Helping SMEs tap into big data Demand is growing for data-intensive applications that harness the potential of big data technologies, yet smaller companies face many challenges in accessing this market. We spoke to Dr Giuliano Casale about the DICE project’s work in developing a methodology and tools that will help accelerate software development, opening up new commercial opportunities for SMEs A vast amount of data is available today on European consumers and the wider economy, leading to increased demand for software capable of exploiting it and delivering commercial benefits. However, smaller companies are typically relatively limited in their ability to develop this type of software, which was a prime motivation behind the work of the DICE project. “We aim to help SMEs to create new data-intensive applications to embrace the big data market,” explains Dr Giuliano Casale, the project’s Principal Investigator. This work centres around developing a framework intended to help accelerate the development of big data software. “The framework is comprised of two families of tools,” outlines Dr Casale. “One is the development environment, which is based on eclipse. It allows us to model the requirements and architecture of the application. It’s also possible to simulate performance and reliability.”
automate this as far as possible, so that the application can go through a sequence of improved prototypes over time,” he explains. “This is effectively a way of accelerating the release process of an application.”
The DICE solution supports the testing and automated configuration of an application, while it also gives developers the opportunity to dig into the monitoring data. Analysis of this data allows researchers to gain deeper
We aim to help SMEs to create new data-intensive applications to embrace the big data market. DevOps The tools being developed within the project cover the full cycle of design, development, deployment and ongoing iterative improvement of a data application. The project’s approach is in line with a wider movement in software engineering called DevOps, which emphasises the importance of automation. “The idea is that you create an application through a sequence of prototypes. You test it, deploy it, run it and analyse how it behaves. You keep updating your code and releasing new versions of the application with the appropriate fixes,” says Dr Casale. Updating, re-releasing and testing an application can be quite laborious, so Dr Casale says the DICE framework is designed to automate this process. “We aim to
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insights into what happened during the test, which can help further improve the quality of an application. “We are able to correlate the monitoring data to the architecture that was designed during development. We can then make recommendations on what the user should change to improve the application,” outlines Dr Casale. This process is automated, greatly improving efficiency and allowing developers to reach the market faster. “Our framework supplies tools that enable companies to analyse the properties of an application and to improve it,” continues Dr Casale. This work holds great relevance for smaller companies looking to make use of big data technologies. Currently a company looking to develop a new data-intensive application
would have to learn how to use multiple different tools; the DICE methodology and associated tools offers an attractive alternative. “In DICE we have created a unified framework, which means companies have to learn just one approach to develop these applications. Everything is in the same place, with the same paradigm and with compatible tools,” says Dr Casale. This will help SMEs to not only develop new software on faster timescales, but also to improve the quality of their big data applications. “The DICE methods will enable companies to create big data applications with better performance and reliability than they would have been able to achieve otherwise,” continues Dr Casale.
DICE Developing Data-Intensive Cloud Applications with Iterative Quality Enhancements DICE aims at defining a framework for quality-driven development of Big Data applications. DICE offers a novel UML profile and tools to help software designers reasoning about reliability, safety and efficiency of data-intensive applications. Project Coordinator Dr Giuliano Casale Department of Computing Imperial College London T: +44 20 759 42920 E: g.casale@imperial.ac.uk W: www.dice-h2020.eu
Dr Giuliano Casale is a Senior Lecturer at Imperial College London, UK. He teaches and does research in performance engineering, cloud computing, and Big data, topics on which he has published more than 100 papers. He served in the program committee of over 80 research conferences. He serves in the ACM SIGMETRICS Board of Directors.
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