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Energy- and Quality-Aware Task Offloading for WebVR Service in Terminal-Aided Mobile Edge Network.
- Source :
- IEEE Transactions on Vehicular Technology; Aug2022, Vol. 71 Issue 8, p8825-8838, 14p
- Publication Year :
- 2022
-
Abstract
- Web virtual reality (WebVR) is gaining increasing attention as interactive VR experiences become more prevalent. The high energy consumption and strict quality requirements are still significant challenges for its application in the mobile edge network. In this context, content caching and task offloading are promising solutions to save the energy of WebVR. This paper proposes a hybrid decentralized offloading architecture in which the MEC server and multiple terminals jointly participate in the caching and processing of WebVR tasks. In this framework, the graph convolutional network and unsupervised clustering algorithm are jointly applied to process the WebVR service feature graph to achieve FoV-level content caching. Moreover, WebVR users with idle computing resources assist neighboring users with performing tasks. On this basis, we propose a distributed low-bound-based alternative direction method of multiplier (LADMM) algorithm to optimize the offloading mode and the allocation of tasks and computing power resources to minimize system energy consumption. The proposed offloading mode can lower energy consumption while maintaining a good balance between delay performance and resource utilization. [ABSTRACT FROM AUTHOR]
- Subjects :
- POWER resources
GRAPH algorithms
ENERGY consumption
VIRTUAL reality
MOBILE apps
TASKS
Subjects
Details
- Language :
- English
- ISSN :
- 00189545
- Volume :
- 71
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Vehicular Technology
- Publication Type :
- Academic Journal
- Accession number :
- 158604175
- Full Text :
- https://doi.org/10.1109/TVT.2022.3173709