Skip to main content
SpringerLink
Log in

Deadline-aware heuristics for reliability optimization in ubiquitous mobile edge computing

  • Regular Paper
  • Published:
International Journal of Data Science and Analytics Aims and scope Submit manuscript

Abstract

With the advent of affordable and widely accessible broadband and mobile internet, there has been a significant surge in user demand. These demands, especially when considering latency and user preferences, exhibit a highly dynamic nature in the realm of Ubiquitous Mobile Edge Computing (UMEC). In (UMEC), the end devices offload computationally intensive tasks to proximate edge servers. The subtasks of a large task are distributed to different edge nodes to improve reliability. Moreover, some tasks in UMEC are deadline constrained, neglecting which may lead to task failure. Optimizing reliability in Ubiquitous Mobile Edge Computing (UMEC) is crucial to ensure consistent and dependable performance of edge computing systems across a wide range of devices and environments. The existing work focuses on the tradeoff between latency and reliability in task offloading to UMEC. In this work, we define the deadlines of different tasks that require the offloading, considering their latency requirements and offloading failure probabilities. A critical issue in UMEC is to find a reliable server. We propose a novel deadline-aware heuristic for task offloading that divides the tasks into subtasks. The heuristic algorithm considers the latency and computing capacity of edge nodes to reduce task failure ratio and optimize the reliability. We consider the latency and offloading failure probability as performance evaluation parameters. The simulation results reveal that the proposed Deadline-aware Heuristic Algorithm (DHA) achieves a remarkable total latency of 12.67 ms, coupled with a mere 0.095 probability of offloading failure. In contrast, the state-of-the-art technique exhibits a latency of 19 ms, and a higher offloading failure probability of 0.38.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Data availability

No data was used for this article.

References

  1. Al Moteri, M., Khan, S. B., Alojail, M.: Machine Learning-Driven Ubiquitous Mobile Edge Computing as a Solution to Network Challenges in Next-Generation IoT. Systems 11(6), 308 (2023). Available: https://www.mdpi.com/2079-8954/11/6/308.

  2. Donta, P. K., Monteiro, E., Dehury, C. K., Murturi, I.: Learning‐driven ubiquitous mobile edge computing: Network management challenges for future generation Internet of Things. 33, e2250 (2023).

  3. Shuja, J., Gani, A., Naveed, A., Ahmed, E., Hsu, C.-H.: Case of ARM emulation optimization for offloading mechanisms in mobile cloud computing. Futur. Gener. Comput. Syst. 76, 407–417 (2017)

    Article  Google Scholar 

  4. Gul-E-Laraib et al.: Content caching in mobile edge computing based on user location and preferences using cosine similarity and collaborative filtering. Electronics 12(2), 284 (2023). Available: https://www.mdpi.com/2079-9292/12/2/284.

  5. Zaman, S. K. U. et al.: Cooperative content caching framework using cuckoo search optimization in vehicular edge networks. Appl. Sci. 13(2), 780 (2023). Available: https://www.mdpi.com/2076-3417/13/2/780.

  6. Nadeem, S. et al.: Runtime Management of Service Level Agreements through Proactive Resource Provisioning for a Cloud Environment. Electronics 12(2), 296 (2023). Available: https://www.mdpi.com/2079-9292/12/2/296.

  7. Atiq, H. U., Ahmad, Z., uz Zaman, S. K., Khan, M. A., Shaikh, A. A., Al-Rasheed, A.: Reliable Resource allocation and management for iot transportation using fog computing. Electronics 12(6), 1452 (2023). Available: https://www.mdpi.com/2079-9292/12/6/1452.

  8. Zhu, Y., Hu, Y., Yang, T., Schmeink, A.: Reliability-optimal offloading in multi-server edge computing networks with transmissions carried by finite blocklength codes, in 2019 IEEE International Conference on Communications Workshops (ICC Workshops), 2019: IEEE, pp. 1–6.

  9. Liu, J., Zhang, Q.: Using imperfect transmission in MEC offloading to improve service reliability of time-critical computer vision applications. IEEE Access 8, 107364–107372 (2020)

    Article  Google Scholar 

  10. Lyu, X., et al.: Selective offloading in mobile edge computing for the green internet of things. IEEE Network 32(1), 54–60 (2018)

    Article  Google Scholar 

  11. El Haber, E., Alameddine, H. A., Assi, C., Sharafeddine, S.: A reliability-aware computation offloading solution via UAV-mounted cloudlets, in 2019 IEEE 8th International Conference on Cloud Networking (CloudNet), Coimbra, Portugal, 04–06 November 2019 2019: IEEE, pp. 1–6, https://doi.org/10.1109/CloudNet47604.2019.9064038.

  12. Liu, H., Cao, L., Pei, T., Deng, Q., Zhu, J.: A fast algorithm for energy-saving offloading with reliability and latency requirements in multi-access edge computing. IEEE Access 8, 151–161 (2019)

    Article  Google Scholar 

  13. Li, B., Peng, Z., Hou, P., He, M., Anisetti, M., Jeon, G.: Reliability and capability based computation offloading strategy for vehicular ad hoc clouds. J. Cloud Comput. 8(1), 1–14 (2019)

    Article  Google Scholar 

  14. Liu, J., Zhang, Q.: Offloading schemes in mobile edge computing for ultra-reliable low latency communications. Ieee Access 6, 12825–12837 (2018)

    Article  Google Scholar 

  15. Merluzzi, M., Di Lorenzo, P., Barbarossa, S., Frascolla, V.: Dynamic computation offloading in multi-access edge computing via ultra-reliable and low-latency communications. IEEE Trans. Signal Inf. Proc. Over Netw. 6, 342–356 (2020)

    Article  MathSciNet  Google Scholar 

  16. Hou, X., et al.: Reliable computation offloading for edge-computing-enabled software-defined IoV. IEEE Internet Things J. 7(8), 7097–7111 (2020)

    Article  Google Scholar 

  17. He, Z., et al.: Computation offloading with reliability guarantee in vehicular edge computing systems, in 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), Victoria, BC, Canada, 18 November 2020 - 16 December 2020 2020: IEEE, pp. 1–5, doi: https://doi.org/10.1109/VTC2020-Fall49728.2020.9348663.

  18. Liu, C.-F., Bennis, M., Debbah, M., Poor, H.V.: Dynamic task offloading and resource allocation for ultra-reliable low-latency edge computing. IEEE Trans. Commun. 67(6), 4132–4150 (2019)

    Article  Google Scholar 

  19. Han, X., et al.: Reliability-aware joint optimization for cooperative vehicular communication and computing. IEEE Trans. Intell. Transp. Syst. 22(8), 5437–5446 (2020)

    Article  Google Scholar 

  20. Hsu, C.-W., Hsu, Y.-L., Wei, H.-Y.: Energy-efficient and reliable MEC offloading for heterogeneous industrial IoT networks, in 2019 European Conference on Networks and Communications (EuCNC), Valencia, Spain, 18–21 June 2019 2019: IEEE, pp. 384–388, doi: https://doi.org/10.1109/EuCNC.2019.8802020.

  21. Yang, T., Hu, Y., Gursoy, M. C., Schmeink, A., Mathar, R.: Deep reinforcement learning based resource allocation in low latency edge computing networks, in 2018 15th international symposium on wireless communication systems (ISWCS), Lisbon, Portugal, 28–31 August 2018 2018: IEEE, pp. 1–5, https://doi.org/10.1109/ISWCS.2018.8491089.

  22. Han, B., Wong, S., Mannweiler, C., Crippa, M.R., Schotten, H.D.: Context-awareness enhances 5G multi-access edge computing reliability. IEEE Access 7, 21290–21299 (2019)

    Article  Google Scholar 

  23. Sun, Z., Mo, Y., Yu, C.: Graph reinforcement learning based task offloading for multi-access edge computing. IEEE Internet Things J. (2021)

  24. Li, Z., Chang, V., Ge, J., Pan, L., Hu, H., Huang, B.: Energy-aware task offloading with deadline constraint in mobile edge computing. EURASIP J. Wirel. Commun. Netw. 2021, 1–24 (2021)

    Article  Google Scholar 

  25. Xu, X., et al.: Joint task offloading and resource optimization in noma-based vehicular edge computing: A game-theoretic drl approach. J. Syst. Architect. 134, 102780 (2023)

    Article  Google Scholar 

  26. Guo, M., Li, Q., Peng, Z., Liu, X., Cui, D.: Energy harvesting computation offloading game towards minimizing delay for mobile edge computing. Comput. Netw. 204, 108678 (2022)

    Article  Google Scholar 

  27. La, Q.D., Ngo, M.V., Dinh, T.Q., Quek, T.Q., Shin, H.: Enabling intelligence in fog computing to achieve energy and latency reduction. Dig. Commun. Netw. 5(1), 3–9 (2019)

    Article  Google Scholar 

  28. Alamouti, S.M., Arjomandi, F., Burger, M.: Hybrid edge cloud: a pragmatic approach for decentralized cloud computing. IEEE Commun. Mag. 60(9), 16–29 (2022)

    Article  Google Scholar 

  29. Wang, C., Elliott, R.C., Feng, D., Krzymien, W.A., Zhang, S., Melzer, J.: A Framework for MEC-enhanced small-cell HetNet with massive MIMO. IEEE Wirel. Commun. 27(4), 64–72 (2020)

    Article  Google Scholar 

  30. Zhao, X., Shi, Y., Chen, S.: MAESP: Mobility aware edge service placement in mobile edge networks. Comput. Netw. 182, 107435 (2020)

    Article  Google Scholar 

  31. Yang, T., Chai, R., Zhang, L.: Latency optimization-based joint task offloading and scheduling for multi-user MEC system. in 2020 29th Wireless and Optical Communications Conference (WOCC), Newark, NJ, USA, 01–02 May 2020 2020: IEEE, pp. 1–6, https://doi.org/10.1109/WOCC48579.2020.9114942.

Download references

Funding

No funding was received for this research.

Author information

Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad (CUI), Abbottabad Campus, Abbottabad, Pakistan

    Sardar Khaliq Uz Zaman, Azra Ramzan, Faisal Rehman & Saad Mustafa

  2. Department of Computer Science, COMSATS University Islamabad (CUI), Lahore Campus, Lahore, Pakistan

    Tahir Maqsood

  3. Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia

    Junaid Shuja

Authors
  1. Sardar Khaliq Uz Zaman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tahir Maqsood
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Azra Ramzan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Faisal Rehman
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Saad Mustafa
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Junaid Shuja
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.K.U.Z. and T.M., conceived and designed the methods, and experiments; A.R. and F.R. performed the simulations; S.M. and J.S. analyzed the results and drafted/revised the manuscript critically. All authors have read and agreed to the submitted version of the manuscript. All authors have read and agreed to this version of the manuscript.

Corresponding author

Correspondence to Sardar Khaliq Uz Zaman.

Ethics declarations

Conflict of interests

The authors declare no competing interests.

Ethical approval

This is the author's own work not submitted anywhere else.

Informed consent

Not applicable.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zaman, S.K.U., Maqsood, T., Ramzan, A. et al. Deadline-aware heuristics for reliability optimization in ubiquitous mobile edge computing. Int J Data Sci Anal (2023). https://doi.org/10.1007/s41060-023-00473-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41060-023-00473-x

Keywords

Search

Navigation