Software Optimization and Orchestration for Heterogeneous and Distributed Architectures

Francesco Lumpp
Department of Computer Science, University of Verona
Department of Computer Science, 2024


   title={Software Optimization and Orchestration for Heterogeneous and Distributed Architectures},

   author={Lumpp, Francesco and others},



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In the context of the Edge-Cloud computing continuum, containerization and orchestration have become two key requirements in software development best practices. Containerization allows for better resource utilization, platform-independent development, and secure software deployment. Orchestration automates the deployment, networking, scaling, and availability of containerized workloads and services. However, there are still several open challenges. First, the optimization of software tailored for edge computing, with the aim of enhancing software portability in real-time distributed environments and containerized applications within the realms of robotics and Industry 4.0/5.0 technologies. Second, the orchestration of real-time containers within mixed-criticality systems. Third, the expansion of the Edge-Cloud computing continuum through innovative runtime scheduling techniques geared towards enhancing throughput and reducing response times. This thesis tackles these challenges with a software methodology that targets various aspects of the Edge-Cloud computing continuum. The aforementioned objectives are divided into five categories that are subsequently analyzed, expanded, and optimized with techniques that allow for improved performance, safety, and reliability. It also addresses the growing demand for faster data processing and more responsive computing in the contemporary technological landscapes of industrial automation. The methodology is analyzed on a multitude of synthetic benchmarks and scenarios, but is also always verified through real-case studies, such as the software implementing the mission of a Robotnik RB-Kairos mobile robot interacting with an industrial agile production chain. The experimental results demonstrate that these objectives were achieved. First, the methodology allows for better performance on heterogeneous embedded edge devices that make use of unified memory architectures in vision-based applications. In addition, the introduction of containers into industrial automation and robotic contexts facilitates flexible software development. Furthermore, mixed-criticality environments benefit from the introduction of orchestration and the real-time plugin that allows for runtime monitoring of software. Finally, the verification and migration of assertions guarantee the reliability and safety of modern robots.
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