Abstract
Current high-end, data-intensive real-time embedded sensor applications (e.g., radar, optronics) require very specific computing platforms. The nature of such applications and the environment in which they are deployed impose numerous constraints, including real-time constraints, and computing throughput and latency needs. Static application placement is traditionally used to deal with these constraints. However, this approach fails to provide adaptation capabilities in an environment in constant evolution. Through the study of an industrial radar use-case, our work aims at mitigating the aforementioned limitations by proposing a low-latency online resource manager derived from techniques used in large-scale systems, such as cloud and grid environments. The resource manager introduced in this paper is able to dynamically allocate resources to fulfill requests coming from several sensors, making the most of the computing platform while providing guaranties on non-functional properties and Quality of Service (QoS) levels. Thanks to the load prediction implemented in the manager, we are able to achieve a 83% load increase before overloading the platform while managing to reduce ten times the incurred QoS penalty. Further methods to reduce the impact of the overload are as well as possible future improvements are proposed and discussed.
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This work was made possible thanks to the support of the Surface Radar Business Line of Thales.
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Goupille-Lescar, B., Lenormand, E., Parlavantzas, N., Morin, C. (2018). Communication-Aware Prediction-Based Online Scheduling in High-Performance Real-Time Embedded Systems. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11336. Springer, Cham. https://doi.org/10.1007/978-3-030-05057-3_43
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