A. Ahmadi, O. Semiari, M. Bennis and M. Debbah, "Variational Autoencoders for Reliability Optimization in Multi-Access Edge Computing Networks," 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, TX, USA, 2022, pp. 752-757, doi: 10.1109/WCNC51071.2022.9771710
Variational autoencoders for reliability optimization in multi-access edge computing networks
|Author:||Ahmadi, Arian1; Semiari, Omid1; Bennis, Mehdi2;|
1Department of Electrical and Computer Engineering, University of Colorado, Colorado Springs, CO, USA
2Department of Communications Engineering, University of Oulu, Oulu, Finland
3Technology Innovation Institute, Mohamed Bin Zayed University of Artificial Intelligence, Masdar City, Abu Dhabi, United Arab Emirates
|Online Access:||PDF Full Text (PDF, 1.4 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023021026765
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-02-10
Multi-access edge computing (MEC) is viewed as an integral part of future wireless networks to support new applications with stringent service reliability and latency requirements. However, guaranteeing ultra-reliable and low-latency MEC (URLL MEC) is very challenging due to uncertainties of wireless links, limited communications and computing resources, as well as dynamic network traffic. Enabling URLL MEC man-dates taking into account the statistics of the end-to-end (E2E) latency and reliability across the wireless and edge computing systems. In this paper, a novel framework is proposed to optimize the reliability of MEC networks by considering the distribution of E2E service delay, encompassing over-the-air transmission and edge computing latency. The proposed framework builds on correlated variational autoencoders (VAEs) to estimate the full distribution of the E2E service delay. Using this result, a new optimization problem based on risk theory is formulated to maximize the network reliability by minimizing the Conditional Value at Risk (CVaR) as a risk measure of the E2E service delay. To solve this problem, a new algorithm is developed to efficiently allocate users’ processing tasks to edge computing servers across the MEC network, while considering the statistics of the E2E service delay learned by VAEs. The simulation results show that the proposed scheme outperforms several baselines that do not account for the risk analyses or statistics of the E2E service delay.
IEEE wireless communications letters
|Pages:||752 - 757|
2022 IEEE Wireless Communications and Networking Conference (WCNC)
IEEE Wireless Communications and Networking Conference
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
This research was supported by the U.S. National Science Foundation under Grants CNS 1941348 and CNS 2008646.
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