DICE: Quality-Driven Development of Data-Intensive Cloud Applications

Abstract

Model-driven engineering (MDE) often features quality assurance (QA) techniques to help developers creating software that meets reliability, efficiency, and safety requirements. In this paper, we consider the question of how quality-aware MDE should support data-intensive software systems. This is a difficult challenge, since existing models and QA techniques largely ignore properties of data such as volumes, velocities, or data location. Furthermore, QA requires the ability to characterize the behavior of technologies such as Hadoop/MapReduce, NoSQL, and stream-based processing, which are poorly understood from a modeling standpoint. To foster a community response to these challenges, we present the research agenda of DICE, a quality-aware MDE methodology for data-intensive cloud applications. DICE aims at developing a quality engineering tool chain offering simulation, verification, and architectural optimization for Big Data applications. We overview some key challenges involved in developing these tools and the underpinning models.

Enhancing Big Data Application Design with the DICE Framework

“…The DICE IDE and updates on the project are available at http://www.diceh2020.eu, together with a project vision document [1]. In the future we plan to release specialized "thinned" product versions of the IDE dedicated to specific technologies, such as Apache Storm, in order to supply to end users an environment targeting specific application classes (e.g., stream-based applications).…”

How bit-vector logic can help improve the verification of LTL specifications over infinite domains

“…This will allow us to increase the range and size of problems that can be tackled through the CLTL-and CLTLoc-based specification and verification approaches. In particular, the gains obtained through the novel tool will be exploited to bring verification techniques into the domain of so-called data-intensive applications [14], to analyze safety and security properties thereof.…”

Model-driven Engineering for the Configuration and Deployment of Data Processing Applications

“…The DICE project (Casale et al, 2015) also leverage model-driven engineering to manage data processing applications, and aims at providing a new UML profile for developers to understand and analyse the applications. Different from our approach, DICE focuses on the non-functional perspectives of the applications, such as reliability and performance, whereas we focus on the rapid construction of data processing from a functional point of view.…”