Kubernetes Edge/Cloud Continuum Task Offloading Framework for Vehicular Computing
Mahmoodi, Alireza Bakhshi Zadi; Peltonen, Ella
CEUR-WS.org
Mahmoodi, A. B. Z., & Peltonen, E. (2024). Kubernetes edge/cloud continuum task offloading framework for vehicular computing. In J. Kasurinen, T. Päivärinta, & T. Vartiainen (Eds.), Proceedings of the Annual Doctoral Symposium of Computer Science 2024. CEUR workshop proceedings, 3776, 143-150.
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© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
https://creativecommons.org/licenses/by/4.0/
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202411016562
https://urn.fi/URN:NBN:fi:oulu-202411016562
Tiivistelmä
Abstract
Cars have significantly been transformed to the point of autonomously driving in complex situations by sensing their surroundings and inferring insights based on sensor inputs. Even though smart cars can process the vast majority of data coming from various in-vehicle-installed sensors such as radars, LiDAR, cameras, and so on, the amount of data processing required is ever-growing, along with the demand for more real-time services for novel driving applications. In addition, sustainability and battery-longevity perspectives appreciate the computation of the vehicle-sensor data to be offloaded to the edge-cloud continuum. This article introduces a Kubernetes-based framework that can be utilized on cloud/edge servers to facilitate various tasks and computation offloading for smart vehicles. The work is ongoing, and we present the preliminary results about the framework’s validity by employing an object recognition task on both edge and cloud computing servers to showcase the proposed architecture’s feasibility. An incidental finding regarding latency is also presented in the experiment. Moreover, we discuss development challenges related to implementing an edge-cloud continuum on vehicular computing.
Cars have significantly been transformed to the point of autonomously driving in complex situations by sensing their surroundings and inferring insights based on sensor inputs. Even though smart cars can process the vast majority of data coming from various in-vehicle-installed sensors such as radars, LiDAR, cameras, and so on, the amount of data processing required is ever-growing, along with the demand for more real-time services for novel driving applications. In addition, sustainability and battery-longevity perspectives appreciate the computation of the vehicle-sensor data to be offloaded to the edge-cloud continuum. This article introduces a Kubernetes-based framework that can be utilized on cloud/edge servers to facilitate various tasks and computation offloading for smart vehicles. The work is ongoing, and we present the preliminary results about the framework’s validity by employing an object recognition task on both edge and cloud computing servers to showcase the proposed architecture’s feasibility. An incidental finding regarding latency is also presented in the experiment. Moreover, we discuss development challenges related to implementing an edge-cloud continuum on vehicular computing.
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