| Autor: |
Gilly, Katja, Filiposka, Sonja, Alcaraz, Salvador, Ruiz, Javier Alonso, Ploeg, Jeroen, Lauer, Martin, Llamazaers, Angel, Parra, Noelia Hernández |
| Předmět: |
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| Zdroj: |
Applied Sciences (2076-3417); Feb2021, Vol. 11 Issue 3, p944, 16p |
| Abstrakt: |
Featured Application: Employing proactive multi-access edge computing (MEC) service migration techniques helps provide offloaded computing power to highly dynamic vehicular networks while maintaining continuously low latency. This enables vehicles to take advantage of extra processing and storage, which can be used for employing deep learning algorithms for autonomous driving. Advanced learning algorithms for autonomous driving require lots of processing and storage power, which puts a strain on vehicles' computing resources. Using a combination of 5G network connectivity with ultra-high bandwidth and low latency together with extra computing power located at the edge of the network can help extend the capabilities of vehicular networks. However, due to the high mobility, it is essential that the offloaded services are migrated so that they are always in close proximity to the requester. Using proactive migration techniques ensures minimum latency for high service quality. However, predicting the next edge server to migrate comes with an error that can have deteriorating effects on the latency. In this paper, we examine the influence of mobility prediction errors on edge service migration performances in terms of latency penalty using a large-scale urban vehicular simulation. Our results show that the average service delay increases almost linearly with the migration prediction error, with 20% error yielding almost double service latency. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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