Supporting dynamic qos in linux

Liu, X.; Goddard, Steve

doi:10.1109/rttas.2004.1317270

Cited by 3 publications

(1 citation statement)

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“…Using a loadable scheduler approach is likely the most straightforward mechanism for the prototyping of a real-time scheduler. As an example authors in [29] utilized this approach to "pre-plant" several hooks (functions) in the original Linux scheduler. For our work, we use some of those same functions with other functions added to support multiprocessor real-time scheduling.…”

Section: Methodsmentioning

confidence: 99%

Towards QoS-Based Embedded Machine Learning

et al. 2022

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Due to various breakthroughs and advancements in machine learning and computer architectures, machine learning models are beginning to proliferate through embedded platforms. Some of these machine learning models cover a range of applications including computer vision, speech recognition, healthcare efficiency, industrial IoT, robotics and many more. However, there is a critical limitation in implementing ML algorithms efficiently on embedded platforms: the computational and memory expense of many machine learning models can make them unsuitable in resource-constrained environments. Therefore, to efficiently implement these memory-intensive and computationally expensive algorithms in an embedded computing environment, innovative resource management techniques are required at the hardware, software and system levels. To this end, we present a novel quality-of-service based resource allocation scheme that uses feedback control to adjust compute resources dynamically to cope with the varying and unpredictable workloads of ML applications while still maintaining an acceptable level of service to the user. To evaluate the feasibility of our approach we implemented a feedback control scheduling simulator that was used to analyze our framework under various simulated workloads. We also implemented our framework as a Linux kernel module running on a virtual machine as well as a Raspberry Pi 4 single board computer. Results illustrate that our approach was able to maintain a sufficient level of service without overloading the processor as well as providing an energy savings of almost 20% as compared to the native resource management in Linux.

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Section: Methodsmentioning

confidence: 99%