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arXiv · 2504.08741

DEEP: Edge-based Dataflow Processing with Hybrid Docker Hub and Regional Registries

Narges Mehran·Zahra Najafabadi Samani·Reza Farahani·Josef Hammer·Dragi Kimovski
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Abstract

Reducing energy consumption is essential to lessen greenhouse gas emissions, conserve natural resources, and help mitigate the impacts of climate change. In this direction, edge computing, a complementary technology to cloud computing, extends computational capabilities closer to the data producers, enabling energy-efficient and latency-sensitive service delivery for end users. To properly manage data and microservice storage, expanding the Docker Hub registry to the edge using an AWS S3-compatible MinIO-based object storage service can reduce completion time and energy consumption. To address this, we introduce Docker rEgistry-based Edge dataflow Processing (DEEP) to optimize the energy consumption of microservice-based application deployments by focusing on deployments from Docker Hub and MinIO-based regional registries and their processing on edge devices. After applying nash equilibrium and benchmarking the execution of two compute-intensive machine learning (ML) applications of video and text processing, we compare energy consumption across three deployment scenarios: exclusively from Docker Hub, exclusively from the regional registry, and a hybrid method utilizing both. Experimental results show that deploying 83% of text processing microservices from the regional registry improves the energy consumption by 0.34% (18J) compared to microservice deployments exclusively from Docker Hub.

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Narges Mehran, Zahra Najafabadi Samani, Reza Farahani, Josef Hammer, Dragi Kimovski. 2025-03-23. DEEP: Edge-based Dataflow Processing with Hybrid Docker Hub and Regional Registries. https://arxiv.org/abs/2504.08741

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