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Title	Serverless Data Pipelines For Iot Data Analytics: A Cloud Vendors Perspective And Solutions
ID_Doc	48501
Authors	Poojara S.; Dehury C.K.; Jakovits P.; Srirama S.N.
Year	2022
Published	Predictive Analytics in Cloud, Fog, and Edge Computing: Perspectives and Practices of Blockchain, IoT, and 5G
DOI	http://dx.doi.org/10.1007/978-3-031-18034-7_7
Abstract	Advancements in Internet and Telecommunications (ICT) accelerated the large-scale deployments of IoT applications including smart city, smart healthcare, and smart factory aiming for faster data processing. The latency and other challenges of cloud-centric IoT, driven edge computing-based data processing architectures. In precise, IoT data analytics requires control for dealing with the complete life cycle of data flow between data sources to sinks by building a set of data pipelines seamlessly deployed on the IoT computing continuum (Edge and Clouds). The execution of data analytic tasks is challenging in the IoT computing continuum due to heterogeneous hardware architecture at edge and cloud environments. This can be succeeded using Serverless or Function as a Service (FaaS) cloud computing model, wherein tasks are defined as virtual functions and seamlessly migrated and executed within the computing continuum. The serverless data pipelines constitutes a data source, a set of serverless functions that performs specific operations on the data and then ultimately a data sink. Similarly, AWS, Microsoft Azure and Google Cloud Providers have typical edge and cloud solutions for IoT data processing that includes the serverless entities as a part of the system. However, cost and latency vary according to cloud provider subscriptions and architecture components. So our proposed work outlines the cloud service provider-specific IoT services that are composed in designing the serverless data pipelines (SDP) for IoT data processing. Further, we investigate their performance in terms of latency, cost and cold/warm start time of serverless functions for IoT data oriented pipelines. Aligning to this, we propose AWS and Microsoft Azure based SDP architectures for predictive analytics in Industrial IoT environments and implement the real time Predictive Maintenance of Industrial Motor application. Accordingly, we measure the efficiency of AWS and Azure SDP architecture for various performance metrics such as cost, processing time and cold/warm start time for serverless invocations. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Author Keywords	Data pipelines; IoT; Predictive maintenance; Serverless

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