Task scheduling in cloud-fog computing systems

Abstract

Fog computing extends cloud services to the edge of the network. In such scenario, it is necessary to decide where applications should be executed so that their quality of service requirements can be supported. Thus, a cloud-fog system requires an efficient task scheduler to decide the locality where applications should run. This paper presents two schedulers based on integer linear programming, that schedule tasks either in the cloud or on fog resources. The schedulers differ from existing ones by the use of class of services to select the processing elements on which the tasks should be executed. Numerical results evince that the proposed schedulers outperform traditional ones, e.g., Random and Round Robin algorithms without causing violation of QoS requirements.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. 1.

    OpenFog Reference Architecture: OpenFog Consortium. Available: https://www.openfogconsortium.org/ra/ [Accessed: 24/05/2017]

  2. 2.

    Aazam M, Huh E (2015) Dynamic resource provisioning through fog micro datacenter. In: 2015 IEEE international conference on pervasive computing and communication workshops (PerCom workshops), pp 105–110

  3. 3.

    Aazam M, Huh EN (2015) Fog computing micro datacenter based dynamic resource estimation and pricing model for IoT. In: 2015 IEEE 29th international conference on advanced information networking and applications, pp 687–694

  4. 4.

    Agarwal S, Yadav S, Yadav A (2016) An efficient architecture and algorithm for resource provisioning in fog computing. Int J Inf Eng Elec Bus 8:48–61

    Google Scholar 

  5. 5.

    Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286 (5439):509–512

    MathSciNet  Article  Google Scholar 

  6. 6.

    Batista DM, da Fonseca NLS, Miyazawa FK, Granelli F (2008) Self-adjustment of resource allocation for grid applications. Comput Netw 52(9):1762–1781

    Article  Google Scholar 

  7. 7.

    Batista DM, Fonseca NLSd (2011) Robust scheduler for grid networks under uncertainties of both application demands and resource availability. Comput Netw 55(1):3–19

    Article  Google Scholar 

  8. 8.

    Batista DM, Fonseca NLSd, Granelli F, Kliazovich D (2007) Self-adjusting grid networks. In: 2007 IEEE international conference on communications, pp 344–349

  9. 9.

    Bittencourt LF, Diaz-Montes J, Buyya R, Rana OF, Parashar M (2017) Mobility-aware application scheduling in fog computing. IEEE Cloud Comput 4(2):26–35

    Article  Google Scholar 

  10. 10.

    Bittencourt LF, Goldman A, Madeira ERM, da Fonseca NLS, Sakellariou R (2019) Scheduling in distributed systems: A cloud computing perspective. arXiv:1901.03270

  11. 11.

    Bittencourt LF, Madeira ERM, da Fonseca NLS (2015) Resource management and scheduling. In: Fonseca NLSd, Boutaba R (eds) Cloud services, networking, and management. Wiley, pp 243–267

  12. 12.

    Bittencourt LF, Madeira ERM, Fonseca NLSD (2012) Scheduling in hybrid clouds. IEEE Commun Mag 50(9):42–47

    Article  Google Scholar 

  13. 13.

    Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Proceedings of the first edition of the MCC wrkshop on mobile cloud computing, MCC’12. ACM, New York, pp 13–16

  14. 14.

    Buttazzo G (2011) Hard real-time computing systems: Predictable scheduling algorithms and applications, 3rd edn. Real-Time Systems Series. 3rd edn. Springer US

  15. 15.

    Cheng N, Lyu F, Quan W, Zhou C, He H, Shi W, Shen X (2019) Space/aerial-assisted computing offloading for IoT applications: A learning-based approach. IEEE J Select Areas Commun 37(5):1117–1129

    Article  Google Scholar 

  16. 16.

    Deng R, Lu R, Lai C, Luan TH (2015) Towards power consumption-delay tradeoff by workload allocation in cloud-fog computing. In: 2015 IEEE international conference on communications (ICC), pp 3909–3914

  17. 17.

    Fonseca NLSd, Boutaba R (2015) (Org.). Cloud services, networking, and management, 1st edn. Wiley, Hoboken

    Google Scholar 

  18. 18.

    Guevara JC, Bittencourt LF, Fonseca NLSd (2017) Class of service in fog computing. In: 2017 IEEE 9th Latin-American conference on communications (LATINCOM), pp 1–6

  19. 19.

    Gupta H, Dastjerdi AV, Ghosh SK, Buyya R (2016) iFogSim: A toolkit for modeling and simulation of resource management techniques in internet of things, edge and fog computing environments. arXiv:1606.02007 [cs]

  20. 20.

    Intharawijitr K, Iida K, Koga H (2016) Analysis of fog model considering computing and communication latency in 5G cellular networks. In: 2016 IEEE international conference on pervasive computing and communication workshops (PerCom workshops), pp 1–4

  21. 21.

    Kertesz A, Pflanzner T, Gyimothy T (2018) A mobile IoT device simulator for IoT-fog-cloud systems. J Grid Comput 17(3):529–551

    Article  Google Scholar 

  22. 22.

    Khajemohammadi H, Fanian A, Gulliver T (2014) Efficient workflow scheduling for grid computing using a leveled multi-objective genetic algorithm. J Grid Comput 12:637–663

    Article  Google Scholar 

  23. 23.

    Kotb Y, Al Ridhawi I, Aloqaily M, Baker T, Jararweh Y, Tawfik H (2019) Cloud-based multi-agent cooperation for IoT devices using workflow-nets. J Grid Comput 17(4):625–650

    Article  Google Scholar 

  24. 24.

    Medina A, Lakhina A, Matta I, Byers J (2001) BRITE: Universal Topology Generation from a Users Perspective. Tech. rep., Boston University, Boston, MA, USA

  25. 25.

    Mouradian C, Naboulsi D, Yangui S, Glitho RH, Morrow MJ, Polakos PA (2018) A comprehensive survey on fog computing: State-of-the-art and research challenges. IEEE Commun Surv Tutor 20 (1):416–464

    Article  Google Scholar 

  26. 26.

    Oueis J, Strinati EC, Barbarossa S (2015) The fog balancing: Load distribution for small cell cloud computing. In: 2015 IEEE 81st vehicular technology conference (VTC Spring), pp 1–6

  27. 27.

    Pham XQ, Huh EN (2016) Towards task scheduling in a cloud-fog computing system. In: 2016 18th Asia-Pacific network operations and management symposium (APNOMS), pp 1–4

  28. 28.

    Pinedo ML (2012) Scheduling: Theory, algorithms, and systems, 4th edn. Springer-Verlag, New York

    Google Scholar 

  29. 29.

    Ren Z, Lu T, Wang X, Guo W, Liu G, Chang S (2020) Resource scheduling for delay-sensitive application in three-layer fog-to-cloud architecture. Peer-to-Peer Netw Appl 13(5):1474–1485

    Article  Google Scholar 

  30. 30.

    Riya, Gupta N, Dhurandher SK (2020) Efficient caching method in fog computing for internet of everything. Peer-to-Peer Netw Appl

  31. 31.

    Wang K, Yin H, Quan W, Min G (2018) Enabling collaborative edge computing for software defined vehicular networks. IEEE Netw 32(5):112–117

    Article  Google Scholar 

  32. 32.

    Wang S, Li K, Mei J, Xiao G, Li K (2017) A reliability-aware task scheduling algorithm based on replication on heterogeneous computing systems. J Grid Comput 15(1):23–39

    Article  Google Scholar 

  33. 33.

    Zeng D, Gu L, Guo S, Cheng Z, Yu S (2016) Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Trans Comput PP (99):1–1

    MathSciNet  MATH  Google Scholar 

  34. 34.

    Zhang G, Shen F, Yang Y, Qian H, Yao W (2018) Fair task offloading among fog nodes in fog computing networks. In: 2018 IEEE international conference on communications (ICC), pp 1–6

  35. 35.

    Zhang M, Zhou Y, Quan W, Zhu J, Zheng R, Wu Q (2020) Online learning for IoT optimization: A Frank-Wolfe Adam based algorithm. IEEE Int Things J, pp 1–1

  36. 36.

    Zhou Z, Wang H, Shao H, Dong L, Yu J (2020) A high-performance scheduling algorithm using greedy strategy toward quality of service in the cloud environments. Peer-to-Peer Netw Appl, pp 1–10

Download references

Acknowledgments

This work was supported in part by the Brazilian Research Agency CNPq and the Academy of Sciences for the Developing World (TWAS), under process 190172/2014-2 of the CNPq-TWAS program. The authors would also like to thank grant #15/24494-8 from São Paulo Research Foundation (FAPESP).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Nelson L. S. da Fonseca.

Ethics declarations

Conflict of interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Guevara, J.C., da Fonseca, N.L.S. Task scheduling in cloud-fog computing systems. Peer-to-Peer Netw. Appl. 14, 962–977 (2021). https://doi.org/10.1007/s12083-020-01051-9

Download citation

Keywords

  • Fog computing
  • Cloud computing
  • Edge computing
  • Scheduling
  • Class of service
  • Quality of service