A federated deep Q-learning approach, IEEE Transactions on Network and Service Management, 2023, 20(3): 3220–3232.

    Google Scholar 

  5. Sahni J and Vidyarthi D P, A cost-effective deadline-constrained dynamic scheduling algorithm for scientific workflows in a cloud environment, IEEE Transactions on Cloud Computing, 2018, 6(1): 2–18.

    Article  Google Scholar 

  6. Chakravarthi K K, Shyamala L, and Vaidehi V, Cost-effective workflow scheduling approach on cloud under deadline constraint using firefly algorithm, Applied Intelligence, 2021, 51(3): 1629–1644.

    Article  Google Scholar 

  7. Zhang L, Li K, Li C, et al., Bi-objective workflow scheduling of the energy consumption and reliability in heterogeneous computing systems, Information Science, 2017, 379: 241–256.

    Article  Google Scholar 

  8. Gu Y and Budati C, Energy-aware workflow scheduling and optimization in clouds using bat algorithm, Future Generation Computer Systems, 2020, 113: 106–112.

    Article  Google Scholar 

  9. Cai S and Liu K, Heuristics for online scheduling on identical parallel machines with two GoS levels, Journal of Systems Science & Complexity, 2019, 32(4): 1180–1193.

    Article  MathSciNet  Google Scholar 

  10. Setlur A, Nirmala S J, Singh H S, et al., An efficient fault tolerant workflow scheduling approach using replication heuristics and checkpointing in the cloud, Journal of Parallel and Distributed Computing, 2020, 136: 14–28.

    Article  Google Scholar 

  11. Li Z, Yu H, Fan G, et al., Cost-efficient fault-tolerant workflow scheduling for deadline-constrained microservice-based applications in clouds, IEEE Transactions on Network and Service Management, 2023, 20(3): 3220–3232.

    Article  Google Scholar 

  12. Han P, Du C, Chen J, et al., Cost and makespan scheduling of workflows in clouds using list multiobjective optimization technique, Journal of Systems Architecture, 2021, 112: 101837.

    Article  Google Scholar 

  13. Arabnejad V, Bubendorfer K, and Ng B, Dynamic multi-workflow scheduling: A deadline and cost-aware approach for commercial clouds, Future Generation Computer Systems, 2019, 100: 98–108.

    Article  Google Scholar 

  14. Wu Q, Ishikawa F, Zhu Q, et al., Deadline-constrained cost optimization approaches for workflow scheduling in clouds, IEEE Transactions on Parallel and Distributed Systems, 2017, 28(12): 3401–3412.

    Article  Google Scholar 

  15. Zhu Z and Tang X, Deadline-constrained workflow scheduling in IaaS clouds with multi-resource packing, Future Generation Computer Sysytems, 2019, 101: 880–893.

    Article  Google Scholar 

  16. Chen Z, Zhan Z, Lin Y, et al., Multiobjective cloud workflow scheduling: A multiple populations ant colony system approach, IEEE Transactions on Cybernetics, 2019, 49(8): 2912–2926.

    Article  Google Scholar 

  17. Fan G, Chen X, Li Z, et al., An energy-efficient dynamic scheduling method of deadline-constrained workflows in a cloud environment, IEEE Transactions on Network and Service Management, 2023, 20(4): 3089–3103.

    Article  Google Scholar 

  18. Ye L, Xia Y, Yang L, et al., Dynamic scheduling stochastic multiworkflows with deadline constraints in clouds, IEEE Transactions on Automation Science and Engineering, 2023, 20(4): 2594–2606.

    Article  Google Scholar 

  19. Qin S, Pi D, Shao Z, et al., A discrete interval-based multiobjective memetic algorithm for scheduling workflow with uncertainty in cloud environment, IEEE Transactions on Network and Service Management, 2023, 20(3): 3020–3037.

    Article  Google Scholar 

  20. Chen H, Zhu X, Liu G, et al., Uncertainty-aware online scheduling for real-time workflows in cloud service environment, IEEE Transactions on Services Computing, 2021, 14(4): 1167–1178.

    Article  Google Scholar 

  21. Ye L, Xia Y, Yang L, et al., SHWS: Stochastic hybrid workflows dynamic scheduling in cloud container services, IEEE Transactions on Automation Science and Engineering, 2022, 19(3): 2620–2636.

    Article  Google Scholar 

  22. Liu J, Ren J, Dai W, et al., Online multi-workflow scheduling under uncertain task execution time in IaaS clouds, IEEE Transactions on Cloud Computing, 2021, 9(3): 1180–1194.

    Article  Google Scholar 

  23. Ma X, Hu H, Gao H, et al., Real-time multiple-workflow scheduling in cloud environments, IEEE Transactions on Network and Service Management, 2021, 18(4): 4002–4018.

    Article  Google Scholar 

  24. Ye L, Xia Y, Yang L, et al., A fuzzy scheduling strategy for online multi-workflows in IaaS clouds, Proceedings of the 41st Chinese Control Conference, 2022, 2428–2433.

  25. Zhu J, Li X, Ruiz R, et al., Scheduling periodical multi-stage jobs with fuzziness to elastic cloud resources, IEEE Transactions on Parallel and Distributed Systems, 2020, 31(12): 2819–2833.

    Article  Google Scholar 

  26. Abrishami S, Naghibzadeh M, and Epema D H, Deadline-constrained workflow scheduling algorithms for infrastructure as a service clouds, Future Generation Computer Systems, 2013, 29(1): 158–169.

    Article  Google Scholar 

  27. Sun Z, Zhang B, Gu C, et al., ET2FA: A hybrid heuristic algorithm for deadline-constrained workflow scheduling in clouds, IEEE Transactions on Services Computing, 2023, 16(3): 1807–1821.

    Google Scholar 

  28. Zhang P and Zhou M, Dynamic cloud task scheduling based on a two-stage strategy, IEEE Transactions on Automation Science and Engineering, 2017, 15(2): 772–783.

    Article  Google Scholar 

  29. Arabnejad V, Bubendorfer K, and Ng B, Budget and deadline aware e-science workflow scheduling in clouds, IEEE Transactions on Parallel and Distributed Systems, 2019, 30(1): 29–44.

    Article  Google Scholar 

  30. Shishido H Y, Estrella J C, Toledo C F M, et al., Genetic-based algorithms applied to a workflow scheduling algorithm with security and deadline constraints in clouds, Computers & Electrical Engineering, 2018, 69: 378–394.

    Article  Google Scholar 

  31. Li H, Wang D, Zhou M, et al., Multi-swarm co-evolution based hybrid intelligent optimization for bi-objective multi-workflow scheduling in the cloud, IEEE Transactions on Parallel and Distributed Systems, 2022, 13(9): 2183–2197.

    Article  Google Scholar 

  32. Yang L, Ye L, Xia Y, et al., Look-ahead workflow scheduling with width changing trend inclouds, Future Generation Computer Systems, 2023, 139: 139–150.

    Article  Google Scholar 

  33. Wen Z, Cala J, Watson P, et al., Cost effective, reliable and secure workflow deployment over federated clouds, IEEE Transactions on Services Computing, 2017, 10(6): 929–941.

    Article  Google Scholar 

  34. Sahni J and Vidyarthi D P, A cost-effective deadline-constrained dynamic scheduling algorithm for scientific workflows in a cloud environment, IEEE Transactions on Cloud Computing, 2018, 6(1): 2–19.

    Article  Google Scholar 

  35. Chen Z G, Zhan Z H, Lin Y, et al., Multiobjective cloud workflow scheduling: A multiple populations ant colony system approach, IEEE Transactions on Cybernetics, 2019, 49(8): 2912–2926.

    Article  Google Scholar 

  36. Ahmad T N, Saeid P, and Javid T, QoS-aware online scheduling of multiple workflows under task execution time uncertainty in clouds, Cluster Computing, 2022, 25: 3767–3784.

    Article  Google Scholar 

  37. Zadeh L A, Fuzzy sets, Information & Control, 1965, 8(3): 338–353.

    Article  MathSciNet  Google Scholar 

  38. Zadeh L A, The concept of linguistic variable and its application to approximate reasoning, Information Sciences, 1975, 8(3–4): 199–249, 301–357.

    Article  MathSciNet  Google Scholar 

  39. Lee E and Li R J, Comparison of fuzzy numbers based on the probability measure of fuzzy events, Computers & Mathematics with Applications, 1988, 15(10): 887–896.

    Article  MathSciNet  Google Scholar 

  40. Chen W and Deelman E, WorkflowSim: A toolkit for simulating scientific workflows in distributed, Proceeding of IEEE International Conference on E-Science, 2012, 1–8.

  41. Juve G, Chervenak A, Deelman E, et al., Characterizing and profiling scientific workflows, Future Generation Computer Systems, 2013, 29(3): 682–692.

    Article  Google Scholar 

  42. Ammari A C, Labidi W, Mnif F, et al., Firefly algorithm and learning-based geographical task scheduling for operational cost minimization in distributed green data centers, Neurocomputing, 2022, 490: 146–162.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Lingjuan Ye & Xinchao Zhao

  2. Key Laboratory of Mathematics and Information Networks, Beijing University of Posts and Telecommunications, Ministry of Education, Beijing, 100876, China

    Lingjuan Ye & Xinchao Zhao

  3. School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Liwen Yang

  4. School of Automation, Beijing Institute of Technology, Beijing, 100081, China

    Yuanqing Xia & Yufeng Zhan

Authors
  1. Lingjuan Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liwen Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuanqing Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yufeng Zhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xinchao Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lingjuan Ye.

Ethics declarations

The authors declare no conflict of interest.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant No. 62303066 and the Fundamental Research Funds for the Central Unverities under Grant No. 2023RC46.

This paper was recommended for publication by Editor LI Hongyi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, L., Yang, L., Xia, Y. et al. Deadline-Constrained and Cost-Effective Multi-Workflow Scheduling with Uncertainty in Cloud Control Systems. J Syst Sci Complex 37, 1861–1886 (2024). https://doi.org/10.1007/s11424-024-3431-6

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11424-024-3431-6

Keywords