Skip to main content
SpringerLink
Log in

A Memetic Genetic Algorithm for Optimal IoT Workflow Scheduling

  • Conference paper
  • First Online:
Applications of Evolutionary Computation (EvoApplications 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13989))

Abstract

Internet of Things (IoT) devices have become a crucial part of daily life. Because IoT devices often have small processing capability and low power supply, two popular technologies, i.e. cloud servers and fog edges, are increasingly integrated with IoT for workflow execution, giving rise to the resource allocation and workflow scheduling problem in hybrid IoT environments, i.e. the IoT workflow scheduling (IoTWS) problem. To tackle this NP-hard IoTWS problem, a new Genetic Algorithm (GA) called IoTGA has been successfully developed in this paper. In comparison to state-of-the-art GA approaches from literature, IoTGA allows fast workflow execution and can explicitly reduce the time and energy consumption thanks to its use of a newly designed local search method. Experiments on benchmark IoTWS problems clearly indicate that IoTGA can significantly outperform several competing GA methods and are more useful in practice.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Abd Elaziz, M., Abualigah, L., Ibrahim, R.A., Attiya, I.: Iot workflow scheduling using intelligent arithmetic optimization algorithm in fog computing. In: Computational Intelligence and Neuroscience 2021 (2021)

    Google Scholar 

  2. Abualigah, L., Diabat, A., Elaziz, M.A.: Intelligent workflow scheduling for big data applications in IoT cloud computing environments. Cluster Comput. 24(4), 2957–2976 (2021)

    Article  Google Scholar 

  3. Aburukba, R.O., AliKarrar, M., Landolsi, T., El-Fakih, K.: Scheduling internet of things requests to minimize latency in hybrid fog-cloud computing. Future Gen. Comput. Syst. 111, 539–551 (2020)

    Article  Google Scholar 

  4. Aburukba, R.O., Landolsi, T., Omer, D.: A heuristic scheduling approach for fog-cloud computing environment with stationary IoT devices. J. Network Comput. Appl. 180, 102994 (2021)

    Article  Google Scholar 

  5. Ahmad, Z., et al.: Scientific workflows management and scheduling in cloud computing: taxonomy, prospects, and challenges. IEEE Access 9, 53491–53508 (2021)

    Article  Google Scholar 

  6. Alsurdeh, R., Calheiros, R.N., Matawie, K.M., Javadi, B.: Hybrid workflow provisioning and scheduling on edge cloud computing using a gradient descent search approach. In: 2020 19th International Symposium on Parallel and Distributed Computing (ISPDC), pp. 68–75. IEEE (2020)

    Google Scholar 

  7. Chen, X., Cai, Y., Shi, Q., Zhao, M., Champagne, B., Hanzo, L.: Efficient resource allocation for relay-assisted computation offloading in mobile-edge computing. IEEE Internet Things J. 7(3), 2452–2468 (2019)

    Article  Google Scholar 

  8. Eiben, A., Smith, J.: Introduction to Evolutionary Computing (Natural Computing Series). Springer, Heidelberg (2008)

    MATH  Google Scholar 

  9. Ghobaei-Arani, M., Souri, A., Rahmanian, A.A.: Resource management approaches in fog computing: a comprehensive review. J. Grid Comput. 18(1), 1–42 (2020)

    Article  Google Scholar 

  10. Goudarzi, M., Wu, H., Palaniswami, M., Buyya, R.: An application placement technique for concurrent IoT applications in edge and fog computing environments. IEEE Trans. Mob. Comput. 20(4), 1298–1311 (2020)

    Article  Google Scholar 

  11. Knuth, D.: Number of Internet of Things (IoT) connected devices worldwide from 2019 to 2021, with forecasts from 2022 to 2030 kernel description. https://www.statista.com/statistics/1183457/iot-connected-devices-worldwide/. Accessed 30 Sept 2010

  12. Laroui, M., Nour, B., Moungla, H., Cherif, M.A., Afifi, H., Guizani, M.: Edge and fog computing for IoT: a survey on current research activities & future directions. Comput. Commun. 180, 210–231 (2021)

    Article  Google Scholar 

  13. Li, S., Zhai, D., Du, P., Han, T.: Energy-efficient task offloading, load balancing, and resource allocation in mobile edge computing enabled IoT networks. Sci. China Inf. Sci. 62(2), 1–3 (2019)

    Article  Google Scholar 

  14. Li, Z., Ge, J., Hu, H., Song, W., Hu, H., Luo, B.: Cost and energy aware scheduling algorithm for scientific workflows with deadline constraint in clouds. IEEE Trans. Serv. Comput. 11(4), 713–726 (2015)

    Article  Google Scholar 

  15. Liu, Y., et al.: Dependency-aware task scheduling in vehicular edge computing. IEEE Internet Things J. 7(6), 4961–4971 (2020)

    Article  Google Scholar 

  16. Miao, Y., Wu, G., Li, M., Ghoneim, A., Al-Rakhami, M., Hossain, M.S.: Intelligent task prediction and computation offloading based on mobile-edge cloud computing. Fut. Gener. Comput. Syst. 102, 925–931 (2020)

    Article  Google Scholar 

  17. Mohammadi, S., Pedram, H., PourKarimi, L.: Integer linear programming-based cost optimization for scheduling scientific workflows in multi-cloud environments. J. Supercomput. 74(9), 4717–4745 (2018). https://doi.org/10.1007/s11227-018-2465-8

    Article  Google Scholar 

  18. Mokni, M., Yassa, S., Hajlaoui, J.E., Chelouah, R., Omri, M.N.: Cooperative agents-based approach for workflow scheduling on fog-cloud computing. J. Ambient Intell. Hum. Comput. 1–20 (2021)

    Google Scholar 

  19. Pan, L., Liu, X., Jia, Z., Xu, J., Li, X.: A multi-objective clustering evolutionary algorithm for multi-workflow computation offloading in mobile edge computing. IEEE Trans. Cloud Comput. (2021)

    Google Scholar 

  20. Sriraghavendra, M., Chawla, P., Wu, H., Gill, S.S., Buyya, R.: DoSP: a deadline-aware dynamic service placement algorithm for workflow-oriented IoT applications in fog-cloud computing environments. In: Tiwari, R., Mittal, M., Goyal, L.M. (eds.) Energy Conservation Solutions for Fog-Edge Computing Paradigms. LNDECT, vol. 74, pp. 21–47. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-3448-2_2

    Chapter  Google Scholar 

  21. Sulaiman, M., Halim, Z., Lebbah, M., Waqas, M., Tu, S.: An evolutionary computing-based efficient hybrid task scheduling approach for heterogeneous computing environment. J. Grid Comput. 19(1), 1–31 (2021)

    Article  Google Scholar 

  22. Tahsien, S.M., Karimipour, H., Spachos, P.: Machine learning based solutions for security of internet of things (IoT): a survey. J. Network Comput. Appl. 161, 102630 (2020)

    Article  Google Scholar 

  23. Tan, B., Ma, H., Mei, Y.: A group genetic algorithm for resource allocation in container-based clouds. In: Paquete, L., Zarges, C. (eds.) EvoCOP 2020. LNCS, vol. 12102, pp. 180–196. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-43680-3_12

    Chapter  Google Scholar 

  24. Wu, H., Knottenbelt, W.J., Wolter, K.: An efficient application partitioning algorithm in mobile environments. IEEE Trans. Parallel Distrib. Syst. 30(7), 1464–1480 (2019)

    Article  Google Scholar 

  25. Xing, L., Zhang, M., Li, H., Gong, M., Yang, J., Wang, K.: Local search driven periodic scheduling for workflows with random task runtime in clouds. Comput. Ind. Eng. 168, 108033 (2022)

    Article  Google Scholar 

  26. Yang, L., Cao, J., Yuan, Y., Li, T., Han, A., Chan, A.: A framework for partitioning and execution of data stream applications in mobile cloud computing. ACM SIGMETRICS Perform. Eval. Rev. 40(4), 23–32 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Victoria University at Wellington, Kelburn, Wellington, 6012, New Zealand

    Amer Saeed, Gang Chen & Hui Ma

  2. RMIT, Melbourne, VIC, 3000, Australia

    Qiang Fu

Authors
  1. Amer Saeed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hui Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qiang Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amer Saeed .

Editor information

Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    João Correia

  2. University of York, York, UK

    Stephen Smith

  3. Al Hussein Technical University, Amman, Jordan

    Raneem Qaddoura

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Saeed, A., Chen, G., Ma, H., Fu, Q. (2023). A Memetic Genetic Algorithm for Optimal IoT Workflow Scheduling. In: Correia, J., Smith, S., Qaddoura, R. (eds) Applications of Evolutionary Computation. EvoApplications 2023. Lecture Notes in Computer Science, vol 13989. Springer, Cham. https://doi.org/10.1007/978-3-031-30229-9_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-30229-9_36

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30228-2

  • Online ISBN: 978-3-031-30229-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation