Modeling of Pre-Fog Computing for the Tactile Internet

Authors

  • A. A. Kovalenko Kharkiv National University of Radio Electronics
  • R. O. Yaroshevych Kharkiv National University of Radio Electronics

DOI:

https://doi.org/10.31649/1997-9266-2024-172-1-65-73

Keywords:

Tactile Internet, computer network, fog computing, cloud network, edge cluster, pre-fog cluster, traffic transfer model

Abstract

In today’s world of information technologies and in the conditions of growing load on computer networks, it is an urgent task to optimize and improve their performance by effective management of resources and latency reduction. Construction of information technology architecture can reduce latency by moving cloud structures to the edge of radio access networks. Edge computing, which involves processing data at the edge of the computer network, reduces latency and improves response time, and using bandwidth at the edge helps reduce bandwidth usage. Pre-fog computing is an important strategy for improving computer networks in the Tactile Internet environment. The aim of this paper is to model the pre-fog computing of a hierarchical edge cloud network, aimed at determining the delay in traffic transmission, optimizing performance, and managing resources. The object of research is a model of a hierarchical network of edge clouds, including edge and pre-fog clusters, fog and cloud computing. The subject of the study is a model of traffic transmission in a hierarchical cloud network to ensure optimal resource management and traffic transmission, taking into account the delay requirements. In this paper, we model a hierarchical edge cloud network, develop a traffic transfer model, and analyze the delay of clusters of the first and second levels of a hierarchical cloud network. The hierarchical edge cloud network is designed to optimize data transmission and resource management. Edge clusters have limited computing capabilities, so they are connected to more powerful pre-fog clusters. In addition, fog computing ensures coordinated interaction between edge clusters across the entire computer network. The traffic transmission model allows achieving the required latency, efficiency, security, and high availability, making it relevant and useful for the Tactile Internet environment. The advantages of the modeled computer network are reduced latency from the data source to the users and reduced risk of network congestion. This provides flexibility in building the network and increases its availability, which meets the requirements of the Tactile Internet.

Author Biographies

A. A. Kovalenko, Kharkiv National University of Radio Electronics

 Dr. Sc. (Eng.), Professor, Head of the Chair of Electronic Computers

R. O. Yaroshevych, Kharkiv National University of Radio Electronics

Assistant of the Chair of Electronic Computers

References

K. Kaur, A. Singh, and A. Sharma, “A systematic review on resource provisioning in fog computing. Transactions on Emerging Telecommunications Technologies”, vol. 34, no. 2, pp. 1-36, January 2023. https://doi.org/10.1002/ett.4731 .

A. Aijaz, “Toward human-in-the-loop mobile networks: A radio resource allocation perspective on haptic communications,” IEEE Trans. Wireless Commun., vol. 17, no. 7, pp. 4493-4508, July 2018. https://doi.org/10.1109/twc.2018.2825985 .

Fog computing and Mobile Edge Cloud Gain Momentum Open Fog Consortium ETSI MEC and Cloudlets, 2015. [Electronic resource]. Available: https://yucianga.info/wp-content/uploads/2015/11/15_11_22-_Fog_computing_and_mobile_edge_cloud_gain_momentum_Open_Fog_Consortium-ETSI_MEC-Cloudlets_v1_1.pdf . Accessed on: Feb 08, 2024.

X. Sun, and N. Ansari, “Green Cloudlet Network: A Distributed Green Mobile Cloud Network,” IEEE Network, vol. 31, no. 1, pp. 64-70, January 2017. http://dx.doi.org/10.1109/mnet.2017.1500293nm .

R. Yaroshevych, V. Tkachov, A. Kovalenko, and D. Rosinskyi “Modelling the Domain Architecture of the Tactile Internet Using a Foggy Infrastructure,” IEEE 9th International Conference on Problems of Infocommunications, Science and Technology (PIC S&T). October 2022, pp. 512-516. https://doi.org/10.1109/PICST57299.2022.10238653 .

Aazam, Mohammad et al. “PRE-Fog: IoT trace based probabilistic resource estimation at Fog.” 13th IEEE Annual Consumer Communications & Networking Conference (CCNC) , January 2016, pp. 12-17.

A. Muthanna et al. “Secure and Reliable IoT Networks Using Fog Computing with Software-Defined Networking and Blockchain,” Journal of Sensor and Actuator Networks, vol. 8, no. 1, pp. 15, February 2019. https://doi.org/10.3390/jsan8010015 .

A. Mukherjee, D. De, and D. G. Roy, “Power and Latency Aware Cloudlet Selection Strategy for Multi-Cloudlet Environment,” IEEE Transactions on Cloud Computing, vol. 7, no. 1, pp. 141-154, July 2019. http://dx.doi.org/10.1109/tcc.2016.2586061 .

M. Jia, W. Liang, Z. Xu, M. Huang, and Y. Ma, “QoS-Aware Cloudlet Load Balancing in Wireless Metropolitan Area Networks,” IEEE Transactions on Cloud Computing, vol. 8, no. 2, pp. 623-634, April-June 2020. http://dx.doi.org/10.1109/tcc.2017.2786738 .

K. Intharawijitr, K. Iida, and H. Koga, “Analysis of fog model considering computing and communication latency in 5G cellular networks,” IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), March 2016, pp. 1-4. https://doi.org/10.1109/PERCOMW.2016.7457059 .

K. Gai, M. Qiu, H. Zhao, L. Tao, and Z. Zong, “Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing,” Journal of Network and Computer Applications, vol. 59, pp. 46-54, January 2016. http://dx.doi.org/10.1016/j.jnca.2015.05.016 .

A. Kartun-Giles, S. Jayaprakasam, and S. Kim, “Euclidean Matchings in Ultra-Dense Networks,” IEEE Communications Letters, vol. 22, no. 6, pp. 1216-1219, January 2018. http://dx.doi.org/10.1109/lcomm.2018.2799207 .

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Published

2024-02-27

How to Cite

[1]
A. A. Kovalenko and R. O. Yaroshevych, “Modeling of Pre-Fog Computing for the Tactile Internet”, Вісник ВПІ, no. 1, pp. 65–73, Feb. 2024.

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Information technologies and computer sciences

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