Brandenburger Tor
Nov. 15–16, 2018 – Fraunhofer FOKUS, Berlin, Germany

Dr. Francisco Gortázar 

Project ElasTest/Universidad Rey Juan Carlos, Spain

Dr. Francisco Gortázar is a Tenure Professor at Universidad Rey Juan Carlos with more than 12 years of experience in teaching distributed systems and concurrent programming. He has a strong connection with the industry, specifically providing consultancy about cloud technologies and improving testing and Continuous Integration and Deployment activities. Currently he is coordinating the H2020 project ElasTest, where he is researching novel ways of testing cloud infrastructures and applications, including 5G, IoT and real-time communication systems. 


Paving the Way for Testing 5G Infrastructures and Applications

In recent years, there’s been more and more pressure to ship software faster. Agile methodologies, infrastructure as code, or deployment automation are all approaches that seek to reduce the time required between versions. Being able to maintain this pace without compromising product quality is key. However, quality is still something that is measured with techniques from the past decade, and are in general far away from current practices like cloud environments, containers or serverless architectures. Moreover, they do not consider the distributed nature of modern applications, comprised of different services connected through a network. When we consider 5G infrastructures and applications, things are even worse, as we would like to know the impact of changes in performance and resiliency of the network infrastructure. We started the ElasTest H2020 project to deal with these challenges and shed light into the behaviour of infrastructure and applications. The ElasTest project can be wired into any infrastructure, collect and visualize data, and perform root cause analysis in the presence of failures, in ways that help the team keep the pace of development and at the same time, maintain high quality standards. The talk will introduce the main features that help teams attain good observability of the system by providing the right abstractions on top of the underlying metrics.