Constrained 𝐤 -means algorithm for resource allocation in mobile cloudlets

Rasim M. Alguliyev; Ramiz M. Aliguliyev; Rashid G. Alakbarov

Kybernetika (2023)

  • Volume: 59, Issue: 1, page 88-109
  • ISSN: 0023-5954

Abstract

top
With the rapid increase in the number of mobile devices connected to the Internet in recent years, the network load is increasing. As a result, there are significant delays in the delivery of cloud resources to mobile users. Edge computing technologies (edge, cloudlet, fog computing, etc.) have been widely used in recent years to eliminate network delays. This problem can be solved by allocating cloud resources to the cloudlets that are close to users. The article proposes a clustering-based model for the optimal allocation of cloud resources among cloudlets. The proposed model takes into account user activity, usage frequency of cloud resources, the physical distance between users and cloud resources, as well as the storage capacity of cloudlets for optimal allocation of cloud resources in cloudlets. The proposed model was formalized as a constrained k -means method and an algorithm was developed to solve it. The MATLAB 2022a toolkit was used to evaluate the efficiency of the proposed algorithm. The obtained results revealed that the algorithm is promising.

How to cite

top

Alguliyev, Rasim M., Aliguliyev, Ramiz M., and Alakbarov, Rashid G.. "Constrained $\mathbf {k}$-means algorithm for resource allocation in mobile cloudlets." Kybernetika 59.1 (2023): 88-109. <http://eudml.org/doc/299056>.

@article{Alguliyev2023,
abstract = {With the rapid increase in the number of mobile devices connected to the Internet in recent years, the network load is increasing. As a result, there are significant delays in the delivery of cloud resources to mobile users. Edge computing technologies (edge, cloudlet, fog computing, etc.) have been widely used in recent years to eliminate network delays. This problem can be solved by allocating cloud resources to the cloudlets that are close to users. The article proposes a clustering-based model for the optimal allocation of cloud resources among cloudlets. The proposed model takes into account user activity, usage frequency of cloud resources, the physical distance between users and cloud resources, as well as the storage capacity of cloudlets for optimal allocation of cloud resources in cloudlets. The proposed model was formalized as a constrained $k$-means method and an algorithm was developed to solve it. The MATLAB 2022a toolkit was used to evaluate the efficiency of the proposed algorithm. The obtained results revealed that the algorithm is promising.},
author = {Alguliyev, Rasim M., Aliguliyev, Ramiz M., Alakbarov, Rashid G.},
journal = {Kybernetika},
keywords = {mobile cloud computing; edge computing; cloudlet; cloud resources; constrained $k$-means},
language = {eng},
number = {1},
pages = {88-109},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Constrained $\mathbf \{k\}$-means algorithm for resource allocation in mobile cloudlets},
url = {http://eudml.org/doc/299056},
volume = {59},
year = {2023},
}

TY - JOUR
AU - Alguliyev, Rasim M.
AU - Aliguliyev, Ramiz M.
AU - Alakbarov, Rashid G.
TI - Constrained $\mathbf {k}$-means algorithm for resource allocation in mobile cloudlets
JO - Kybernetika
PY - 2023
PB - Institute of Information Theory and Automation AS CR
VL - 59
IS - 1
SP - 88
EP - 109
AB - With the rapid increase in the number of mobile devices connected to the Internet in recent years, the network load is increasing. As a result, there are significant delays in the delivery of cloud resources to mobile users. Edge computing technologies (edge, cloudlet, fog computing, etc.) have been widely used in recent years to eliminate network delays. This problem can be solved by allocating cloud resources to the cloudlets that are close to users. The article proposes a clustering-based model for the optimal allocation of cloud resources among cloudlets. The proposed model takes into account user activity, usage frequency of cloud resources, the physical distance between users and cloud resources, as well as the storage capacity of cloudlets for optimal allocation of cloud resources in cloudlets. The proposed model was formalized as a constrained $k$-means method and an algorithm was developed to solve it. The MATLAB 2022a toolkit was used to evaluate the efficiency of the proposed algorithm. The obtained results revealed that the algorithm is promising.
LA - eng
KW - mobile cloud computing; edge computing; cloudlet; cloud resources; constrained $k$-means
UR - http://eudml.org/doc/299056
ER -

References

top
  1. Ahmed, A., Ahmed, E., , In: 2016 10th International Conference on Intelligent Systems and Control 2016, pp. 1-8. DOI
  2. Ahmed, E., Akhunzada, A., Whaiduzzaman, M., Gani, A., Hamid, S. H. Ab, Buyya, R., , Simul. Modelling Practice Theory 50 (2015), 42-56. DOI
  3. Alakberov, R., , Int. J. Wireless Networks Broadband Technol. 10 (2021), 1, 32-44. DOI
  4. Alakberov, R. G., , Int. J. Computer Network Inform. Security 14 (2022), 3, 75-87. DOI
  5. Alakbarov, R., Alakbarov, O., , Int. J. Computer Networks Commun. 11 (2019), 1 93-107. DOI
  6. Ala'anzy, M., Othman, M., Hanapi, Z. M., Alrshah, M. A., , Sensors 21 (2021), 7308, 1-19. DOI
  7. Alguliyev, R. M., Alakbarov, R. G., Integer programming models for task scheduling and resource allocation in mobile cloud computing., Int. J. Computer Network Inform. Security, 2023 (in press). 
  8. Asghar, H., Jung, E. S., , arXiv.org 2022, 1-19. DOI
  9. Azad, P., Navimipour, N. J., , Int. J. Cloud Appl. Computing 7 (2017), 4, 20-40. DOI
  10. Bagirov, A. M., , Pattern Recognition 41 (2008), 10, 3192-3199. DOI
  11. Bindu, G. H., Ramani, K., Bindu, C. S., , Int. J. Internet Protocol Technol. 11 (2018), 4, 242-249. DOI
  12. Bradley, P. S., Bennett, K. P., Demiriz, A., Constrained k-means clustering., Technical Report MSR-TR-2000-65, Microsoft Research, Redmond 2000, pp. 1-8. MR1770524
  13. Chen, X., Jiao, L., Li, W. Z., Fu, X. M., , IEEE/ACM Trans. Networking 24 (2015), 5, 2795-2808. DOI
  14. Chen, L., Zhou, S., Xu, J., , IEEE ACM Trans. Networking 26 (2018), 4, 1619-1632. DOI
  15. Dalan, D., An overview of edge computing., Int. J. Engrg. Res. Technol. 7 (2019), 5, 1-4. MR3828166
  16. Hu, M., Zhuang, L., Wu, D., Zhou, Y. P., Chen, X., Xiao, L., , IEEE Trans. Parallel Distributed Systems 30 (2019), 8, 1802-1815. DOI
  17. Liao, K., Yang, J., Miao, L., , In: International Conference on Network Communication and Information Security 2021, pp. 1-9. MR4439438DOI
  18. Lin, L., Li, P., Xiong, J., Lin, M., , In: 2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) 2018, pp. 165-170. DOI
  19. Lin, R., Zhou, Z., Luo, S., Xiao, Y., Zukerman, M., , IEEE Trans. Wireless Commun. 19 (2020), 12, 8179-8194. DOI
  20. Luo, Q., Hu, S., Li, C., Li, G., Shi, W., , IEEE Commun. Survey Tutorials 23 (2021), 4, 2131-2165. DOI
  21. Mach, P., Becvar, Z., , IEEE Commun. Surveys Tutorials 19 (2017), 3, 1628-1656. MR3476603DOI
  22. Mike, J., Cao, J., Liang, W., , IEEE Trans. Cloud Computing 5 (2017), 4, 725-737. DOI
  23. Mukherjee, A., Priti, D., De, D., Buyya, R., , J. Supercomputing 75 (2019), 11, 7125-7146. DOI
  24. Nasr, A., El-Bahnasawy, N. A., Attiya, G., El-Sayed, A., Cloudlet scheduling based load balancing on virtual machines in cloud computing environment., J. Internet Technol. 20 (2019), 5, 1376-1378. 
  25. Sachula, M., Wang, Y., Miao, Z., Sun, K., , Peer-to-Peer Networking Appl. 11 (2018), 3, 462-472. DOI
  26. Sajnani, D. K., Mahesar, A. R., Lakhan, A., Jamali, I. A., , Commun. Network 10 (2018), 4, Article ID 87708. DOI
  27. Shen, Y., Bao, Z., Qin, X., Shen, J., , World Wide Web 20 (2016), 155-173. DOI
  28. Shenoy, K., Bhokare, P., Pai, U., , Int. J. Sci. Res. 4 (2015), 6, 55-56. DOI
  29. Shreya, G., Mukherjee, A., Ghosh, S., Buyya, R., , IEEE Trans. Network Science Engrg. 7 (2019), 4, 2271-2285. DOI
  30. Somula, R. S., Ra, S., , Scalable Computing: Practice and Experience 19 (2018) 4, 309-337. DOI
  31. Vencalek, O., Hlubinka, D., , Kybernetika 57 (2021), 1, 15-37. MR4231854DOI
  32. Wang, X. Y., Ning, Z. L., Guo, S., , IEEE Trans. Parallel Distributed Systems 32 (2020), 2, 411-425. DOI
  33. Yang, L. C., Zhang, H. L., Li, X., Ji, H., Leung, V. C. M., , IEEE ACM Trans. Networking 26 (2018), 6, 2762-2773. DOI
  34. Yuyi, M., You, C., Zhang, J., Huang, K., Letaief, K., , IEEE Commun. Surveys Tutorials 19 (2017), 4, 2322-2358. DOI
  35. Zhang, F., Ge, J., Li, Z., Li, C., Wong, C., Kong, L., Luo, B., Chang, V., , Future Generation Computer Systems 87 (2018), 438-456. DOI
  36. Zhang, P., Zhou, M., , IEEE Trans. Automat. Sci. Engrg. 15 (2018), 2, 772-783. DOI

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

                
Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.