Vienna, the second-largest city in the German-speaking world, had become a meeting place earlier this week for software and service engineers who explore the crossroads of software architecture, DevOps processes and continuous-* (software development, integration, delivery) approaches. The 1st Vienna Software Seminar had mixed business and academic participants and has been of particular interest to architects and practitioners who want to migrate applications or related processes into cloud environments and are in need of relevant methods and tools. With its interactive agile format and focus on break-out groups, the seminar was structured so that topics could be discussed in detail and grouped by interest. This report summarises the four-day event including some highlights from selected discussions from a participant perspective.
Our own researchers Piyush and Josef are in Austin, the capital of the lone star state Texas to attend the current iteration of IEEE/ACM International Conference on Utility and Cloud Computing which takes place in conjunction with the International Conference on Big Data Computing, Applications and Technologies. ICCLab’s and SPLab’s recent research results have been accepted as multiple peer-reviewed workshop papers and a tutorial presented on the first day and a work in progress poster which will be presented in the next days.
In this series of blog posts, starting with this one, we will present our views and analysis of the results that will be presented at this event by cloud researchers from around the world.
As the trend continues to move towards Serverless Computing, Edge Computing and Functions as a Service (FaaS), the need for a storage system that can adapt to these architectures grows ever bigger. In a scenario where smart cars have to make decisions on a whim, there is no chance for that car to ask a data center what to do in this scenario. These scenarios constitute a driver for new storage solutions in more distributed architectures. In our work, we have been considering a scenario in which there is a distributed storage solution which exposes different local endpoints to applications distributed over a mix of cloud and local resources; such applications can give the storage infrastructure and indicator of the nature of the data which can then be used to determine where it should be stored. For example, data could be considered to be either latency-sensitive (in which case the storage system should try to store it as locally as possible) or loss sensitive (in which case the storage system should ensure it is on reliable storage). Continue reading