Towards smarter continuum application designs

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.

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Upcoming talks on serverless application engineering

XP 2020, the 21st International Conference on Agile Software Development, is hosting the Second European Symposium on Serverless Computing and Applications (ESSCA). In contrast to the first edition, it is a bit smaller this time, and exclusively online as opposed to the planned venue Copenhagen.

Our research team is present with two talks:

  • Josef Spillner and Daiana Boruta will demonstrate how to trace the memory consumption of cloud functions, and reason about how to use the trace profile for memory autotuning.

Two additional talks are scheduled on optimising the startup time of functions (TU Berlin) and on performance predictions and resource sensitivity (Intel).

Register to participate. And in case you miss it – talk to us about your serverless computing innovation needs!

Research and Innovation around Distributed Application Computing Paradigms

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.

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Financial Analytics with Cloud Functions

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.

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Microservices in Numbers: Diagnostic Docker Deep Dive (Online Tutorial at CLOSER 2020)

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.

Investigation of Self-Management for Flask-based Services

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.

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Deploying Singer.io to the Cloud

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.

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Experimenting with WebAssembly in a Serverless context

Note

This post was first published on Medium by Leonardas (Badrie) Persaud – one of the students who was involved in this project. The post is republished here as the project was run within the context of the Software Maintenance and Evolution course run by Sebastiano and the project itself was supervised by Seán. The students involved in the project were UZH CS Master’s students: Badrie L. Persaud, Bill Bosshard, and, William Martini and all project related content is in the project’s github repo.

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Coronavirus Mapping as a Service

Note: This post does not contain any medical advice or suggestions on how to act and react. If you are looking for that, you are looking in the wrong place.

Economy and society in Switzerland are currently highly affected by the spreading second version of the coronavirus (SARS-CoV-2) that causes the associated infectious desease (COVID-19). The World Health Organisation (WHO) has classified the virus outbreak as PHEIC on January 30 and as pandemy on March 11. In Switzerland, the state emergency level Eminent/Special Situation was reached on February 28, and further restrictions led de-facto to the subsequent level Extraordinary Situation on March 13. This blog post reports on how the outbreak evolution can be continuously visualised as a reliable service with off-the-shelf tools.

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Log files: Semantic compression and learned attribute-based notifications

Many providers of hosted services, including cloud applications, are subject to a contradiction in handling log data. On the one hand, storing logs consumes resources and should be minimised or avoided altogether to save resource cost. On the other hand, regulatory constraints such as keeping the data for the purpose of future audits exist. A smart solution to encode the data appropriately needs to be found. The coding encompasses both compression, to keep resource use low, and encryption, to prevent leaking information to unauthorised parties, for instance when logging for the purpose of intrusion detection. On an algorithmic level, the encoded data should still be usable for computation, in particular comparison and search. In this blog post, based on the didactic log example shown in the figure below, we present algorithms and architectures to handle cloud log files in a smart way.

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