Have a personal or library account? Click to login

A Multi–Source Fluid Queue Based Stochastic Model of the Probabilistic Offloading Strategy in a MEC System With Multiple Mobile Devices and a Single MEC Server

By:
Open Access
|Mar 2022

References

  1. Anick, D., Mitra, D. and Sondhi, M. (1982). Stochastic theory of a data-handling system with multiple sources, Bell System Technical Journal 61(8): 1871–1894.10.1002/j.1538-7305.1982.tb03089.x
  2. Arunachalam, V., Gupta, V. and Dharmaraja, S. (2010). A fluid queue modulated by two independent birth-death processes, Computers and Mathematics with Applications 60(8): 2433–2444.10.1016/j.camwa.2010.08.039
  3. Bai, T., Pan, C., Deng, Y., Elkashlan, M., Nallanathan, A. and Hanzo, L. (2020). Latency minimization for intelligent reflecting surface aided mobile edge computing, IEEE Journal on Selected Areas in Communications 38(11): 2666–2682.10.1109/JSAC.2020.3007035
  4. Bista, B., Wang, J. and Takata, T. (2020). Probabilistic computation offloading for mobile edge computing in dynamic network environment, Internet of Things 11, Article no. 100225.
  5. Cardellini, V., Personé, V., Valerio, V., Facchinei, F., Grassi, V., Presti, F. and Piccialli, V. (2016). A game-theoretic approach to computation offloading in mobile cloud computing, Mathematical Programming 157(2): 421–449.10.1007/s10107-015-0881-6
  6. El-Baz, A., Tarabia, A. and Darwiesh, A. (2020). Cloud storage facility as a fluid queue controlled by Markovian queue, Probability in the Engineering and Informational Sciences: 1–17, DOI: 10.1017/S0269964820000613.10.1017/S0269964820000613
  7. Elwalid, A. and Mitra, D. (1995). Analysis, approximations and admission control of a multi-service multiplexing system with priorities, Proceedings of International Conference on Computer Communications, INFOCOM 1995, Boston, USA, pp. 463–472.
  8. Fiedler, M. and Voos, H. (2000). New results on the numerical stability of the stochastic fluid flow model analysis, Proceedings of the Networking 2000 Conference, Paris, France, pp. 446–457.
  9. Goścień, R. and Walkowiak, K. (2017). A column generation technique for routing and spectrum allocation in cloud-ready survivable elastic optical networks, International Journal of Applied Mathematics and Computer Science 27(3): 591–603, DOI: 10.1515/amcs-2017-0042.10.1515/amcs-2017-0042
  10. Hassan, M., Qi, W. and Chen, S. (2015). ELICIT: Efficiently identify computation-intensive tasks in mobile applications for offloading, Proceedings of IEEE International Conference on Networking, Architecture and Storage, NAS 2015, Boston, USA, pp. 12–22.
  11. Kim, J. and Krunz, M. (2000). Bandwidth allocation in wireless networks with guaranteed packet-loss performance, Mathematical Programming 8(3): 337–349.10.1109/90.851980
  12. Kulkarni, V. (1997). Fluid Models for Single Buffer Systems, CRC Press, Boca Raton.
  13. Lenin, R. and Parthasarathy, P. (2000). Fluid queues driven by an M/M/1/N queue, Mathematical Problems in Engineering 6(5): 439–460.10.1155/S1024123X00001423
  14. Li, K. (2019). How to stabilize a competitive mobile edge computing environment: A game theoretic approach, IEEE Access 7: 69960–69985.10.1109/ACCESS.2019.2919106
  15. Li, W. and Jin, S. (2021). Performance evaluation and optimization of a task offloading strategy on the mobile edge computing with edge heterogeneity, Journal of Supercomputing 77(11): 1286–12507, DOI: 10.1007/S11227-021-03781-W.10.1007/s11227-021-03781-w
  16. Lim, W., Luong, N., Hoang, D., Jiao, Y., Liang, Y., Yang, Q., Niyato, D. and Miao, C. (2020). Federated learning in mobile edge networks: A comprehensive survey, IEEE Communications Surveys and Tutorials 22(3): 2031–2063.10.1109/COMST.2020.2986024
  17. Liu, Y., Peng, M., Shou, G., Chen, Y. and Chen, S. (2020). Toward edge intelligence: Multi-access edge computing for 5G and internet of things, IEEE Internet of Things Journal 7(8): 6722–6747.10.1109/JIOT.2020.3004500
  18. Mao, B., wang, F. and Tian, N. (2012). Fluid model driven by an M/M/1 queue with multiple vacations and N-policy, Journal of Applied Mathematics and Computing 38(1): 119–131.10.1007/s12190-010-0467-7
  19. Mitra, D. (1988). Stochastic theory of a fluid model of producers and consumers coupled by a buffer, Advances in Applied Probability 20(1): 646–676.10.2307/1427040
  20. Mukherjee, M., Kumar, V., Kumar, S., Matamy, R., Mavromoustakis, C., Zhang, Q., Shojafar, M. and Mastorakis, G. (2020). Computation offloading strategy in heterogeneous fog computing with energy and delay constraints, Proceedings of IEEE International Conference on Communications, ICC 2020, Dublin, Ireland, pp. 1–5.
  21. Nouri, N., Abouei, J., Jaseemuddin, M. and Anpalagan, A. (2020). Joint access and resource allocation in ultradense mmWave NOMA networks with mobile edge computing, IEEE Internet of Things Journal 7(2): 1531–1547.10.1109/JIOT.2019.2956409
  22. Razaque, A., Aloqaily, M., Almiani, M., Jararweh, Y. and Srivastava, G. (2021). Efficient and reliable forensics using intelligent edge computing, Future Generation Computer Systems 118: 230–239, DOI: 10.1016/j.future.2021.01.012.10.1016/j.future.2021.01.012
  23. Sericola, B., Parthasarathy, P. and Vijayashree, K. (2005). Exact transient solution of an M/M/1 driven fluid queue, International Journal of Computer Mathematics 82(6): 659–671.10.1080/00207160512331329041
  24. Song, F., Ai, Z., Zhang, H., You, I. and Li, S. (2021). Smart collaborative balancing for dependable network components in cyber-physical systems, IEEE Transactions on Industrial Informatics 17(10): 6916–6924.10.1109/TII.2020.3029766
  25. Virtamo, J. and Norros, I. (1994). Fluid queue driven by an M/M/1 queue, Queueing Systems 16(3): 373–386.10.1007/BF01158963
  26. Wu, H., Sun, Y. and Wolter, K. (2020). Energy-efficient decision making for mobile cloud offloading, IEEE Transactions on Cloud Computing 8(2): 570–584.10.1109/TCC.2018.2789446
  27. Xu, X., Shen, B., Ding, S., Srivstava, G., Bilal, M., Khosravi, M., Menon, V., Jan, M. and Wang, M. (2020). Service offloading with deep Q-network for digital twinning empowered internet of vehicles in edge computing, IEEE Transactions on Industrial Informatics 18(2): 1414–1423, DOI: 10.1109/TII.2020.3040180.10.1109/TII.2020.3040180
  28. Zeifman, A., Razumchik, R., Satin, Y., Kiseleva, K., Korotysheva, A. and Korolev, V. (2018). Bounds on the rate of convergence for one class of inhomogeneous Markovian queueing models with possible batch arrivals and services, International Journal of Applied Mathematics and Computer Science 28(1): 141–154, DOI: 10.2478/amcs-2018-0011.10.2478/amcs-2018-0011
  29. Zeifman, A., Satin, Y., Kryukova, A., Razumchik, R., Kiseleva, K. and Shilova, G. (2020). On three methods for bounding the rate of convergence for some continuous-time Markov chains, International Journal of Applied Mathematics and Computer Science 30(2): 251–266, DOI: 10.34768/amcs-2020-0020.
  30. Zhao, T., Zhou, S., Guo, X. and Niu, Z. (2017). Tasks scheduling and resource allocation in heterogeneous cloud for delay-bounded mobile edge computing, Proceedings of IEEE International Conference on Communications, ICC 2017, Paris, France, pp. 1–7.
DOI: https://doi.org/10.34768/amcs-2022-0010 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 125 - 138
Submitted on: Aug 2, 2021
Accepted on: Dec 28, 2021
Published on: Mar 31, 2022
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2022 Huan Zheng, Shunfu Jin, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.