Optimizing System Availability in Client-Server Network through Fog Computing: A Stochastic Model with Foggy Markovian Paths
Keywords:
Network, Availability , Computing, ReliabilityAbstract
The main goal of this paper's extensive analysis of a client-server fog computing network is to increase system availability. Fog computing, which extends cloud computing to the network's edge, necessitates a robust and reliable architecture to handle distributed computational tasks effectively. To achieve this, the paper introduces a sophisticated architecture comprising five distinct subsystems: A, B, C, D, and E. Each subsystem plays a critical role in ensuring the seamless operation of the network. Subsystem A represents the clients, the devices or applications that generate computational tasks. Subsystem B consists of fog nodes strategically placed closer to the clients to process data with minimal latency. Subsystem C includes the servers, which provide more substantial computational power and storage capacity. Subsystems D and E function as first- and second-level load balancers to distribute the workload efficiently across the network. The arrangement of these subsystems is meticulously designed to enhance the overall performance and availability of the network. The system can distribute and manage computational tasks more effectively by organizing clients, fog nodes, servers, and load balancers in a series-parallel configuration. This setup allows optimal resource utilization and ensures the network can handle varying loads without compromising availability. To model the availability dynamics of the network, the study employs differential-difference equations and a transition diagram. These mathematical tools help understand the system's long-run availability under different conditions. The analysis involves conducting numerical experiments thoroughly documented using tables and graphs. These visual aids effectively illustrate how various network parameters influence the optimization of system availability. The findings from these experiments underscore the vital role of load balancers and fog nodes configured in a series-parallel arrangement. This configuration not only facilitates optimal task distribution but also significantly boosts the overall availability of the system. The study concludes by emphasizing the effectiveness of this approach, highlighting it as a strategic method to enhance system availability in client-server fog computing networks. The results of this study provide valuable insights for researchers, system administrators, and network architects. By demonstrating the benefits of a series-parallel configuration of fog nodes and load balancers, the paper offers practical guidance for improving the performance and reliability of fog computing environments. These findings can help stakeholders design more resilient and efficient networks, ultimately advancing the field of fog computing.
References
[1] Mahmud, R., Kotagiri, R., & Buyya, R. (2018). Fog computing: a taxonomy, survey and future directions. In Internet of everything: algorithms, methodologies, technologies and perspectives (pp. 103–130). Springer. https://doi.org/10.1007/978-981-10-5861-5_5
[2] Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R. H., Morrow, M. J., & Polakos, P. A. (2017). A comprehensive survey on fog computing: state-of-the-art and research challenges. IEEE communications surveys & tutorials, 20(1), 416–464. DOI:10.1109/COMST.2017.2771153
[3] Yi, S., Li, C., & Li, Q. (2015). A survey of fog computing: concepts, applications and issues [presentation]. Proceedings of the 2015 workshop on mobile big data (pp. 37–42).
[4] Zhang, P. Y., Zhou, M. C., & Fortino, G. (2018). Security and trust issues in Fog computing: a survey. Future generation computer systems, 88, 16–27. DOI:10.1016/j.future.2018.05.008
[5] Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K., Jalali, F., Niakanlahiji, A., …& Jue, J. P. (2019). All one needs to know about fog computing and related edge computing paradigms: a complete survey. Journal of systems architecture, 98, 289–330. DOI:10.1016/j.sysarc.2019.02.009
[6] Stojmenovic, I., Wen, S., Huang, X., & Luan, H. (2016). An overview of Fog computing and its security issues. Concurrency and computation: practice and experience, 28(10), 2991–3005. DOI:10.1002/cpe.3485
[7] Alli, A. A., & Alam, M. M. (2020). The fog cloud of things: a survey on concepts, architecture, standards, tools, and applications. Internet of things (Netherlands), 9, 100177. DOI:10.1016/j.iot.2020.100177
[8] Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012). Fog computing and its role in the internet of things [presentation]. Proceedings of the first edition of the mcc workshop on mobile cloud computing (pp. 13–16). https://doi.org/10.1145/2342509.234251
[9] Caiza, G., Saeteros, M., Oñate, W., & Garcia, M. V. (2020). Fog computing at industrial level, architecture, latency, energy, and security: a review. Heliyon, 6(4). DOI:10.1016/j.heliyon.2020.e03706
[10] Hu, P., Dhelim, S., Ning, H., & Qiu, T. (2017). Survey on fog computing: architecture, key technologies, applications and open issues. Journal of network and computer applications, 98, 27–42. DOI:10.1016/j.jnca.2017.09.002
[11] Yusuf, I., & Auta, A. A. (2021). Availability analysis of a distributed system with homogeneity in client and server under four different maintenance options. Life cycle reliability and safety engineering, 10(4), 355–371. DOI:10.1007/s41872-021-00177-w
[12] Melnik, E. V., Klimenko, A. B., & Ivanov, D. Y. (2018). Fog-computing concept usage as means to enhance information and control system reliability. Journal of physics: conference series (Vol. 1015, p. 32175). IOP Publishing. DOI: 10.1088/1742-6596/1015/3/032175
[13] Pereira, J., Ricardo, L., Luís, M., Senna, C., & Sargento, S. (2019). Assessing the reliability of fog computing for smart mobility applications in VANETs. Future generation computer systems, 94, 317–332. DOI:10.1016/j.future.2018.11.043
[14] Al-khafajiy, M., Baker, T., Al-Libawy, H., Maamar, Z., Aloqaily, M., & Jararweh, Y. (2019). Improving fog computing performance via Fog-2-Fog collaboration. Future generation computer systems, 100, 266–280. DOI:10.1016/j.future.2019.05.015
[15] Chen, Y. F., Huang, D. H., Huang, C. F., & Lin, Y. K. (2020). Reliability evaluation for a cloud computer network with fog computing [presentation]. 2020 IEEE 20th international conference on software quality, reliability and security companion (QRS-C) (pp. 682–683). 10.1109/QRS-C51114.2020.00119
[16] Montoya-Munoz, A. I., & Rendon, O. M. C. (2020). An approach based on fog computing for providing reliability in iot data collection: a case study in a colombian coffee smart farm. Applied sciences (Switzerland), 10(24), 1–16. DOI:10.3390/app10248904
[17] Kabeer, M., Yusuf, I., & Sufi, N. A. (2023). Distributed software defined network-based fog to fog collaboration scheme. Parallel computing, 117, 103040. DOI:10.1016/j.parco.2023.103040
[18] Das, R., & Inuwa, M. M. (2023). A review on fog computing: Issues, characteristics, challenges, and potential applications. Telematics and informatics reports, 10, 100049. DOI:10.1016/j.teler.2023.100049
Metrics
