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Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock the potential of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services. Hence, orchestrating ML pipelines that encompass model training and implication involved in the holistic development lifecycle of an IoT application often leads to complex system integration. This article provides a comprehensive and systematic survey of the development lifecycle of ML-based IoT applications. We outline the core roadmap and taxonomy and subsequently assess and compare existing standard techniques used at individual stages. [ABSTRACT FROM AUTHOR]

Summary: Containers are gaining popularity over virtual machines as they provide the advantages of virtualization with the performance of near bare metal. The uniformity of support provided by Docker containers across different cloud providers makes them a popular choice for developers. Evolution of microservice architecture allows complex applications to be structured into independent modular components making them easier to manage. High‐performance computing (HPC) applications are one such application to be deployed as microservices, placing significant resource requirements on the container framework. However, there is a possibility of interference between different microservices hosted within the same container (intracontainer) and different containers (intercontainer) on the same physical host. In this paper, we describe an extensive experimental investigation to determine the performance evaluation of Docker containers executing heterogeneous HPC microservices. We are particularly concerned with how intracontainer and intercontainer interference influences the performance. Moreover, we investigate the performance variations in Docker containers when control groups (cgroups) are used for resource limitation. For ease of presentation and reproducibility, we use Cloud Evaluation Experiment Methodology (CEEM) to conduct our comprehensive set of experiments. We expect that the results of evaluation can be used in understanding the behavior of HPC microservices in the interfering containerized environment. [ABSTRACT FROM AUTHOR]