Automation is one of the key concerns in cloud environments. The need to introduce effort-saving automation around the process of bringing new applications to powerful cloud environments ranges from developer tooling over testing and deployment to operational concerns. According to Nokia’s Eric Bauer, application service efficiency is the ratio of service output produced to resource input consumed, and automation can significantly reduce the input effort.
We are delighted to organize the next iteration of International Workshop on Cloud, IoT and Fog Systems (and Security) – CIFS 2020 which will be colocated with UCC and BDCAT conferences to be held online this year.
For several years, we have conducted research on the design, implementation and evaluation of microservice-based applications, as well as on the assessment of characteristics of the constituent software artefacts. Yet we were so far not present in the first two editions of the International Conference on Microservices. Needless to say, we are now correcting this for the third edition of the conference with a talk on Syn.
In the context of our «Smart Cities and Regions Services Enablement» efforts, space (and to some extent time) are important dimensions. First, the digital transformation has an inherent spatial component. While the research application field is pragmatically scoped to cities and regions, indeed it spans a wider spectrum from households, quarters, districts to countries and even supranational entities. The recent wave of «surface digitalisation» has primarily affected mobile citizens (pandemic apps) and workers (video conferencing in home offices) around the world. This increased the surface over the previous one that for most citizens encompassed e-banking, e-ticketing and e-tax declarations, with various degrees of voluntariness.
For the past five years, Zurich University of Applied Sciences has hosted the Service Prototyping Lab to investigate new ways to design, prototype, implement and deploy SaaS and related cloud application concepts. We have worked with many companies from all over Switzerland to come up with innovative solutions together. We still continue this way, but we also want to reflect technological change and the evolving requirements of our research and innovation partners as well as our students in education. Therefore, we are working on reflecting this evolution also in naming, and gradually move towards a positioning as leading research partner in Switzerland around the topic of Distributed Application Computing Paradigms.
As we have recently been granted Google Cloud Research Credits for the investigation of Serverless Data Integration, we continue our exploration of open and public data. This HOWTO-style blog post presents the application domain of financial analytics and explains how to run a cloud function to achieve elastically scalable analytics. Although there are no research results to report yet, it raises a couple of interesting challenges that we or other computer scientists should work on in the future.
Cloud applications are typically designed as coupled microservices and deployed in managed containerised form. Industry trends around container build processes, deployment packages, management platforms and abstractions (e.g. cloud functions) are still fast-paced. Developers and operators need to be able to tell good from bad practices based on automatically determined metrics. Assuming they participate in this tutorial, they will learn how to do that on a hands-on level. We introduce approaches and open source tools for quantitative assessment of containers and other microservice technologies and ecosystems. On the research side, we explain how this blends with policy-driven deployments, trusted cloud execution and data science opportunities.
The three-hours tutorial will be offered at the CLOSER 2020 conference (originally scheduled in Prague, now online) in the afternoon on May 7. Registration information is available from the conference website.
Self-management is an important property of software services to increase the degree of exploiting benefitial characteristics of underlying runtime systems. Whether such services run in a managed cloud environment, on a device or somewhere else in the computing continuum, there may always be limitations in the managing runtime platform that a complementary or overarching application-level management can help to overcome. Using a Python Flask-based web service as example, this research blog post informs about our ongoing investigations into two specific self-management aspects: runtime resilience and feistiness.
As presented in a prior post, Singer.io is a modern, open-source ETL (Extract, Transform and Load) framework for integrating data from various sources, including online datasets and services, with a focus on being simple and light-weight. The basics of the framework were explored in our last post on the topic, so we will refer you to that if you are unfamiliar.
This post is about our process for deploying Singer to the cloud, more specifically, to the Cloud Foundry open source cloud application platform. This was done in the context of researching the maturity of data transformation tools in a cloud-native environment. We will explore the options for deploying Singer taps and targets to a cloud provider and discuss our implementation and deployment process in detail.