Liu, X., Derakhshani, M., Mihaylova, L. orcid.org/0000-0001-5856-2223 et al. (1 more author) (2023) Risk-aware contextual learning for edge-assisted crowdsourced live streaming. IEEE Journal on Selected Areas in Communications, 41 (3). pp. 740-754. ISSN 0733-8716
Abstract
This paper proposes an edge-assisted crowdsourced live video transcoding approach where the transcoding capabilities of the edge transcoders are unknown and dynamic. The resilience and trustworthiness of highly unstable transcoders in decision making are characterized with mean-variance-based measures to avoid making highly risky decisions. The risk level of each device’s situation is assessed and two upper confidence bounds of the variance of transcoding performance are presented. Based on the derived bounds and by leveraging the contextual information of devices, two risk-aware contextual learning schemes are developed to efficiently estimate the transcoding capabilities of the edge devices. Combining context awareness and risk sensitivity, a novel transcoding task assignment and viewer association algorithm is proposed. Simulation results demonstrate that the proposed algorithm achieves robust task offloading with superior network utility performance as compared to the linear upper confidence bound and the risk-aware mean-variance upper confidence bound-based algorithms. In particular, an epoch-based task assignment strategy is designed to reduce the task switching costs incurred in assigning the same transcoding task to different transcoders over time. This strategy also reduces the computational time needed. Numerical results confirm that this strategy achieves up to 86.8% switching costs reduction and 92.3% computational time reduction.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. This is an author-produced version of a paper subsequently published in IEEE Journal on Selected Areas in Communications. This version is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0). |
Keywords: | Reinforcement learning; edge computing; task offloading; risk-awareness; contextual learning |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/T013265/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Nov 2022 12:41 |
Last Modified: | 16 Feb 2023 11:09 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/JSAC.2022.3229423 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:192731 |