PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

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

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Mobile edge computing (MEC) is one of the key technologies to achieve high bandwidth, low latency and reliable service in fifth generation (5G) networks. In order to better evaluate the performance of the probabilistic offloading strategy in a MEC system, we give a modeling method to capture the stochastic behavior of tasks based on a multi-source fluid queue. Considering multiple mobile devices (MDs) in a MEC system, we build a multi-source fluid queue to model the tasks offloaded to the MEC server. We give an approach to analyze the fluid queue driven by multiple independent heterogeneous finite-state birth-and-death processes (BDPs) and present the cumulative distribution function (CDF) of the edge buffer content. Then, we evaluate the performance measures in terms of the utilization of the MEC server, the expected edge buffer content and the average response time of a task. Finally, we provide numerical results with some analysis to illustrate the feasibility of the stochastic model built in this paper.
Rocznik
Strony
125--138
Opis fizyczny
Bibliogr. 30 poz., rys., tab., wykr.
Twórcy
autor
  • School of Information Science and Engineering Yanshan University No. 438 West Hebei Avenue, Qinhuangdao 066004, China; Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University No. 438 West Hebei Avenue, Qinhuangdao 066004, China
autor
  • School of Information Science and Engineering Yanshan University No. 438 West Hebei Avenue, Qinhuangdao 066004, China; Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University No. 438 West Hebei Avenue, Qinhuangdao 066004, China
Bibliografia
  • [1] Anick, D., Mitra, D. and Sondhi, M. (1982). Stochastic theory of a data-handling system with multiple sources, Bell SystemTechnical Journal 61(8): 1871–1894.
  • [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.
  • [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.
  • [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.
  • [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.
  • [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] 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.
  • [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.
  • [14] Li, K. (2019). How to stabilize a competitive mobile edge computing environment: A game theoretic approach, IEEE Access 7: 69960–69985.
  • [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.
  • [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.
  • [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.
  • [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.
  • [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.
  • [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.
  • [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.
  • [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.
  • [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.
  • [25] Virtamo, J. and Norros, I. (1994). Fluid queue driven by an M/M/1 queue, Queueing Systems 16(3): 373–386.
  • [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.
  • [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.
  • [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.
  • [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.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-81e85b2e-8a41-4950-a27b-ab8b4b76bc7d
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.