ODRE Workshop: Probabilistic Dynamic Hard Real-Time Scheduling in HPC

Hofer, Florian; Sehr, Martin A.; Russo, Barbara; Sangiovanni‐Vincentelli, Alberto

doi:10.1109/isorc49007.2020.00043

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…High-performance computing (HPC) makes wide use of containers. A framework has been proposed to efficiently schedule real-time containers in many-CPU systems [38] but additional research is needed to determine the effects of the operating system and I/O on deadlines. The feasibility of migrating real-time applications from bare-metal servers to virtualized IaaS configurations for Industry 4.0 has also been explored [17].…”

Section: Related Workmentioning

confidence: 99%

Enabling Kubernetes Orchestration of Mixed-Criticality Software for Autonomous Mobile Robots

Lumpp,

Fummi,

Patel

et al. 2024

IEEE Trans. Robot.

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Containerization and orchestration have become two key requirements in software development best practices. Containerization allows for better resource utilization, platform-independent development, and secure deployment of software. Orchestration automates the deployment, networking, scaling, and availability of containerized workloads and services. While containerization is increasingly being adopted in the robotic community, the use of task orchestration platforms (e.g., Kubernetes) is still an open challenge. The biggest limitation is due to the fact that state-of-the-art orchestrators do not support real-time containers, while advanced robotic software often consists of a mix of heterogeneous tasks (i.e., ROS nodes) with different levels of temporal constraints (i.e., mixed-criticality systems). This work addresses this challenge by presenting RT-Kube, a platform that extends the de-facto reference standard for container orchestration, Kubernetes, to schedule tasks with mixed-criticality requirements. It implements monitoring of tasks and detects missed deadlines for those with real-time constraints. It selects low-priority tasks to be migrated at runtime to different units of the computing cluster to free resources and recover from temporal violations. We present quantitative experimental results on the software implementing the mission of a Robotnik RB-Kairos mobile robot to demonstrate the effectiveness of the proposed approach. The source code is publicly available on GitHub.

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Section: Related Workmentioning

confidence: 99%

Enabling Kubernetes Orchestration of Mixed-Criticality Software for Autonomous Mobile Robots

Lumpp,

Fummi,

Patel

et al. 2024

IEEE Trans. Robot.

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“…Our architecture aims at contributing to the current debate within the systems engineering research community on strategies for designing flexible systems' architecture 24 where flexibility is defined as how easily a system's capabilities can be modified in response to external change. 25 In this respect, our work proposes a flexible architecture for control systems that exploit virtualization and containerization capabilities over the cloud, designs it as a leveled model for the real-time system in the cloud, 16,26 and finally, is deployed exploiting off-the-shelf technologies. The way we decoupled control from the machine and move it to the cloud makes the overall system highly modular as it combines the decoupling mechanism of the leveled model for real-time systems of Hallman et al 16 and the containerization decomposition in a virtualized cloud system.…”

Section: Real-time Smart System Architecturementioning

confidence: 99%

Industrial control via application containers: Maintaining determinism in IAAS

Hofer

Sehr

Sangiovanni-Vincentelli

et al. 2021

Systems Engineering

Self Cite

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Industry 4.0 is changing data collection, storage, and analysis in industrial processes fundamentally, enabling novel applications such as flexible manufacturing of highly customized products. However, real-time control of these processes has not yet realized its full potential in using the collected data to drive further development. Indeed, typical industrial control systems are tailored to the plant they need to control, reusing, and adapting to challenge. In the past, the need to solve plant-specific problems overshadowed the benefits of physically isolating a control system from its plant. We believe that modern virtualization techniques, specifically application containers, present a unique opportunity to decouple control from plants. This separation permits us to fully realize the potential for highly distributed and transferable industrial processes even with real-time constraints arising from time-critical subprocesses. This paper explores the challenges and opportunities of shifting industrial control software from dedicated hardware to bare-metal servers or (edge) cloud computing platforms using off-the-shelf technology, that is, technologies commercially available. We present a migration architecture and show, using a specifically developed orchestration tool, that containerized applications can run on shared resources without compromising scheduled execution within given time constraints. Through latency and computational performance experiments, we explore three system setups' limits and summarize lessons learned. K E Y W O R D S container orchestration, determinism, IAAS, industrial control systems, real-time 1 INTRODUCTION Emerging technologies such as the Internet of Things and Cloud Computing offer the chance to innovate structure and control of industrial processes. These new technologies allow the creation of flexible production systems, for which control is performed through distributed sensing, big-data analysis, and cloud storage. Such systems may also take advantage of new computing paradigms like the Edge Networking paradigm or the Fog Computing, which brings data stor-This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…In dynamic resource management, instead of allocating resources based on worst case parameters, it uses probabilities to asses situation [27]. The orchestrator considers typical run times, contemporaneity factors and probabilities of occurrence of the WCET.…”

Section: The Orchestratormentioning

confidence: 99%