Cited By
View all- Aminiyeganeh KCoutinho RBoukerche A(2024)IoT video analytics for surveillance-based systems in smart citiesComputer Communications10.1016/j.comcom.2024.05.021224:C(95-105)Online publication date: 19-Sep-2024
Offloading Augmented Reality Tasks with Smart Energy Source-Aware Algorithms at the Edge
Pages 73 - 82
Abstract
The development of novel use cases in beyond-5G and 6G networks will rely, among other aspects, on the availability of computing resources at the edge, therefore enabling the realization of applications that are both computationally demanding and latency constrained, such as Mobile Augmented Reality (MAR). Indeed, due to end devices' intrinsic constraints on computation capabilities and battery, newer MAR applications require offloading their most demanding tasks. However, the constrained nature of edge resources implies that these tasks should be carefully allocated at the edge network in order to guarantee satisfactory Quality of Experience to end-users. In this context, we analyze the edge operator's resource allocation to support the energy-aware offloading of MAR tasks at the edge of the cellular network with the goal of not only maximizing service acceptance (i.e., revenue), but also optimizing the operator's business utility, which depends on its carbon footprint and the profit of operating the service. We leverage Deep Reinforcement Learning to propose an efficient model to operate the edge resource allocation that can adapt to different utilities.
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Index Terms
- Offloading Augmented Reality Tasks with Smart Energy Source-Aware Algorithms at the Edge
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Published In
October 2023
330 pages
ISBN:9798400703669
DOI:10.1145/3616388
- General Chair:
- Azzedine Boukerche,
- Program Chairs:
- Floriano De Rango,
- Zhi Sun
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Published: 30 October 2023
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View all- Aminiyeganeh KCoutinho RBoukerche A(2024)IoT video analytics for surveillance-based systems in smart citiesComputer Communications10.1016/j.comcom.2024.05.021224:C(95-105)Online publication date: 19-Sep-2024
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