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Modern manufacturing increasingly depends on distributed, software-based systems
in which computer vision and inference workloads must operate under strict latency,
availability and resource constraints. In such environments, end-to-end responsiveness
is determined not only by model inference, but also by the supporting messaging, storage
and orchestration layers, whose design becomes a cross-cutting concern for both
system architects and Development and Operations (DevOps) practitioners.
This work was carried out during an internship at Twevo S.A., a Portuguese company
that develops AI-powered edge computer vision solutions for the manufacturing
sector, and is framed within the evolution of the company’s VIEXPAND AI platform.
The objective was to study, design a distributed edge architecture capable of keeping
end-to-end latency low while improving availability and lowering the cost of developing
new components, while leveraging industry-standard DevOps workflows to ensure
long-term maintainability.
Four workstreams were implemented. A lightweight storage subsystem based on
SQLite was designed to replace a legacy text log based persistence mechanism, with
tuned journalling modes and a refactored worker-thread concurrency model. A containerisation
strategy for the graphical components of the platform was developed, combining
Docker images with a remote-desktop-based display layer to enable reproducible,
isolated and securely exposed deployments. A Kafka-based messaging backbone was
introduced to replace a point-to-point Transmission Control Protocol (TCP) messaging
system, including topic and partition layouts, producer and consumer tuning for low
latency, and a thin librdkafka wrapper to support deterministic benchmarking and
integration. Finally, a deterministic perception-to-action controller was implemented
to translate vendor-agnostic orders into machine-executable instructions.
The artefacts were assessed through targeted validation activities, including database
latency and resource measurements, Kafka broker and integration tests, a container
display-latency comparison, and actuator functional and latency tests. The remaining
contribution consists of a set of integrated, production-oriented components and
a body of design principles and best practices for deploying low-latency, resourceefficient
edge inference systems in manufacturing environments.
Descrição
Palavras-chave
Edge Computing Low Latency Stream Processing Real-Time Data Processing Kafka DevOps Computer Vision