Serverless Computing Approach for Deploying Machine Learning Applications in Edge Layer

Bac, Ta Phuong; Tran, Martin; Kim, Young-Han

doi:10.1109/icoin53446.2022.9687209

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…The Federated Learning-based architecture was proposed in the primary dataset [47], [56], [66]. This computing model supports edge computing, where the processing edges can learn from a shared machine learning model while keeping the model training on remote clients, followed by global aggregation of the updated model parameters.…”

Section: ) Security and Privacymentioning

confidence: 99%

“…Rausch et al [35] chose to transmit the base model to an edge device to refine the base model locally using a serverless function with the private data to ensure data privacy. The edge computing paradigm allows training distributed machine learning models between local edge data to secure data privacy and save resources in the cloud [66]. Bac et al [66] applied a federated learning approach on serverless edge computing, where they saved bandwidth and ensured data privacy of edge nodes.…”

Section: ) Security and Privacymentioning

confidence: 99%

“…The edge computing paradigm allows training distributed machine learning models between local edge data to secure data privacy and save resources in the cloud [66]. Bac et al [66] applied a federated learning approach on serverless edge computing, where they saved bandwidth and ensured data privacy of edge nodes.…”

Section: ) Security and Privacymentioning

confidence: 99%

“…The serverless edge computing platform that provides appropriate support to define AI workflow functions has been extended to work at the edge of the network to reduce response latency, and bandwidth associated with the public cloud [35], [54], [66].…”

Section: ) Edge Computingmentioning

confidence: 99%

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Serverless on Machine Learning: A Systematic Mapping Study

2022

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Machine Learning Operations (MLOps) is an approach to managing the entire lifecycle of a machine learning model. It has evolved over the last years and has started attracting many people in research and businesses in the industry. It supports the development of machine learning (ML) pipelines typical in the phases of data collection, data pre-processing, building datasets, model training, hyperparameters refinement, testing, and deployment to production. This complex pipeline workflow is a tedious process of iterative experimentation. Moreover, cloud computing services provide advanced features for managing ML stages and deploying them efficiently to production. Specifically, serverless computing has been applied in different stages of the machine learning pipeline. However, to the best of our knowledge, it is missing to know the serverless suitability and benefits it can provide to the ML pipeline. In this paper, we provide a systematic mapping study of machine learning systems applied on serverless architecture that include 53 relevant studies. During this study, we focused on (1) exploring the evolution trend and the main venues; (2) determining the researchers' focus and interest in using serverless on machine learning;(3) discussing solutions that serverless computing provides to machine learning. Our results show that serverless usage is growing, and several venues are interested in the topic. In addition, we found that the most widely used serverless provider is AWS Lambda, where the primary application was used in the deployment of the ML model. Additionally, several challenges were explored, such as reducing cost, resource scalability, and reducing latency. We moreover discuss the potential challenges of adopting ML on serverless, such as respecting service level agreement, the cold start problem, security, and privacy. Finally, our contribution provides foundations for future research and applications implying machine learning in serverless computing.

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Section: ) Security and Privacymentioning

confidence: 99%

Section: ) Security and Privacymentioning

confidence: 99%

Section: ) Security and Privacymentioning

confidence: 99%

Section: ) Edge Computingmentioning

confidence: 99%

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Serverless on Machine Learning: A Systematic Mapping Study

2022

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“…In addition, the requirements of big data processing, such as the training of machine learning models [19][20][21], stream processing [22], and event processing [23,24], raise scalability issues and require significant computing and storage resources [25,26]. Fortunately, big data management and processing platforms such as Apache Spark [27], Apache Flink [28], and Apache OpenWhisk [29] address these concerns by facilitating distribution and collaboration.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Data Processing: A Comparative Study of Big Data Platforms in Edge, Fog, and Cloud Layers

Shwe,

Aritsugi

2024

Applied Sciences

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Intelligent applications in several areas increasingly rely on big data solutions to improve their efficiency, but the processing and management of big data incur high costs. Although cloud-computing-based big data management and processing offer a promising solution to provide scalable and abundant resources, the current cloud-based big data management platforms do not properly address the high latency, privacy, and bandwidth consumption challenges that arise when sending large volumes of user data to the cloud. Computing in the edge and fog layers is quickly emerging as an extension of cloud computing used to reduce latency and bandwidth consumption, resulting in some of the processing tasks being performed in edge/fog-layer devices. Although these devices are resource-constrained, recent increases in resource capacity provide the potential for collaborative big data processing. We investigated the deployment of data processing platforms based on three different computing paradigms, namely batch processing, stream processing, and function processing, by aggregating the processing power from a diverse set of nodes in the local area. Herein, we demonstrate the efficacy and viability of edge-/fog-layer big data processing across a variety of real-world applications and in comparison to the cloud-native approach in terms of performance.

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