Skip to main content

Advertisement

SpringerLink
Log in

AGCM: Active Queue Management-Based Green Cloud Model for Mobile Edge Computing

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Mobile edge computing (MEC) introduced a way for mobile users to acquire the benefits of cloud computing and satisfy the continuous growth of data demands. Still, amidst the evolutionary development of cloud computing and MEC, the wireless bandwidth and mobile devices limitations present numerous obstacles which limit the system efficiency, including the energy consumption and latency, these restrictions must be eliminated to realize the determined low energy and millisecond-scale latency for 5G. In this paper, an “Active queue management-based green cloud model for mobile edge computing” referred to as ‘AGCM’ is proposed for 5G to address this issue, in which the mobile users are served more efficiently with less energy waste at both the cloud and the mobile devices and reduced latency. The proposed model achieves this by alleviating the congestion in the cloud by utilizing the enhanced random early detection algorithm and implementing a virtual list to store the packets information and smartly prioritize and serve the packets. The simulation results, implemented in NS2 Green Cloud Simulator, attested that AGCM compared to the conventional cloud and femtolet model provided enhancement in the energy consumption by 90.6% and 24.6% respectively, the results also shows that AGCM can reduce the latency by 84% and 65% than the conventional cloud and femtolet model respectively. The quality of service also improved as the throughput is increased by 420% and 3.48% compared with cloud and femtolet respectively.

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
Fig. 10

Similar content being viewed by others

References

  1. Andrews, J. G., Buzzi, S., Choi, W., Hanly, S. V., Lozano, A., Soong, A. C., et al. (2014). What will 5G Be? IEEE Journal on Selected Areas in Communications, 32(6), 1065–1082. https://doi.org/10.1109/jsac.2014.2328098.

    Article  Google Scholar 

  2. Chin, W., Fan, Z., & Haines, R. (2014). Emerging technologies and research challenges for 5G wireless networks. IEEE Wireless Communications, 21(2), 106–112. https://doi.org/10.1109/mwc.2014.6812298.

    Article  Google Scholar 

  3. Mukherjee, A., Bhattacherjee, S., Pal, S., & De, D. (2013). Femtocell based green power consumption methods for mobile network. Computer Networks, 57(1), 162–178. https://doi.org/10.1016/j.comnet.2012.09.007.

    Article  Google Scholar 

  4. Dahlman, D., Sachs, J., Parkvall, S., Mildh, G., Selen, Y., & Peisa, J. (2014). 5G radio access. Ericsson white paper. Ericsson Review. https://pdfs.semanticscholar.org/9a06/bcadf0f4e3770260e0193746d5365b6c9114.pdf. Accessed 27 July 2018.

  5. Demestichas, K., Adamopoulou, E., & Choraś, M. (2017). 5G communications: Energy efficiency. Mobile Information Systems, 2017, 1–3. https://doi.org/10.1155/2017/5121302.

    Article  Google Scholar 

  6. Salo, J. (2012). Data centre network architectures. Seminar on Internetworking, 1–6. http://www.cse.tkk.fi/en/publications/B/10/papers/Salo_final.pdf. Accessed 1 August 2018.

  7. Qi, H., Shiraz, M., Liu, J., Gani, A., Rahman, Z. A., & Altameem, T. A. (2014). Data center network architecture in cloud computing: Review, taxonomy, and open research issues. Journal of Zhejiang University Science C, 15(9), 776–793. https://doi.org/10.1631/jzus.c1400013.

    Article  Google Scholar 

  8. Evans, D. (2011). The internet of things: How the next evolution of the internet is changing everything. Cisco white paper. Cisco. https://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf. Accessed 29 July 2018.

  9. Dolui, K., & Datta, S. K. (2017). Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing. In 2017 Global Internet of Things Summit (GIoTS). https://doi.org/10.1109/giots.2017.8016213.

  10. Gai, K., Qiu, M., Zhao, H., Tao, L., & Zong, Z. (2016). Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing. Journal of Network and Computer Applications, 59, 46–54. https://doi.org/10.1016/j.jnca.2015.05.016.

    Article  Google Scholar 

  11. Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30–39. https://doi.org/10.1109/mc.2017.9.

    Article  Google Scholar 

  12. Satyanarayanan, M., Bahl, V., Caceres, R., & Davies, N. (2011). The case for VM-based cloudlets in mobile computing. IEEE Pervasive Computing. https://doi.org/10.1109/mprv.2009.64.

    Google Scholar 

  13. Satyanarayanan, M., Chen, Z., Ha, K., Hu, W., Richter, W., & Pillai, P. (2014). Cloudlets: At the leading edge of mobile-cloud convergence. In Proceedings of the 6th international conference on mobile computing, applications and services. https://doi.org/10.4108/icst.mobicase.2014.257757.

  14. Sun, X., & Ansari, N. (2017). Green cloudlet network: A distributed green mobile cloud Network. IEEE Network, 31(1), 64–70. https://doi.org/10.1109/mnet.2017.1500293nm.

    Article  Google Scholar 

  15. Tanzil, S., Gharehshiran, O., & Krishnamurthy, V. (2016). A distributed coalition game approach to femto-cloud formation. IEEE Transactions on Cloud Computing. https://doi.org/10.1109/tcc.2016.2594175.

    Google Scholar 

  16. Habak, K., Ammar, M., Harras, K. A., & Zegura, E. (2015). Femto Clouds: Leveraging Mobile Devices to Provide Cloud Service at the Edge. In 2015 IEEE 8th international conference on cloud computing. https://doi.org/10.1109/cloud.2015.12.

  17. Mukherjee, A., & De, D. (2016). Femtolet: A novel fifth generation network device for green mobile cloud computing. Simulation Modelling Practice and Theory, 62, 68–87. https://doi.org/10.1016/j.simpat.2016.01.014.

    Article  Google Scholar 

  18. Barroso, L. A., & Hölzle, U. (2007). The case for energy-proportional computing. Computer, 40(12), 33–37. https://doi.org/10.1109/mc.2007.443.

    Article  Google Scholar 

  19. Lin, M., Wierman, A., Andrew, L. L., & Thereska, E. (2011). Dynamic right-sizing for power-proportional data centers. In 2011 Proceedings IEEE INFOCOM. https://doi.org/10.1109/infcom.2011.5934885.

  20. Elbamby, M. S., Bennis, M., & Saad, W. (2017). Proactive edge computing in latency-constrained fog networks. In 2017 European conference on networks and communications (EuCNC). https://doi.org/10.1109/eucnc.2017.7980678.

  21. Thinh, T. Q., Tang, J., La, Q. D., & Quek, T. Q. (2017). Offloading in mobile edge computing: Task allocation and computational frequency scaling. IEEE Transactions on Communications. https://doi.org/10.1109/tcomm.2017.2699660.

    Google Scholar 

  22. Wang, S., Urgaonkar, R., He, T., Zafer, M., Chan, K., & Leung, K. K. (2014). Mobility-Induced service migration in mobile micro-clouds. In 2014 IEEE military communications conference. https://doi.org/10.1109/milcom.2014.145.

  23. Ge, Y., Zhang, Y., Qiu, Q., & Lu, Y. (2012). A game theoretic resource allocation for overall energy minimization in mobile cloud computing system. In Proceedings of the 2012 ACM/IEEE international symposium on low power electronics and design—ISLPED 12. https://doi.org/10.1145/2333660.2333724.

  24. Jararweh, Y., Doulat, A., Alqudah, O., Ahmed, E., Al-Ayyoub, M., & Benkhelifa, E. (2016). The future of mobile cloud computing: Integrating cloudlets and Mobile Edge Computing. In 2016 23rd International conference on telecommunications (ICT). https://doi.org/10.1109/ict.2016.7500486.

  25. Huang, J., Qian, F., Gerber, A., Mao, Z. M., Sen, S., & Spatscheck, O. (2012). A close examination of performance and power characteristics of 4G LTE networks. In Proceedings of the 10th international conference on mobile systems, applications, and services—MobiSys 12. https://doi.org/10.1145/2307636.2307658.

  26. Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017). A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys & Tutorials, 19(4), 2322–2358. https://doi.org/10.1109/comst.2017.2745201.

    Article  Google Scholar 

  27. Li, H., Shou, G., Hu, Y., & Guo, Z. (2016). Mobile Edge Computing: Progress and Challenges. In 2016 4th IEEE international conference on mobile cloud computing, services, and engineering (MobileCloud). https://doi.org/10.1109/mobilecloud.2016.16.

  28. Kekki, S., Featherstone, W., Fang, Y., Kuure, P., Li, A., Ranjan, A., et al. (2018). MEC in 5G networks. ETSI white paper. The European Telecommunications Standards Institute (ETSI). https://www.etsi.org/images/files/ETSIWhitePapers/etsi_wp28_mec_in_5G_FINAL.pdf. Accessed 5 August 2018.

  29. 3rd Generation Partnership Project. (2017). System architecture for the 5g systems. Technical specification 23.501-040 Rel-15. In 3rd Generation partnership project (3GPP). https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3144. Accessed 1 August 2018.

  30. Satria, D., Park, D., & Jo, M. (2017). Recovery for overloaded mobile edge computing. Future Generation Computer Systems, 70, 138–147. https://doi.org/10.1016/j.future.2016.06.024.

    Article  Google Scholar 

  31. Machen, A., Wang, S., Kin K., Leung, Ko, B., & Salonidis, S. (2016). Migrating running applications across mobile edge clouds: Poster. In Proceedings of the 22nd annual international conference on mobile computing and networking (MobiCom ‘16) (pp. 435–436). New York, NY: ACM. https://doi.org/10.1145/2973750.2985265.

  32. Chiang, M., & Zhang, T. (2016). Fog and IoT: An Overview of Research Opportunities. IEEE Internet of Things Journal, 3(6), 854–864. https://doi.org/10.1109/jiot.2016.2584538.

    Article  Google Scholar 

  33. Yi, S., Li, C., & Li, Q. (2015). A survey of fog computing. In: Proceedings of the 2015 workshop on mobile big data—Mobidata 15. https://doi.org/10.1145/2757384.2757397.

  34. Ismail, A. H., El-Sayed, A., Elsaghir, Z., & Morsi, I. Z. (2014). Enhanced random early detection (ENRED). International Journal of Computer Applications, 92(9), 25–28. https://doi.org/10.5120/16039-5015.

    Article  Google Scholar 

  35. Ge, C., Sun, Z., & Wang, N. (2013). A survey of power-saving techniques on data centers and content delivery networks. IEEE Communications Surveys and Tutorials, 15(3), 1334–1354. https://doi.org/10.1109/surv.2012.102512.00019.

    Article  Google Scholar 

  36. Nie, J., Luo, J., & Yin, L. (2017). Energy-aware Multi-dimensional resource allocation algorithm in Cloud Data Center. KSII Transactions on Internet and Information Systems. https://doi.org/10.3837/tiis.2017.09.008.

    Google Scholar 

Download references

Acknowledgements

An acknowledgement to Prof. Ali Ismail Awad For his positive support, Department of Computer Science, Electrical and Space Engineering Lulea University of Technology, Lulea, Sweden.

Author information

Authors and Affiliations

  1. Electronics and Communications Engineering Department, Delta Higher Institute for Engineering and Technology, Talkha, 35111, Egypt

    Alshimaa H. Ismail

  2. Computer Science and Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Nirmeen A. El-Bahnasawy

  3. Electrical Engineering Department, Faculty of Engineering, Minia University, El-Minia, 61111, Egypt

    Hesham F. A. Hamed

Authors
  1. Alshimaa H. Ismail
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nirmeen A. El-Bahnasawy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hesham F. A. Hamed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alshimaa H. Ismail.

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

Ismail, A.H., El-Bahnasawy, N.A. & Hamed, H.F.A. AGCM: Active Queue Management-Based Green Cloud Model for Mobile Edge Computing. Wireless Pers Commun 105, 765–785 (2019). https://doi.org/10.1007/s11277-019-06119-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06119-1

Keywords

Search

Navigation