Efficient Pareto Optimality-based Task Scheduling for Vehicular Edge Computing
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen › Begutachtung
Beitragende
Abstract
Vehicular Edge Computing is a promising paradigm that provides cloud computing services closer to vehicular users. Vehicles and communication infrastructure can cooperatively provide vehicular services with low latency constraints through vehicular cloud formation and use of these computational re- sources via task scheduling. An efficient task scheduler needs to decide which cloud will run the tasks, considering vehicular mobility and task requirements. This is important to minimize processing time and, consequently, monetary cost. However, the literature solutions do not consider these contextual aspects together, degrading the overall system efficiency. This work presents EFESTO, a task scheduling mechanism that considers contextual aspects in its decision process. The results show that, compared to state-of-the-art solutions, EFESTO can schedule more tasks while minimizing monetary cost and system latency.
Details
Originalsprache | Englisch |
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Titel | 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall) |
Erscheinungsort | London, United Kingdom |
Herausgeber (Verlag) | IEEE |
Seitenumfang | 6 |
ISBN (elektronisch) | 978-1-66545-468-1 |
ISBN (Print) | 978-1-6654-5469-8 |
Publikationsstatus | Veröffentlicht - Sept. 2022 |
Peer-Review-Status | Ja |
Publikationsreihe
Reihe | IEEE Conference on Vehicular Technology (VTC) |
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ISSN | 1090-3038 |
Externe IDs
Scopus | 85147015985 |
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Bibtex | nsm-dacosta2022efficient |