Eclipse is a powerful solution for software development, a reference in the field. Why not enrich its Model-Driven facilities to architect and analyse Big Data applications with quality metrics? What is more, why not adding to the Eclipse foundation instruments for software Operation as well, as opposed to software development? This talk/demo tells the story of how we addressed the two questions above within the DICE R&D collaboration http://www.dice-h2020.eu
We successfully integrated the Eclipse IDE with model-driven facilities for architecting, analysing, and operating Big Data applications, enhancing the standard Eclipse IDE outfit with our own analysis tools, specific UML profiles, as well as an orchestration engine based on Cloudify capable of deploying Big Data Application blueprints specified in standard OASIS TOSCA language on multiple cloud infrastructures, such as Amazon, FlexiOPS, and OpenStack. The talk will introduce the main features of the DICE Eclipse-based framework and explain how it can help developing Big data applications in a DevOps fashion, taking into account quality constraints. The DICE framework is based on 15 tools, built on top of the DICE IDE, and of a UML profile to model technologies such as Apache MapReduce, Spark, Storm, and several others. The remaining tools add great potential to any Eclipse IDE and are respectively for automated deployment, simulation, optimization, verification, monitoring, anomaly detection, trace checking, enhancement, quality testing, configuration optimization, fault injection, repository management and continuous integration. DICE has been applied to industrial case studies, such as in the development of data-intensive IoT application for port operations, which will be demonstrated in this talk.
Giuliano Casale received the Ph.D. degree in Computer Engineering from Politecnico di Milano, Italy, in 2006. In 2010 he joined the Department of Computing at Imperial College London, UK, where is currently a Senior Lecturer in modeling and simulation. Previously, he worked as a scientist at SAP Research UK and as a consultant in the capacity planning industry. He teaches and does research in performance engineering, cloud computing, and Big data, topics on which he has published more than 100 refereed papers. He has served in the technical program committee of over 80 conferences and workshops and as general or program co-chair for conferences in the area of performance engineering including SIGMETRICS/Performance, MASCOTS, QEST, Valuetools, ICPE, and ICAC. He is the recipient of several awards, including the best paper award at ACM SIGMETRICS 2017. He is a member of the IFIP WG 7.3 group on Computer Performance Analysis and since 2015 serves in the ACM SIGMETRICS Board of Directors.