Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Delay-aware optimization of energy consumption for task offloading in fog environments using metaheuristic algorithms

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Due to the limitations associated with the processing capability of mobile devices in cloud environments, various tasks are offloaded to the cloud server. This has led to an increase in the efficiency of mobile applications in the two decades since the advent of the cloud paradigm. However, task offloading may not be a suitable option for delay-sensitive mobile applications because the cloud server is usually located remotely from mobile users. To overcome this problem, fog computing, also known as “Cloud at the Edge”, has been introduced as a complementary solution. On the other hand, although fog computing brings computing and radio resources closer to mobile devices, fog nodes cannot adequately meet users’ needs due to limited computing resources. To minimize delays in responding to mobile users’ requests, it is necessary to establish a trade-off between local execution of requests on end-devices and the fog environment. In this paper, we present task offloading in the form of a multi-objective optimization problem with a focus on reducing both total power consumption of the system and the delay in executing tasks. Then, considering the NP-hardness of the problem, we solve it using two meta-heuristic methods, namely the non-dominated sorting genetic algorithm (NSGA-II) and the Bees algorithm. The simulation results supported the robustness of both meta-heuristic algorithms in terms of energy consumption and delay reduction. The proposed methods achieve a better tradeoff concerning both offloading probability and the power required for data transmission.

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

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

Similar content being viewed by others

References

  1. Sanaei, Z., Abolfazli, S., Gani, A., Buyya, R.: Heterogeneity in mobile cloud computjing: taxonomy and open challenges. IEEE Commun. Surv. Tutor. 16(1), 369–392 (2014)

    Article  Google Scholar 

  2. Song, J., Cui, Y., Li, M., Qiu, J., Buyya, R.: Energy-traffic tradeoff cooperative offloading for mobile cloud computing. In: IEEE 22nd, Intemational Symposium of Quality of Service, Hong Kong. (2014)

  3. Guo, X., Liu, L., Chang, Z., Ristaniemi, T.: Data offloading and task, allocation for cloudlet-assisted ad hoc mobile clouds. Wireless Netw. 24, 79–88 (2016)

    Article  Google Scholar 

  4. Zhang, Y., Niyato, D., Wang, P.: Offloading in mobile cloudlet systems with intermittent connectivity. IEEE Trans. Mob. Comput. 14(12), 2529 (2015)

    Article  Google Scholar 

  5. De Maio, V., Kimovski, D.: Multi-objective scheduling of extreme data scientific workflows in Fog. Future Gener. Comput. Syst 106, 171–184 (2020)

    Article  Google Scholar 

  6. Mahmud, R., Koch, F.L., Buyya, R.: Cloud-fog interoperability in IoT-enabled healthcare solutions. In: Proceedings of the 19th International Conference on Distributed Computing and Networking (ICDCN ‘18), pp. 1–10, Varanasi (2018)

  7. Shakarami, A., Ghobaei-Arani, M., Masdari, M. and Hosseinzadeh, M.: A survey on the computation offloading approaches in mobile edge/cloud computing environment: a stochastic-based perspective. J. Grid Comput. pp. 1–33 (2020)

  8. Liu, L., Chang, Z., Ristaniemi, T., Niu, Z.: Multi-objective optimization for computation offloading in fog computing. In: IEEE Internet of Things J. https://doi.org/10.1109/jiot. (2017)

  9. Rahbari, D., Nickray, M.: Task offloading in mobile fog computing by classification and regression tree. Peer-to-Peer Netw. Appl. 13(1), 104–122 (2020)

    Article  Google Scholar 

  10. Jiang, Y.L., Chen, Y.S., Yang, S.W., Wu, C.H.: Energy-efficient task offloading for time-sensitive applications in fog computing. IEEE Syst. J. 13(3), 2930–2941 (2018)

    Article  Google Scholar 

  11. Farahbakhsh, F., Shahidinejad, A., Ghobaei-Arani, M.: Multiuser context aware computation offloading in mobile edge computing based on Bayesian learning automata. Trans. Emerg. Telecommun. Technol., p. e4127 (2020)

  12. Shahidinejad, A., Ghobaei-Arani, M.: Joint computation offloading and resource provisioning for edge-cloud computing environment: a machine learning-based approach. Software 50(12), 2212–2230 (2020)

    Google Scholar 

  13. Jazayeri, F., Shahidinejad, A, Ghobaei-Arani, M.: Autonomous computation offloading and auto-scaling the in the mobile fog computing: a deep reinforcement learning-based approach. J. Ambient Intell. Hum. Comput. pp. 1–20 (2020)

  14. Liu, L., Chang, Z., Guo, X.: Socially aware dynamic computation offloading scheme for fog computing system with energy harvesting devices. IEEE Internet Things J. https://doi.org/10.1109/jiot.2018

  15. Josilo, S., Dán, G.: Computing resource management for offloading of periodic tasks. https://doi.org/10.1109/infcomw.2018

  16. Wei, Z., Jiang, H.: Optimal offloading in fog computing systems with non-orthogonal multiple access. In: IEEE Access. https://doi.org/10.1109/access.2018

  17. Chen, L., Zhou, S., Xu, J.: Computation peer offloading for energy-constrained mobile edge computing in small-cell networks. IEEE/ACM Trans. Netw. https://doi.org/10.1109/tnet.2018

  18. Kim, Y., Kwak, J., Chong, S.: Dual-side optimization for cost-delay tradeoff in mobile edge computing. In: IEEE Transactions on Vehicular Technology, https://doi.org/10.1109/tvt.2017

  19. Wang, J., Liu, T., Liu, K., Kim, B., Xie, J., Han, Z.: Computation offloading over fog and cloud using multi-dimensional multiple knapsack problem. In: 2018 IEEE Global Communications Conference (GLOBECOM) (pp. 1–7). IEEE (2018)

  20. Huang, X., Yang, Y., Wu, X.: A meta-heuristic computation offloading strategy for IoT applications in an edge-cloud framework. In: Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control (pp. 1–6) (2019)

  21. Adhikari, M., Srirama, S.N., Amgoth, T.: Application offloading strategy for hierarchical fog environment through swarm optimization. IEEE Internet Things J 7(5), 4317–4328 (2019)

    Article  Google Scholar 

  22. Hussein, M.K., Mousa, M.H.: Efficient task offloading for IoT-based applications in fog computing using ant colony optimization. IEEE Access 8, 37191–37201 (2020)

    Article  Google Scholar 

  23. Subramaniam, E.V.D., Krishnasamy, V.: Energy aware smartphone tasks offloading to the cloud using gray wolf optimization. J Ambient Intell. Hum. Comput. pp. 1–9 (2020)

  24. Adhikari, M., Gianey, H.: Energy efficient offloading strategy in fog-cloud environment for IoT applications. Internet Things 6, 100053 (2019)

    Article  Google Scholar 

  25. Manasrah, A.M., Gupta, B.B.: An optimized service broker routing policy based on differential evolution algorithm in fog/cloud environment. Clust. Comput. 22(1), 1639–1653 (2019)

    Article  Google Scholar 

  26. Ghobaei-Arani, M., Souri, A., Safara, F., Norouzi, M.: An efficient task scheduling approach using moth-flame optimization algorithm for cyber-physical system applications in fog computing. Trans. Emerg. Telecommun. Technol. 31(2), e3770 (2020)

    Google Scholar 

  27. Bozorgchenani, A., Tarchi, D., Corazza, G.E.: An energy and delay-efficient partial offloading technique for fog computing architectures. IEEE Global Commun. https://doi.org/10.1109/glocom.2017

  28. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  29. Pham, D.T., Castellani, M.: The bees algorithm: modelling foraging behaviour to solve continuous optimization problems. Proc. Inst. Mech. Eng. Part C 223(12), 2919–2938 (2009)

    Article  Google Scholar 

  30. Aboutorabi, S.J.S., Rezvani, M.H.:. An optimized meta-heuristic bees algorithm for players’ frame rate allocation problem in cloud gaming environments. Comput. Games J, pp. 1–24 (2020)

  31. Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya R.: iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments. Software (2017)

  32. Mahmud, R., Kotagiri, R., Buyya, R.: Fog computing: A taxonomy, survey and future directions. In: Internet of Everything, pp. 103–130. Springer, Singapore (2018)

    Chapter  Google Scholar 

  33. Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R.H., Morrow, M.J., Polakos, P.A.: A comprehensive survey on fog computing: state-of-the-art and research challenges. IEEE Commun. Surv. Tutor. (2017). https://doi.org/10.1109/comst.2017

    Article  Google Scholar 

  34. Ghobaei-Arani, M., Souri, A., Rahmanian, A.A.: Resource management approaches in fog computing: a comprehensive review. J. Grid Comput. 18, 1–42 (2019)

    Article  Google Scholar 

  35. Shakarami, A., Shahidinejad, A., Ghobaei‐Arani, M,. A review on the computation offloading approaches in mobile edge computing: a game‐theoretic perspective. Software (2020)

  36. Chang, Z., Zhou, Z., Ristaniemi, T., Niu, Z.: Energy efficient optimization for computation offloading in fog computing system. IEEE Global Commun. (2017). https://doi.org/10.1109/glocom.2017

    Article  Google Scholar 

  37. Jia, M., Cao, J., Liang, W.: Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Trans Cloud Comput. (2015). https://doi.org/10.1109/tcc.2015.2449834

    Article  Google Scholar 

  38. Wang, Y., Lin, X., Pedram, M.: A nested two stage game-based optimization framework in mobile cloud computing system. In: 2013 IEEE Seventh International Symposium on Service-Oriented System Engineering, Washington (2013)

  39. Besharati, R., Rezvani, M.H.:A prototype auction-based mechanism for computation offloading in fog-cloud environments. In: Proceedings of 5th IEEE International Conference on Knowledge-Based Engineering and Innovation (KBEI’19), Tehra (2019) https://doi.org/10.1109/kbei.2019.8734918

  40. Elashri, S., Azim, A.: Energy-efficient offloading of real-time tasks using cloud computing. Clust. Comput. 34, 1–16 (2020)

    Google Scholar 

  41. Alam, Md Golam Rabiul, et al.: Autonomic computation offloading in mobile edge for IoT applications. Science Direct Future Gener. Comput. Syst. 90, 149–157 (2019)

    Article  Google Scholar 

  42. Misra, Sudip, et al.: Detour: dynamic task offloading in software-defined fog for IoT applications. IEEE J. Sel. Areas Commun. 37(5), 1159–1166 (2019)

    Article  Google Scholar 

  43. Liu, C.F., et al.: Dynamic task offloading and resource allocation for ultra-reliable low-latency edge computing. IEEE Trans. Commun. 67, 4132–4150 (2019)

    Article  Google Scholar 

  44. Li, Qiuping, et al.: Energy-efficient computation offloading and resource allocation in fog computing for internet of everything. IEEE China Commun. 16(3), 32–41 (2019)

    Google Scholar 

  45. Zhou, S.et al.: Exploiting moving intelligence: delay-optimized computation offloading in vehicular fog networks. IEEE Communication Magazine (2019)

  46. Mostafa M.A.A., Khater, A.M.: Horizontal offloading mechanism for IoT application in fog computing using microservices case study: traffic management system. In: IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), (2019)

  47. Nguyen, TT et al.: Joint data compression and computation offloading in hierarchical fog-cloud systems. arxiv:1903.08566v2, (2019)

  48. Wang, Dongyu, et al.: Mobility-aware task offloading and migration schemes in fog computing networks. IEEE Access 7, 43356–43368 (2019)

    Article  Google Scholar 

  49. Chen, X., Li, W., Lu, S., Fu, X.: Efficient resource allocation for on-demand mobile-edge cloud computing. IEEE Trans. Vehic. Technol. (2018). https://doi.org/10.1109/tvt.2018

    Article  Google Scholar 

  50. Du, J., Zhao, L., Chu, X.I.: Computation offloading and resource allocation in mixed fog/cloud computing systems with min-max fairness guarantee. IEEE Access (2018). https://doi.org/10.1109/tcomm.2017

    Article  Google Scholar 

  51. Yousefpour, A., Ishigaki, G., Jue, J.P.: On reducing IoT service delay via fog offloading. IEEE Internet Things J. (2018). https://doi.org/10.1109/jiot.2017

    Article  Google Scholar 

  52. Yu, L., Jiang, T., Zou, Y.: Fog-assisted operational cost reduction for cloud data centers. IEEE Access (2017). https://doi.org/10.1109/access.2017

    Article  Google Scholar 

  53. Meng, X., Wang, W., Zhang, Z.: Delay-constrained hybrid computation offloading with cloud and fog computing. IEEE Access (2017). https://doi.org/10.1109/access.2017

    Article  Google Scholar 

  54. Zhu, Q., Si, B., Chu, X.: Task offloading decision in fog computing system. China Commun. 14(11), 59–68 (2017)

    Article  Google Scholar 

  55. Sardellitti, S., Scutari, G., Barbarossa, S.: Joint optimization of radio and computational resource for multicell mobile-edge computing. IEEE Trans. Signal Inform. Process. Over Netw. 1(2), 89–103 (2015)

    Article  MathSciNet  Google Scholar 

  56. Hu, D., Alsmadi, Y.M., Xu, L.: High-fidelity nonlinear IPM modeling based on measured stator winding flux linkage. IEEE Trans. Ind. Appl. 51(4), 3012–3019 (2015)

    Article  Google Scholar 

  57. Gondzio, J.: Interior point methods 25 years later. Eur. J. Oper. Res. 218(3), 587–601 (2012)

    Article  MathSciNet  Google Scholar 

  58. Tavakoli-Someh, Sanaz, Rezvani, M.H.: Multi-objective virtual network function placement using NSGA-II meta-heuristic approach”. J. Supercomput. 75(10), 6451–6487 (2019). https://doi.org/10.1007/s11227-019-02849-y

    Article  Google Scholar 

  59. Bose, S.K.: An Introduction to Queueing Systems. Springer Science & Business Media, New York (2013)

    Google Scholar 

  60. Mohammadi, A., Rezvani, M.H.: A novel optimized approach for resource reservation in cloud computing using producer–consumer theory of microeconomics. J. Supercomput. (2019). https://doi.org/10.1007/s11227-019-02951-1

    Article  Google Scholar 

  61. Parvizi, E., Rezvani, M.H.: Utilization-aware energy-efficient virtual machine placement in cloud networks using NSGA-III meta-heuristic approach. Clust. Comput. (2020)

  62. Esfandiari, S., Rezvani, M.H.: An optimized content delivery approach based on demand–supply theory in disruption-tolerant networks. Telecommun. Syst. 48, 1–25 (2020)

    Google Scholar 

  63. Lung, C.H., Zhou, C.: Using hierarchical agglomerative clustering in wireless sensor networks: an energy-efficient and flexible approach. Ad Hoc Netw. 8(3), 328–344 (2010)

    Article  Google Scholar 

  64. Fisher, G.G.: Work/personal life balance: a construct development study (Doctoral Dissertation, ProQuest Information & Learning) (2002)

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran

    Maryam Keshavarznejad & Sepideh Adabi

  2. Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Mohammad Hossein Rezvani

Authors
  1. Maryam Keshavarznejad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Hossein Rezvani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sepideh Adabi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Hossein Rezvani.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Keshavarznejad, M., Rezvani, M.H. & Adabi, S. Delay-aware optimization of energy consumption for task offloading in fog environments using metaheuristic algorithms. Cluster Comput 24, 1825–1853 (2021). https://doi.org/10.1007/s10586-020-03230-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-020-03230-y

Keywords

Search

Navigation