access icon free Energy-efficient workload allocation in fog-cloud based services of intelligent transportation systems using a learning classifier system

© The Institution of Engineering and Technology
Received 25/11/2019, Accepted 13/05/2020, Revised 22/04/2020, Published 13/05/2020

Nowadays, renewable energies have been considered as one of the important sources of energy supply in delay-sensitive fog computations in intelligent transportation systems due to their cheapness and availability. This study addresses the challenges of using renewable power supplies in delay-sensitive fogs and proposes an efficient workload allocation method based on a learning classifier system. The system dynamically learns the workload allocation policies between the cloud and the fog servers and then converges on the optimal allocation that fulfils the energy and delay requirements in the overall transportation system. Simulation results confirm that the proposed algorithm reduces the long-term costs of the system including service delay and operating costs. Also, compared to some other techniques, when the proposed method presents the most successful solution for reducing the average delay of the workloads and converging on the minimum value as well as retaining or even increasing the battery levels of fog nodes up to 100%. The lowest cost of the delay is 5 among other available methods, whereas in the proposed method, this value approaches 4.5.

Inspec keywords: learning (artificial intelligence); energy conservation; processor scheduling; power aware computing; cloud computing; resource allocation

Other keywords: delay-sensitive fog computations; fog servers; energy supply; system including service delay; fog nodes; renewable energies; intelligent transportation systems; average delay; transportation system; workload allocation policies; fog-cloud based services; energy-efficient workload allocation; optimal allocation; learning classifier system; delay-sensitive fogs; renewable power supplies; efficient workload allocation method

Subjects: Internet software; Optimisation techniques; Optimisation techniques; Knowledge engineering techniques

References

    1. 1)
      • 14. Xu, J., Chen, L., Ren, S.: ‘Online learning for offloading and autoscaling in energy harvesting mobile edge computing’, IEEE Trans. Cognitive Commun. Netw., 2017, 3, (3), pp. 361–373.
    2. 2)
      • 5. Zhang, W., Zhang, Z., Zeadally, S., et al: ‘Masm: a multiple-algorithm service model for energy-delay optimization in edge artificial intelligence’, IEEE Trans. Ind. Inf., 2019, 15, (7), pp. 4216–4224.
    3. 3)
      • 10. Shi, W., Cao, J., Zhang, Q., et al: ‘Edge computing: vision and challenges’, IEEE Internet Things J., 2016, 3, (5), pp. 637–646.
    4. 4)
      • 6. Xu, X., Liu, X., Qi, L., et al: ‘Energy-efficient virtual machine scheduling across cloudlets in wireless metropolitan area networks’, Mobile Netw. Appl., 2019, 25, pp. 1–15.
    5. 5)
      • 7. Kerrache, C.A., Ahmad, F., Ahmad, Z., et al: ‘Towards an efficient vehicular clouds using mobile brokers’. 2019 Int. Conf. on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia, 2019, pp. 1–5.
    6. 6)
      • 18. Wan, J., Chen, B., Wang, S., et al: ‘Fog computing for energy-aware load balancing and scheduling in smart factory’, IEEE Trans. Ind. Inf., 2018, 14, (10), pp. 4548–4556.
    7. 7)
      • 16. Kumar, A., Bansal, A.: ‘Software fault proneness prediction using genetic based machine learning techniques’. 2019 4th Int. Conf. on Internet of Things: Smart Innovation and Usages (IoT-SIU), Ghaziabad, India, 2019, pp. 1–5.
    8. 8)
      • 12. Islam, T., Hashem, M.: ‘A big data management system for providing real time services using fog infrastructure’. 2018 IEEE Symp. on Computer Applications & Industrial Electronics (ISCAIE), Penang, Malaysia., 2018, pp. 85–89.
    9. 9)
      • 13. Kanapram, D., Lamanna, G., Repetto, M.: ‘Exploring the trade-off between performance and energy consumption in cloud infrastructures’. 2017 Second Int. Conf. on Fog and Mobile Edge Computing (FMEC), Valencia, Spain, 2017, pp. 121–126.
    10. 10)
      • 8. Alam, T.: ‘Middleware implementation in cloud-manet mobility model for internet of smart devices’, arXiv preprint arXiv:1902.09744, 2019.
    11. 11)
      • 22. Wilson, S.W., ‘Classifier fitness based on accuracy’, Evol. Comput., 1995, 3, (2), pp. 149–175.
    12. 12)
      • 9. Yousefpour, A., Fung, C., Nguyen, T., et al: ‘All one needs to know about fog computing and related edge computing paradigms: a complete survey’, J. Syst. Archit., 2019, 98, pp. 289–330.
    13. 13)
      • 24. Karlsen, M.R., Moschoyiannis, S.: ‘Evolution of control with learning classifier systems’, Appl. Netw. Sci., 2018, 3, (1), p. 30.
    14. 14)
      • 23. Arif, M.H., Li, J., Iqbal, M., et al: ‘Sentiment analysis and spam detection in short informal text using learning classifier systems’, Soft Comput., 2018, 22, (21), pp. 7281–7291.
    15. 15)
      • 11. Wu, H., Chen, L., Shen, C., et al: ‘Online geographical load balancing for energy-harvesting Mobile edge computing’. 2018 IEEE Int. Conf. on Communications (ICC), Kansas City, MO, USA., 2018, pp. 1–6.
    16. 16)
      • 25. Sutton, R.S., Barto, A.G.: ‘Introduction to reinforcement learning’ (MIT Press, Cambridge, MA, USA., 1998), vol. 135.
    17. 17)
      • 20. Lanzi, P.L.: ‘Learning classifier systems: from foundations to applications’ (No. 1813) (Springer, Berlin, Heidelberg, 2000).
    18. 18)
      • 1. Al-Fuqaha, A., Guizani, M., Mohammadi, M., et al: ‘Internet of things: a survey on enabling technologies, protocols, and applications’, IEEE Commun. Surv. Tutor., 2015, 17, (4), pp. 2347–2376.
    19. 19)
      • 4. Keivani, A., Ghayoor, F., Tapamo, J.-R.: ‘Collaborative mobile edge computing in Ev2x: a solution for low-cost driver assistance systems’, Wirel. Pers. Commun., 2019, pp. 1–14, Available at: https://doi.org/10.1007/s11277-019-06401-2.
    20. 20)
      • 21. Holland, J., Booker, L., Colombetti, M.,: ‘What Is a learning classifier system?’, in Lanzi, P., et al (Eds.): ‘Learning classifier systems’ (Springer, Berlin, Heidelberg, 2000), pp. 3–32.
    21. 21)
      • 19. Zeng, D., Gu, L., Yao, H.: ‘Towards energy efficient service composition in green energy powered cyber–physical fog systems’, Future Gener. Comput. Syst., 2018, 105, pp. 757–765.
    22. 22)
      • 17. Xu, J., Ren, S.: ‘Online learning for offloading and autoscaling in renewable-powered Mobile edge computing’. 2016 IEEE Global Communications Conf. (GLOBECOM), Washington, DC, USA., 2016, pp. 1–6.
    23. 23)
      • 15. Bartin, B.: ‘Use of learning classifier systems in microscopic toll plaza simulation models’, IET Intell. Transp. Syst., 2019, 13, (5), pp. 860–869.
    24. 24)
      • 3. Ahmed, Z.E., Saeed, R.A., Mukherjee, A.: ‘Challenges and opportunities in vehicular cloud computing’, Cloud Security: Concepts, Methodologies, Tools, and Applications, (IGI Global, 2019), pp. 2168–2185.
    25. 25)
      • 2. Zhang, W., Zhang, Z., Chao, H.-C.: ‘Cooperative fog computing for dealing with big data in the internet of vehicles: architecture and hierarchical resource management’, Commun. Mag., 2017, 55, (12), pp. 60–67.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2019.0783
Loading

Related content

content/journals/10.1049/iet-its.2019.0783
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading