DEEP: Edge-based Dataflow Processing with Hybrid Docker Hub and Regional Registries
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917982834262016 |
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| author | Mehran, Narges Samani, Zahra Najafabadi Farahani, Reza Hammer, Josef Kimovski, Dragi |
| author_facet | Mehran, Narges Samani, Zahra Najafabadi Farahani, Reza Hammer, Josef Kimovski, Dragi |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_08741 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DEEP: Edge-based Dataflow Processing with Hybrid Docker Hub and Regional Registries Mehran, Narges Samani, Zahra Najafabadi Farahani, Reza Hammer, Josef Kimovski, Dragi Distributed, Parallel, and Cluster Computing 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. |
| title | DEEP: Edge-based Dataflow Processing with Hybrid Docker Hub and Regional Registries |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2504.08741 |