Skip to main content
SpringerLink
Log in

Load-Aware Edge Server Placement for Mobile Edge Computing in 5G Networks

  • Conference paper
  • First Online:
Service-Oriented Computing (ICSOC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11895))

Included in the following conference series:

Abstract

Edge computing is a promising technique for 5G networks to collect a wide range of environmental information from mobile devices and return real-time feedbacks to the mobile users. Generally, the edge servers (ESs) are both contributing in macro-base station (MABS) sites for large-scale resource provisioning and micro-base station (MIBS) sites for light-weighted resource response. However, to lower the investment of construing the edge computing systems in the MIBS sites, limited number of ESs are employed, since there is an intensive distribution of MIBSs in 5G networks. Thus, it remains challenging to guarantee the execution efficiency of the edge services and the overall performance of the edge computing systems with limited ESs. In view of this challenge, a load-aware edge server placement method, named LESP, is devised for mobile edge computing in 5G networks. Technically, a decision tree is constructed to identify the MIBSs served by a definite ES and confirm the data transmission routes across MIBSs. Then, the non-dominated sorting genetic algorithm II (NSGA-II) is employed to obtain the balanced ES placement strategies. Furthermore, simple additive weighting (SAW) and multiple criteria decision making (MCDM) techniques are leveraged to recognize the optimal ES placement strategy. Finally, the experimental evaluations are implemented and the observed simulation results verify the efficiency and effectiveness of LESP.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Chen, M., Qian, Y., Hao, Y., Li, Y., Song, J.: Data-driven computing and caching in 5G networks: architecture and delay analysis. IEEE Wireless Commun. 25(1), 70–75 (2018)

    Article  Google Scholar 

  2. Sun, S., Rappaport, T.S., Shafi, M., Tang, P., Zhang, J., Smith, P.J.: Propagation models and performance evaluation for 5G millimeter-wave bands. IEEE Trans. Veh. Technol. 67(9), 8422–8439 (2018)

    Article  Google Scholar 

  3. Gringoli, F., Patras, P., Donato, C., Serrano, P., Grunenberger, Y.: Performance assessment of open software platforms for 5G prototyping. IEEE Wireless Commun. 25(5), 10–15 (2018)

    Article  Google Scholar 

  4. Skouroumounis, C., Psomas, C., Krikidis, I.: Heterogeneous FD-mm-wave cellular networks with cell center/edge users. IEEE Trans. Commun. 67(1), 791–806 (2019)

    Article  Google Scholar 

  5. Ordonez-Lucena, J., Ameigeiras, P., Lopez, D., Ramos-Munoz, J.J., Lorca, J., Folgueira, J.: Network slicing for 5G with SDN/NFV: concepts, architectures, and challenges. IEEE Commun. Mag. 55(5), 80–87 (2017)

    Article  Google Scholar 

  6. Duan, P., Jia, Y., Liang, L., Rodriguez, J., Huq, K.M.S., Li, G.: Space-reserved cooperative caching in 5G heterogeneous networks for industrial IoT. IEEE Trans. Industr. Inf. 14(6), 2715–2724 (2018)

    Article  Google Scholar 

  7. Rodrigues, T.G., Suto, K., Nishiyama, H., Kato, N.: Hybrid method for minimizing service delay in edge cloud computing through VM migration and transmission power control. IEEE Trans. Comput. 66(5), 810–819 (2016)

    Article  MathSciNet  Google Scholar 

  8. Li, H., Ota, K., Dong, M.: Learning IoT in edge: deep learning for the internet of things with edge computing. IEEE Netw. 32(1), 96–101 (2018)

    Article  Google Scholar 

  9. Kim, Y., Kwak, J., Chong, S.: Dual-side optimization for cost-delay tradeoff in mobile edge computing. IEEE Trans. Veh. Technol. 67(2), 1765–1781 (2017)

    Article  Google Scholar 

  10. Boulogeorgos, A.A.A., et al.: Terahertz technologies to deliver optical network quality of experience in wireless systems beyond 5G. IEEE Commun. Mag. 56(6), 144–151 (2018)

    Article  Google Scholar 

  11. Li, M., Yu, F.R., Si, P., Zhang, Y.: Green machine-to-machine communications with mobile edge computing and wireless network virtualization. IEEE Commun. Mag. 56(5), 148–154 (2018)

    Article  Google Scholar 

  12. Beyranvand, H., Lévesque, M., Maier, M., Salehi, J.A., Verikoukis, C., Tipper, D.: Toward 5G: FiWi enhanced LTE-A HetNets with reliable low-latency fiber backhaul sharing and WiFi offloading. IEEE/ACM Trans. Networking 25(2), 690–707 (2016)

    Article  Google Scholar 

  13. Mozaffari, M., Kasgari, A.T.Z., Saad, W., Bennis, M., Debbah, M.: Beyond 5G with UAVs: foundations of a 3D wireless cellular network. IEEE Trans. Wireless Commun. 18(1), 357–372 (2019)

    Article  Google Scholar 

  14. Richardson, T., Kudekar, S.: Design of low-density parity check codes for 5G new radio. IEEE Commun. Mag. 56(3), 28–34 (2018)

    Article  Google Scholar 

  15. Lyu, X., Tian, H., Ni, W., Zhang, Y., Zhang, P., Liu, R.P.: Energy-efficient admission of delay-sensitive tasks for mobile edge computing. IEEE Trans. Commun. 66(6), 2603–2616 (2018)

    Article  Google Scholar 

  16. Ning, Z., Kong, X., Xia, F., Hou, W., Wang, X.: Green and sustainable cloud of things: enabling collaborative edge computing. IEEE Commun. Mag. 57(1), 72–78 (2019)

    Article  Google Scholar 

  17. Bi, S., Zhang, Y.J.: Computation rate maximization for wireless powered mobile-edge computing with binary computation offloading. IEEE Trans. Wireless Commun. 17(6), 4177–4190 (2018)

    Article  Google Scholar 

  18. Wang, R., Yan, J., Wu, D., Wang, H., Yang, Q.: Knowledge-centric edge computing based on virtualized D2D communication systems. IEEE Commun. Mag. 56(5), 32–38 (2018)

    Article  Google Scholar 

  19. Wang, K., Yin, H., Quan, W., Min, G.: Enabling collaborative edge computing for software defined vehicular networks. IEEE Network 99, 1–6 (2018)

    Google Scholar 

  20. Hou, W., Ning, Z., Guo, L.: Green survivable collaborative edge computing in smart cities. IEEE Trans. Industr. Inf. 14(4), 1594–1605 (2018)

    Article  Google Scholar 

Download references

Acknowledgment

This work was supported by the National Key Research and Development Program of China (No. 2017YFB1400600). Besides, this research is supported by the National Natural Science Foundation of China under grant no. 61702277, no. 61872219 and no. 616722763. This research is also supported by College Students’ Enterprise and Entrepreneurship Education Program of NUIST, CSEEEP.

Author information

Authors and Affiliations

  1. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China

    Xiaolong Xu & Yuan Xue

  2. School of Information Science and Engineering, Qufu Normal University, Qufu, China

    Lianyong Qi

  3. Department of Electrical and Computer Engineering, University of Auckland, Auckland, New Zealand

    Xuyun Zhang

  4. School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, Hubei, China

    Shaohua Wan

  5. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Wanchun Dou

  6. School of Computing & Digital Technologies, Teesside University, Middlesbrough, UK

    Victor Chang

Authors
  1. Xiaolong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuan Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lianyong Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xuyun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shaohua Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wanchun Dou
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Victor Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Lianyong Qi or Wanchun Dou .

Editor information

Editors and Affiliations

  1. Laboratory for Analysis and Architecture, Toulouse, France

    Sami Yangui

  2. National Engineering School of Sfax, Sfax, Tunisia

    Ismael Bouassida Rodriguez

  3. Laboratory for Analysis and Architecture, Toulouse, France

    Khalil Drira

  4. RMIT University, Melbourne, VIC, Australia

    Zahir Tari

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xu, X. et al. (2019). Load-Aware Edge Server Placement for Mobile Edge Computing in 5G Networks. In: Yangui, S., Bouassida Rodriguez, I., Drira, K., Tari, Z. (eds) Service-Oriented Computing. ICSOC 2019. Lecture Notes in Computer Science(), vol 11895. Springer, Cham. https://doi.org/10.1007/978-3-030-33702-5_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33702-5_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33701-8

  • Online ISBN: 978-3-030-33702-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation