Advanced search
1 file | 810.28 KB Add to list
Author
Organization
Abstract
In recent years, the continuing growth of the network edge, along with increasing user demands, has led to the need for increasingly complex and responsive management strategies for edge services. Many of these strategies are cloudbased, offering near-perfect solutions at the cost of requiring massive computational power, or edge-based, offering reactive strategies to changing edge conditions. This paper presents a decentralized, pro-active Quality of Experience (QoE) based architecture designed to run on edge nodes, which allows nodes to predict optimal service providers (fog nodes) in advance and request their services. The concepts behind the components of the architecture are explained, as well as geometry-inspired design decisions to limit model size. Evaluations on an NVIDIA Jetson Nano show that the architecture can predict optimal service providers for an edge node in real-time for 5 to 20 QoS (Quality of Service) and QoE parameters, with at least 50 potential fog nodes, and that overall QoE resulting from its use is improved by 1% to 18% over previous work such as SoSwirly, depending on the scenario.
Keywords
quality of experience, edge AI, edge intelligence, edge computing

Downloads

  • (...).pdf
    • full text (Published version)
    • |
    • UGent only
    • |
    • PDF
    • |
    • 810.28 KB

Citation

Please use this url to cite or link to this publication:

MLA
Goethals, Tom, et al. “A Geometric Approach to Real-Time Quality of Experience Prediction in Volatile Edge Networks.” 2022 18TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM 2022) : INTELLIGENT MANAGEMENT OF DISRUPTIVE NETWORK TECHNOLOGIES AND SERVICES, IEEE, 2022, pp. 170–76.
APA
Goethals, T., Volckaert, B., & De Turck, F. (2022). A geometric approach to real-time quality of experience prediction in volatile edge networks. 2022 18TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM 2022) : INTELLIGENT MANAGEMENT OF DISRUPTIVE NETWORK TECHNOLOGIES AND SERVICES, 170–176. IEEE.
Chicago author-date
Goethals, Tom, Bruno Volckaert, and Filip De Turck. 2022. “A Geometric Approach to Real-Time Quality of Experience Prediction in Volatile Edge Networks.” In 2022 18TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM 2022) : INTELLIGENT MANAGEMENT OF DISRUPTIVE NETWORK TECHNOLOGIES AND SERVICES, 170–76. IEEE.
Chicago author-date (all authors)
Goethals, Tom, Bruno Volckaert, and Filip De Turck. 2022. “A Geometric Approach to Real-Time Quality of Experience Prediction in Volatile Edge Networks.” In 2022 18TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM 2022) : INTELLIGENT MANAGEMENT OF DISRUPTIVE NETWORK TECHNOLOGIES AND SERVICES, 170–176. IEEE.
Vancouver
1.
Goethals T, Volckaert B, De Turck F. A geometric approach to real-time quality of experience prediction in volatile edge networks. In: 2022 18TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM 2022) : INTELLIGENT MANAGEMENT OF DISRUPTIVE NETWORK TECHNOLOGIES AND SERVICES. IEEE; 2022. p. 170–6.
IEEE
[1]
T. Goethals, B. Volckaert, and F. De Turck, “A geometric approach to real-time quality of experience prediction in volatile edge networks,” in 2022 18TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM 2022) : INTELLIGENT MANAGEMENT OF DISRUPTIVE NETWORK TECHNOLOGIES AND SERVICES, Thessaloniki, Greece, 2022, pp. 170–176.
@inproceedings{01GJYR5ES45BSP8PK8Q1VA2GJ2,
  abstract     = {{In recent years, the continuing growth of the network edge, along with increasing user demands, has led to the need for increasingly complex and responsive management strategies for edge services. Many of these strategies are cloudbased, offering near-perfect solutions at the cost of requiring massive computational power, or edge-based, offering reactive strategies to changing edge conditions. This paper presents a decentralized, pro-active Quality of Experience (QoE) based architecture designed to run on edge nodes, which allows nodes to predict optimal service providers (fog nodes) in advance and request their services. The concepts behind the components of the architecture are explained, as well as geometry-inspired design decisions to limit model size. Evaluations on an NVIDIA Jetson Nano show that the architecture can predict optimal service providers for an edge node in real-time for 5 to 20 QoS (Quality of Service) and QoE parameters, with at least 50 potential fog nodes, and that overall QoE resulting from its use is improved by 1% to 18% over previous work such as SoSwirly, depending on the scenario.}},
  author       = {{Goethals, Tom and Volckaert, Bruno and De Turck, Filip}},
  booktitle    = {{2022 18TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM 2022) : INTELLIGENT MANAGEMENT OF DISRUPTIVE NETWORK TECHNOLOGIES AND SERVICES}},
  isbn         = {{9783903176515}},
  issn         = {{2165-9605}},
  keywords     = {{quality of experience,edge AI,edge intelligence,edge computing}},
  language     = {{eng}},
  location     = {{Thessaloniki, Greece}},
  pages        = {{170--176}},
  publisher    = {{IEEE}},
  title        = {{A geometric approach to real-time quality of experience prediction in volatile edge networks}},
  year         = {{2022}},
}

Web of Science
Times cited: