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With the increasing number of Internet of Things (IoT) devices, massiveamounts of raw data is being generated. The latency, cost, and other challengesin cloud-based IoT data processing have driven the adoption of Edge and Fogcomputing models, where some data processing tasks are moved closer to datasources. Properly dealing with the flow of such data requires building datapipelines, to control the complete life cycle of data streams from dataacquisition at the data source, edge and fog processing, to Cloud side storageand analytics. Data analytics tasks need to be executed dynamically atdifferent distances from the data sources and often on very heterogeneoushardware devices. This can be streamlined by the use of a Serverless (or FaaS)cloud computing model, where tasks are defined as virtual functions, which canbe migrated from edge to cloud (and vice versa) and executed in an event-drivenmanner on data streams. In this work, we investigate the benefits of buildingServerless data pipelines (SDP) for IoT data analytics and evaluate threedifferent approaches for designing SDPs: 1) Off-the-shelf data flow tool (DFT)based, 2) Object storage service (OSS) based and 3) MQTT based. Further, weapplied these strategies on three fog applications (Aeneas, PocketSphinx, andcustom Video processing application) and evaluated the performance by comparingtheir processing time (computation time, network communication and disk accesstime), and resource utilization. Results show that DFT is unsuitable forcompute-intensive applications such as video or image processing, whereas OSSis best suitable for this task. However, DFT is nicely fit forbandwidth-intensive applications due to the minimum use of network resources.On the other hand, MQTT-based SDP is observed with increase in CPU and Memoryusage as the number of...<truncted to fit character limit in Arxiv>

Serverless data pipeline approaches for IoT data in fog and cloud computing