Introducing selective Lambdafication
“Lambdafication” is the automated transformation of source code to make it run on AWS Lambda. It is a provider-specific flavour of generic “FaaSification” which is our ultimate research goal. With our Lambdafication tooling, we offer application engineers today the possibility to step into the serverless world without much effort, and leave the more challenging research tasks for the summer time.
PyParis is a community-organised conference on all topics around the Python programming language. The expected target group are primarily practitioners and researchers in the greater capital region of France, but also international engineers and language advocates. At Zurich University of Applied Sciences, Python is taught as automation and statistics application language to more than 200 business engineering, aviation and traffic engineering undergraduates per year. It is furthermore used a lot in research, including several prototypes resulting from the Service Prototyping Lab. Therefore, it was consequential for us to attend the conference and to contribute an in-depth tutorial on one of our research topics, Function-as-a-Service, to its attendees.
After too many hours of trial and error and searching for the right solution on how to properly write and integrate your own backend in cinder, here are all the steps and instructions necessary. So if you are looking for a guide on how to integrate your own cinder driver, look no further. Continue reading
Applications are increasingly delivered for cloud deployment as set of composite artefacts such as containers. The composition descriptions vary widely: There are Docker compose files, Vamp blueprints, Kubernetes descriptors, OpenShift service instance templates, and more. Ideally, taking these compositions and deploying them somewhere would always work. In practice, it is more complex than that. Commercial production environments are often constrained depending on the chosen pricing plan. Many applications would still run but due to over-estimating deployment information do not “fit” into the target environment. In this blog post, we look at how to “right-size” an application deployed into such a constrained Kubernetes instance, and furthermore propose a tool to automate this process.