At Zurich University of Applied Sciences, we are currently building a test track to link applied teaching with research and innovation. Such a facility allows for covering a whole range of topics: programming, autonomous driving, robotics, cloud, serverless, continuums, sensing, open data, data science, and various computing paradigms. We expect a video to be available around November that explains the facility and especially the teaching element. In this research blog post, we already report on interesting observations around the uplink between sensors and FaaS. We expect these insights to bring benefits to companies building IoT-cloud integrations.
In an increasingly self-aware and knowledge-driven software world, understanding the execution behaviour of an application is mandatory for cost-effective delivery. This applies especially to cloud functions, because many complex applications are composed of those functions. Similar short-lived, event-driven processing models can be found inside databases and message brokers. This means systematic tracing of cloud functions should be conducted so that a large variety of applications benefits from rightsizing memory allocation and associated fair microbilling.
We recently reported on our upcoming presentation on generating static trace profiles for cloud functions. In general, tracing techniques help reporting with high precision how much memory an application requires. But in practice, the memory needs depend on a number of factors that change over time. Similar to how static environment variables have been replaced by dynamic queries to updateable key-value stores to increase dynamics, we should be able to produce dynamic traces that show at least a correlation to certain values within the function, like the number of rows in a table. This research blog post shows one approach to do that.
For software development to succeed in Switzerland, that is to justify the relatively high development cost, it is essential to offer unique advantages in terms of timeliness and quality assurance. At Zurich University of Applied Sciences, we are proud to have contributed a number of tools for quality assessment and linting especially for cloudware – among others, the first Docker Compose checker, the first multi-Dockerfile linter, and the first advanced Helm and SAM consistency scans.
As we also teach Python programming to first-year engineering students, we consider it important to encourage the frequent use of linting tools. This blog post introduces such a service, naturally doubling as informal case study on how to deliver SaaS linting functionality without much effort through serverless technologies.