Microservices are one of the most recommended architectural styles for distributed applications that support independent development and deployment, enable rapid release, and are highly scalable and polyglot. Many well-established patterns and best practices have been documented in the literature. As there are many such guidances, they have numerous interdependencies, and system designs must adhere to many other architecture constraints, too, implementations do not always conform to those guidances. In complex or large systems, it can be hard and tedious to spot violations. Our work aims to offer automated support for architecting during the continuous evolution of microservice-based systems. More specifically we aim to provide the foundations for an automated approach for architecture reconstruction, assessing conformance to patterns and practices specific for microservice architectures, and detect possible violations. Based on this, we provide actionable options to architects for improving architecture conformance as part of a continuous feedback loop. That is, our goal is to support architecting in the context of continuous delivery practices, where architecture violations are continuously analyzed and fix options are continuously suggested.

Operators of pipeline infrastructure buried underground are in many countries required to ensure that depth of cover—a measure of the quantity of soil covering a pipeline—lie within prescribed bounds. Traditionally, monitoring depth of cover at scale has been carried out qualitatively by means of visual inspection. We proceed instead to rely on airborne remote sensing techniques to obtain densely sampled ground surface point measurements from the pipeline's right of way, from which we determine depth of cover using automated algorithms. Proceeding in our manner presents a reproducible, quantitative approach to monitoring depth of cover, yet the demands thus made by the scale of real-world pipeline monitoring scenarios on compute and storage resources can be substantial. We show that the scalability afforded by the cloud can be leveraged to address such scenarios, distributing the algorithms we employ to take advantage of multiple compute nodes and exploiting elastic storage. While the use case underlying this paper is monitoring depth of cover, our proposed architecture can be applied more broadly to a wide variety of geospatial analytics tasks carried out 'in the large', including change detection, semantic classification or segmentation, or computation of vegetation indices.

We present a novel system that allows small handheld devices to detect other devices in the local proximity and to exchange messages with them. The client devices use WLAN to access the system via a web interface and do not require additional software or modification of the device's operating system. The physical closeness of client devices is estimated based on their signal strengths measured by the system's access points. A major advantage compared to indoor positioning systems is the fact that our system does not require a training phase. We conduct a comprehensive evaluation to demonstrate the practicability of our approach.