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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.00621 |
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| _version_ | 1866911974707691520 |
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| author | Cao, Jiahe Liu, Qiang Chen, Dawei Han, Kyungtae |
| author_facet | Cao, Jiahe Liu, Qiang Chen, Dawei Han, Kyungtae |
| contents | In-vehicle edge computing is a much anticipated paradigm to serve ever-increasing computation demands originated from the ego vehicle, such as passenger entertainments. In this paper, we explore the unique idea of crowdsourcing passing-by vehicles to augment computing of the ego vehicle. The challenges lie in the high dynamics of passing-by vehicles, time-correlated task computation, and the stringent requirement of computing reliability for individual user tasks. To this end, we formulate an optimization problem to minimize the end-to-end latency by optimizing the task assignment and resource allocation of user tasks. To address the complex problem, we propose a new algorithm (named CAVE) with multiple key designs. We build an end-to-end network and compute simulator and conduct extensive simulation to evaluate the performance of the proposed algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00621 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CAVE: Crowdsourcing Passing-By Vehicles for Reliable In-Vehicle Edge Computing Cao, Jiahe Liu, Qiang Chen, Dawei Han, Kyungtae Networking and Internet Architecture In-vehicle edge computing is a much anticipated paradigm to serve ever-increasing computation demands originated from the ego vehicle, such as passenger entertainments. In this paper, we explore the unique idea of crowdsourcing passing-by vehicles to augment computing of the ego vehicle. The challenges lie in the high dynamics of passing-by vehicles, time-correlated task computation, and the stringent requirement of computing reliability for individual user tasks. To this end, we formulate an optimization problem to minimize the end-to-end latency by optimizing the task assignment and resource allocation of user tasks. To address the complex problem, we propose a new algorithm (named CAVE) with multiple key designs. We build an end-to-end network and compute simulator and conduct extensive simulation to evaluate the performance of the proposed algorithm. |
| title | CAVE: Crowdsourcing Passing-By Vehicles for Reliable In-Vehicle Edge Computing |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2408.00621 |