Skip to main content
Log in

Deep learning based dynamic task offloading in mobile cloudlet environments

  • Special Issue
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript


The mobile computing world is migrating from 4G to 5G and one of the major offering of 5G is the seamless computing power and it is the major set back in the current scenario. The major difficulties that need to be addressed are computing, quality of services. Speed, power and security. This research paper aims in addressing the issue of task management in the mobile systems that is directly related to quality. The article proposes a deep learning-based algorithm that performs dynamic task offloading in the mobile cloudlet since cloudlet aids in the reduction of the delay that occur in the WLAN. The delay in performing tasks is one of the major drawback of cloudlet that it is deprived of resources when compared to cloud server due to which the tasks that are to be performed are divided and is designated to mobile devices, different cloud servers and cloudlet itself. Therefore, to determine the combination of devices required to perform different tasks, deep learning algorithms are considered. The algorithm is responsible to identify the subtasks, the subtasks that has to be computed/executed in which device or cloudlet or cloud server. The proposed algorithm is named Deep Learning based Dynamic Task Offloading in Mobile Cloudlet (DLDTO). The algorithm is implemented and compared with Cloudlet based Dynamic Task Offloading (CDTO). The overall analysis and comparison with the existing CDTO for job allocation proved that the performance of the proposed DLDTO algorithm is better in terms of energy consumption and completion time.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others


  1. Leung V, Taleb T, Chen M, Magedanz T, Wang L, Tafazolli R (2014) Unveiling 5G wireless networks: emerging research advances, prospects, and challenges. IEEE Netw 28(6):3–5

    Article  Google Scholar 

  2. Tang C, Xiao S, Wei X, Hao M, Chen W (2018) Energy efficient and deadline satisfied task scheduling in mobile cloud computing. In: 2018 IEEE international conference on big data and smart computing (BigComp), IEEE, pp 198–205

  3. Conti M, Chong S, Fdida S, Jia W, Karl H, Lin YD et al (2011) Research challenges towards the future internet. Comput Commun 34(18):2115–2134

    Article  Google Scholar 

  4. Kumar K, Lu YH (2010) Cloud computing for mobile users: can offloading computation save energy? Computer 43(4):51–56

    Article  Google Scholar 

  5. Satyanarayanan M, Bahl V, Caceres R, Davies N (2009) The case for VM-based cloudlets in mobile computing. IEEE Pervasive Comput 8(4):14–23

    Article  Google Scholar 

  6. Xia Q, Liang W, Xu W (2013) Throughput maximization for online request admissions in mobile cloudlets. In: The 38th IEEE conference on local computer networks (LCN), Sydney, pp 589–596

  7. Youn CH, Chen M, Dazzi P (2017) Mobile device as cloud broker for computation offloading at cloudlets. In: Cloud broker and cloudlet for workflow scheduling, Springer, Singapore, pp 135–146

  8. Satyanarayanan M (2001) Pervasive computing: vision and challenges. IEEE Pers Commun 8(4):10–17

    Article  Google Scholar 

  9. Li Y, Wang W (2014) Can mobile cloudlets support mobile applications? In: Proceedings of IEEE INFOCOM 2014, pp 1060–1068

  10. Su W-T, Ng KS (2013) Mobile cloud with smart offloading system. In: Proceedings of 2013 IEEE/CIC international conference on communication in China, pp 680–685

  11. Barbarossa S, Sardellitti S, Lorenzo PD (2014) Communicating while computing: distributed mobile cloud computing over 5G heterogeneous networks. IEEE Signal Process Magn 31(6):45–55

    Article  Google Scholar 

  12. Zohreh S, Saeid A, Abdullah G, Rajkumar B (2013) Heterogeneity in mobile cloud computing: taxonomy and open challenges. IEEE Commun Surv Tutor 16(1):369–392

    Google Scholar 

  13. Su WT, Kao CY (2017) EstiTO: an efficient task offloading approach based on node capability estimation in a cloudlet. In: 2017 IEEE wireless communications and networking conference (WCNC), IEEE, pp 1–6

  14. Yao D, Gui L, Hou F, Sun F, Mo D, Shan H (2017) Load balancing oriented computation offloading in mobile cloudlet. In: 2017 IEEE 86th vehicular technology conference (VTC-Fall), IEEE, pp 1–6

  15. Roy DG, De D, Mukherjee A, Buyya R (2017) Application-aware cloudlet selection for computation offloading in multi-cloudlet environment. J Supercomput 73(4):1672–1690

    Article  Google Scholar 

  16. Lee HS, Lee JW (2018) Task offloading in heterogeneous mobile cloud computing: modeling, analysis, and cloudlet deployment. IEEE Access 6:14908–14925

    Article  Google Scholar 

  17. Jia M, Liang W, Xu Z (2017) QoS-aware task offloading in distributed cloudlets with virtual network function services. In: Proceedings of the 20th ACM international conference on modelling, analysis and simulation of wireless and mobile systems, ACM, pp 109–116

  18. Guo X, Liu L, Chang Z, Ristaniemi T (2018) Data offloading and task allocation for cloudlet-assisted ad hoc mobile clouds. Wireless Netw 24(1):79–88

    Article  Google Scholar 

  19. Fan X, He X, Puthal D, Chen S, Xiang C, Nanda P, Rao X (2018) CTOM: collaborative task offloading mechanism for mobile cloudlet networks. In: 2018 IEEE international conference on communications (ICC), IEEE, pp 1–6

  20. Shobha Rani D, Pounambal M, Saritha V (2019) An efficient algorithm for dynamic task offloading using cloudlets in mobile cloud computing. Int J Commun Syst.

    Article  Google Scholar 

  21. Alkhalaileh M, Calheiros RN, Nguyen QV, Javadi B (2019) Dynamic resource allocation in hybrid mobile cloud computing for data-intensive applications. In: Miani R, Camargos L, Zarpelão B, Rosas E, Pasquini R (eds) Green, pervasive, and cloud computing, GPC 2019. Lecture notes in computer science, vol 11484. Springer, Cham

    Google Scholar 

  22. Mahesar AR, Lakhan A, Sajnani DK, Jamali IA (2018) Hybrid delay optimization and workload assignment in mobile edge cloud networks. Open Access Library J 5(9):1

    Google Scholar 

  23. Van Le D, Tham CK (2018) A deep reinforcement learning based offloading scheme in ad-hoc mobile clouds. In: IEEE INFOCOM 2018-IEEE conference on computer communications workshops (INFOCOM WKSHPS), IEEE, pp 760–765

  24. Mohammad U, Sorour S (2018) Adaptive task allocation for mobile edge learning. arXiv preprint arXiv:1811.03748

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to D. Shobha Rani.

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

Rani, D.S., Pounambal, M. Deep learning based dynamic task offloading in mobile cloudlet environments. Evol. Intel. 14, 499–507 (2021).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: