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Cited by 23IoT and Edge/Fog ComputingCloud Computing and Resource ManagementInternet of Things and AI

Containerized Architecture for Edge Computing in Smart Home : A consistent architecture for model deployment

Gupta, Nitu, Anantharaj, Katpagavalli, Subramani, Karthikeyan·2020·2020 International Conference on Computer Communication and Informatics (ICCCI)
DOI 10.1109/iccci48352.2020.9104073
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Abstract

Network bandwidth and high latency are the main bottlenecks of cloud computing. To combat such scenarios, new paradigm Edge Computing is used. Edge computing shifts the computation of resources from centralized cloud closer to the devices which generates data. Edge Computing reduces the response time, latency and improves the battery life while maintaining data safety and privacy. The distributed architecture of edge computing makes resource management an important aspect of edge computing. In such a resource constrained environment edge devices should be capable of processing all types of request coming from IoT devices. With the advancement in Data science and Machine learning models in different domains, many intelligent services have emerged to provide better user experience. These deep learning models have frequent updates to adapt to new hardware and software requirements. These models also have some installation dependencies and requirement of cross platform compatibility for training and prediction. In home edge environment, these issues are more important due to resource constraint in terms of memory and computing power. Also, due to the availability of extensive list of deep learning models for different service, it is difficult to maintain an environment supporting such models across various devices in the smart home to support all services. To solve this issue in smart home environment, this paper proposes architecture, using containerization techniques to deploy and manage the deep learning models. This paper also explains about the steps to convert the existing model into containers. Minimal space requirement on the edge device, data privacy, low latency along with device independence for deep learning models are prime benefits of the architecture proposed. To test the performance of the architecture, deep learning model was containerized and compared with the actual model deployed in the same edge environment. The experimental results demonstrated the performance is almost similar of the containerized architecture in terms of model execution time and CPU load vs execution time. But it comes with ease of model deployment and cross platform model execution.

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Crossref · 10.1109/iccci48352.2020.9104073

Cites

25 of 25 references verified0 citation errors · 0 not checkable

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Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Serving-related challenges are some of the ones most often reported in the literature, usually from a performance perspective.

    “Serving-related challenges are some of the ones most often reported in the literature [5-7, 9, 11, 12, 16, 20, 28, 34, 44, 53, 54], usually from a performance perspective.”

    From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [20]

  • Edge deployments are often reported as being more challenging than cloud deployments because of hardware resource heterogeneity, the typically distributed nature of edge devices, and possibly varying network connection quality.

    “Edge deployments are often reported as being more challenging than cloud deployments because of hardware resource heterogeneity, the typically distributed nature of edge devices, and possibly varying network connection quality [7,10,20,22,39,53].”

    From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [20]

  • Deploying ML models to the edge can reduce network latency.

    “[20] suggested deploying ML models to the edge in order to reduce network latency.”

    From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [20]

  • Models packaged in containers are typically accessed through either a REST API or an RPC interface.

    “Models packaged in containers are typically accessed through either a REST API [16,40], such as Flask 18 [20], or RPC interface [12,45], such as Apache Thrift 19 [28].”

    From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [20]

  • Docker is by far the most frequently reported solution for packaging ML models in containers.

    “Docker 17 is by far the most frequently reported solution [12,16,20,26,28,40,44].”

    From Operationalizing machine learning models: a systematic literature review (2022) · cites this paper as [20]

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Authors
Gupta, N., Anantharaj, K., & Subramani, K.
Affiliations
Things Platform Samsung Research Institute,Bangalore
Venue
2020 International Conference on Computer Communication and Informatics (ICCCI)
Publisher
IEEE
Published
2020-01
Pages
1-8
DOI
10.1109/iccci48352.2020.9104073
OpenAlex
W3033321778
Cited by
23
References
25
Citations by year
2025: 3 · 2024: 2 · 2023: 6 · 2022: 3 · 2021: 8 · 2020: 1
FWCI
3.77
Type
conference paper
Language
en
Open access
closed
Licence
Locations
International Conference on Computer Communication and Informatics (ICCCI) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
  • Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
Keywords
edge computing, home automation, containerization, deep learning model deployment, IoT devices, resource-constrained edge devices, cross-platform compatibility, model portability, latency reduction, data privacy

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