[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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
]Search in Google Scholar
[
Kulkarni, V. (1997). Fluid Models for Single Buffer Systems, CRC Press, Boca Raton.
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
Virtamo, J. and Norros, I. (1994). Fluid queue driven by an M/M/1 queue, Queueing Systems 16(3): 373–386.10.1007/BF01158963
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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.
]Search in Google Scholar
