DICE - Developing Data-Intensive Cloud Applications with Iterative Quality Enhancements
DataInc is a small software vendor selling cloud-based Big Data services. DataInc has to deliver an initial version of a new Big Data application within 3 months, capable of processing sensor data acquired from a geographical area, with the goal of increasing coverage of areas, sensors and compute capacity on a monthly basis.
The contractors require the application to be highly-available, scalable and cost-efficient. Yet, software developers are puzzled on how to implement a complex Big Data application of this kind in just 3 months: they could rush development but how could they satisfy all the quality requirements? What architecture should they adopt, keeping in mind the future evolution of the service? How should they accelerate quality testing for this initial release?
Small & medium enterprises
Software engineers at DataInc discover the DICE Horizon 2020 project.DICE provides an Eclipse-based integrated development environment (IDE) to design the new Big Data application.The IDE features the DICE profile, which extends UML to describe Big Data applications. The DICE quality analysis tools, integrated with the IDE, compare possible architectures by predicting their expected reliability, efficiency and safety characteristics.
DICE also generates a cloud deployment plan for the application and offers testing tools for initial quality assessment. Once the application becomes operational, the application quality characteristics are “learned-as-you-go” from monitoring data and are fed back to the developer for evaluation. The iterative quality enhancement toolchain supports the developer in this task by identifying design anti-patterns and performance outliers in the monitoring data.
Using DICE, DataInc can rapidly implement the first version of the Big Data application and continuously evolve its quality characteristics over time.
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