Computation task assignment in vehicular fog computing: a learning approach via neighbor advice
Visualitza/Obre
NCA_CRV.pdf (412,0Kb) (Accés restringit)
Sol·licita una còpia a l'autor
Què és aquest botó?
Aquest botó permet demanar una còpia d'un document restringit a l'autor. Es mostra quan:
- Disposem del correu electrònic de l'autor
- El document té una mida inferior a 20 Mb
- Es tracta d'un document d'accés restringit per decisió de l'autor o d'un document d'accés restringit per política de l'editorial
Cita com:
hdl:2117/177565
Tipus de documentText en actes de congrés
Data publicació2019
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés restringit per política de l'editorial
Tots els drets reservats. Aquesta obra està protegida pels drets de propietat intel·lectual i
industrial corresponents. Sense perjudici de les exempcions legals existents, queda prohibida la seva
reproducció, distribució, comunicació pública o transformació sense l'autorització del titular dels drets
ProjecteGESTION DE UNA ARQUITECTURA CLOUD JERARQUICA PARA ESCENARIOS IOT: FOGGING CLOUD (MINECO-TEC2015-66220-R)
mF2C - Towards an Open, Secure, Decentralized and Coordinated Fog-to-Cloud Management Ecosystem (EC-H2020-730929)
mF2C - Towards an Open, Secure, Decentralized and Coordinated Fog-to-Cloud Management Ecosystem (EC-H2020-730929)
Abstract
In the recent years, fog computing has been proposed as a promising paradigm to enhance the performance of latency-critical applications by hosting them within idle computation nodes (Fog Nodes, FNs) at the edge of the network. Such a paradigm has been further extended to the vehicular fog computing (VFC) scenario by leveraging available computing resources offered by modern vehicles, thus allowing them to process different types of computation tasks on behalf of onboard users or nearby vehicles. Within this context, the proper assignment of computation tasks to Vehicular FNs is an important issue that is currently under active research. To address this issue, online learning approaches, where the performances of the Vehicular FNs in terms of task execution delay are learnt via trial and error, are starting to gain in popularity. This is mainly motivated by the inherent uncertainty caused by mobility and fluctuating resource availabilities in VFC environments. However, since the process of learning from scratch in such a dynamic vehicular environment may lead to a degradation in the learning performance, this paper proposes the use of an advising mechanism, where a roadside unit (RSU) who has already learnt the performances of the vehicles within its range, uses its acquired knowledge to provide advice to a neighbor RSU who does not have enough experience allowing it to make efficient assignment decisions on its own. To evaluate this approach, we used realistic vehicular mobility traces to simulate the VFC scenario. The obtained results show that our proposed approach improves the learning performance compared to the case where no advice is leveraged.
CitacióRejiba, Z.; Masip, X.; Marin, E. Computation task assignment in vehicular fog computing: a learning approach via neighbor advice. A: IEEE International Symposium on Network Computing and Applications. "2019 IEEE 18th International Symposium on Network Computing and Applications (NCA): September 26-28, 2019". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 1-5.
ISBN978-1-7281-2523-7
Versió de l'editorhttps://ieeexplore.ieee.org/document/8935033
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
NCA_CRV.pdf | 412,0Kb | Accés restringit |