Skip to main content
SpringerLink
Log in

QUICK: QoS-guaranteed efficient cloudlet placement in wireless metropolitan area networks

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

This article defines the QoS-guaranteed efficient cloudlet deployment problem in wireless metropolitan area network, which aims to minimize the average access delay of mobile users, i.e., the average delay when service requests are successfully sent and being served by cloudlets. Meanwhile, we try to optimize total deployment cost represented by the total number of deployed cloudlets. For the first target, both un-designated capacity and constrained capacity cases are studied, and we have designed efficient heuristic and clustering algorithms, respectively. We show our algorithms are more efficient than the existing algorithm. For the second target, we formulate an integer linear programming to minimize the number of used cloudlets with given average access delay requirement. A clustering algorithm is devised to guarantee the scalability. For a special case of the deployment cost optimization problem where all cloudlets’ computing capabilities have been given, i.e., designated capacity, an efficient heuristic algorithm is further proposed to minimize the number of cloudlets. We finally evaluate the performance of proposed algorithms through extensive experimental simulations. Simulation results demonstrate the proposed algorithms are more than \(46\%\) efficient than existing algorithms on the average cloudlet access delay. Compared with existing algorithms, our proposed clustering and heuristic algorithms can reduce the number of deployed cloudlets by about \(50\%\) averagely, owing to the calculation processes of shortest paths between APs and the sorting processes of user access delays.

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
Fig. 11

Similar content being viewed by others

Notes

  1. In this work, we only focus on the offline case and refine the offline algorithm originally presented in the article [29]. Similarly, the competitive analysis is omitted because the enhanced algorithms are as similar as the algorithms mentioned in [29]

  2. Following [29] capacity means the ability to handle user requests which is represented by the maximum number of user requests one cloudlet can receive. In the following paragraphs, we do not rank capacity according to CPU cycles or data storage sizes one cloudlet can offer, as well as the case when one cloudlet might have a better CPU, while the other has more memory.

  3. To the best of our knowledge, there are numerous clustering methods. We only choose one typical clustering method in this work to better present the comparisons with existing works and to enlighten the new problem, the DBOCP problem and its solutions.

  4. The heuristic algorithm proposed by [29], where each cloudlet should be placed to a location that can cover as many user requests as possible.

References

  1. Barioni MCN, Razente HL, Traina AJ, Traina C (2008) Accelerating k-medoid-based algorithms through metric access methods. J Syst Softw 81(3):343–355

    Article  Google Scholar 

  2. Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Edition of the Mcc workshop on mobile cloud computing, pp 13–16. ACM

  3. Bourdena A, Mavromoustakis C, Mastorakis G, Rodrigues J, Dobre C (2015) Using socio-spatial context in mobile cloud offload process for energy conservation in wireless devices. IEEE Trans Cloud Comput

  4. Cai W, Leung VC, Hu L (2014) A cloudlet-assisted multiplayer cloud gaming system. Mob Netw Appl 19(2):144–152

    Article  Google Scholar 

  5. Charikar M, Guha S, Tardos Shmoys DB (1999) A constant-factor approximation algorithm for the k -median problem (extended abstract). In: ACM Symposium on Theory of Computing, pp 1–10

  6. Chen L, Wu J, Dai HN, Huang X (2018) Brains: Joint bandwidth-relay allocation in multi-homing cooperative d2d networks. IEEE Trans Veh Technol 1–12. https://doi.org/10.1109/TVT.2018.2799970

  7. Chen L, Wu J, Zhang XX, Zhou G (2018) Tarco: Two-stage auction for d2d relay aided computation resource allocation in hetnet. IEEE Trans Serv Comput 1–14. https://doi.org/10.1109/TSC.2018.2792024

  8. Cormen TH, Leiserson CE, Rivest RL, Stein C (2001) Introduction to algorithms. second edition

  9. Fan Q, Ansari N (2017) Cost aware cloudlet placement for big data processing at the edge. In: IEEE International Conference on Communications, pp 1–6

  10. Fazio P, De Rango F, Tropea M (2017) Prediction and qos enhancement in new generation cellular networks with mobile hosts: a survey on different protocols and conventional/unconventional approaches. IEEE Commun Surv Tutor 19(3):1822–1841

    Article  Google Scholar 

  11. Gu L, Zeng D, Barnawi A, Guo S, Stojmenovic I (2015) Optimal task placement with qos constraints in geo-distributed data centers using dvfs. IEEE Trans Comput 64(7):2049–2059

    Article  MathSciNet  Google Scholar 

  12. Gt-itm (2017). http://www.cc.gatech.edu/projects/gtitm/. [Online; accessed 10-May-2017]

  13. Hoang DT, Niyato D, Wang P (2012) Optimal admission control policy for mobile cloud computing hotspot with cloudlet. In: Wireless Communications and Networking Conference (WCNC), pp 3145–3149. IEEE, Shanghai, China

  14. Huang H, Guo S (2017) Service provisioning update scheme for mobile application users in a cloudlet network. In: IEEE International Conference on Communications (ICC), pp 1–6

  15. Ieee standards for local and metropolitan area networks: Overview and architecture (ansi) (1990)

  16. Jia M, Cao J, Liang W (2017) Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks. IEEE Trans Cloud Comput 5(4):725–737

    Article  Google Scholar 

  17. Jin A, Song W, Wang P, Niyato D, Ju P (2016) Auction mechanisms toward efficient resource sharing for cloudlets in mobile cloud computing. IEEE Trans Serv Comput 9(6):895–909

    Article  Google Scholar 

  18. Kosta S, Aucinas A, Hui P, Mortier R, Zhang X (2012) Thinkair: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading. In: Proceedings of Infocom, pp 945–953. IEEE

  19. Lp-solve (2003). http://lpsolve.sourceforge.net

  20. Ma L, Wu J, Chen L (2017) Dota: Delay bounded optimal cloudlet deployment and user association in wmans. In: 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp 196–203. IEEE, Madrid, Spain

  21. Pang Z, Sun L, Wang Z, Tian E, Yang S (2015) A survey of cloudlet based mobile computing. In: International Conference on Cloud Computing and Big Data, pp 268–275. IEEE, Shanghai, China

  22. Ren S, Schaar MVD (2014) Dynamic scheduling and pricing in wireless cloud computing. IEEE Trans Mob Comput 13(10):2283–2292

    Article  Google Scholar 

  23. Rimal BP, Van DP, Maier M (2017) Cloudlet enhanced fiber-wireless access networks for mobile-edge computing. IEEE Trans Wirel Commun 16(6):3601–3618

    Article  Google Scholar 

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

  25. Shaukat U, Ahmed E, Anwar Z, Xia F (2016) Cloudlet deployment in local wireless networks: motivation, architectures, applications, and open challenges. J Netw Comput Appl 62(3):18–40

    Article  Google Scholar 

  26. Vaquero LM, Rodero-Merino L (2014) Finding your way in the fog: towards a comprehensive definition of fog computing. ACM SIGCOMM Comput Commun Rev 44(5):27–32

    Article  Google Scholar 

  27. Verbelen T, Simoens P, De Turck F, Dhoedt B (2012) Cloudlets: bringing the cloud to the mobile user. In: Proceedings of the Third ACM workshop on Mobile Cloud Computing and Services, pp 29–36. ACM, Low Wood Bay, UK

  28. Xu Z, Liang W, Xu W, Jia M (2015) Capacitated cloudlet placements in wireless metropolitan area networks. In: Local Computer Networks, pp 570–578

  29. Xu Z, Liang W, Xu W, Jia M, Guo S (2016) Efficient algorithms for capacitated cloudlet placements. IEEE Trans Parallel Distrib Syst 27(10):2866–2880

    Article  Google Scholar 

  30. Zhang Y, Liu H, Jiao L, Fu X (2012) To offload or not to offload: an efficient code partition algorithm for mobile cloud computing. In: the 1st International Conference on Cloud Networking (CLOUDNET), pp 80–86. IEEE, Paris, France

  31. Zhao L, Sun W, Shi Y, Liu J (2018) Optimal placement of cloudlets for access delay minimization in sdn-based internet of things networks. IEEE Internet Things J 5(2):1334–1344

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Guangdong University of Technology (GDUT), Guangzhou, 510006, China

    Long Chen, Jigang Wu, Gangqiang Zhou & Longjie Ma

Authors
  1. Long Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jigang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gangqiang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Longjie Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jigang Wu.

Additional information

This work was partially supported by the National Key R&D Program of China under Grant No. 2018YFB1003201. It was also supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61702115 and 61672171. Part of the work was funded by China Postdoctoral Science Foundation under Grant No. 2017M622632.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, L., Wu, J., Zhou, G. et al. QUICK: QoS-guaranteed efficient cloudlet placement in wireless metropolitan area networks. J Supercomput 74, 4037–4059 (2018). https://doi.org/10.1007/s11227-018-2412-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-018-2412-8

Keywords

Search

Navigation