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Chiron workflow engine

Chiron is a workflow execution engine designed to execute workflows in parallel in High Performance Computing (HPC) environment. A major goal of Chiron is to take a workflow specification and provide for data parallelism automatically with runtime query provenance support. Data is fragmented from a set of parameter sweep combinations or input dataset. Parallel processing is obtained in a MapReduce (Hadoop) style, however, Chiron engine is supported by a workflow algebra, which allows for optimization, dynamic scheduling and runtime workflow steering. Some additional libraries are necessary to execute Chiron such as the JDBC drivers to connect with PostgreSQL database and MPJ libraries.

The Chiron workflow engine was first created at the COPPE Institute from The Federal University of Rio de Janeiro in the NACAD Lab with Marta Mattoso‘s research group in March 2012. Recent contributions have been made by CEFET/RJ and students from the Polytechnic School of the Federal University of Rio de Janeiro, in addition to developers from Marta Mattoso’s research Chiron team. The Chiron developers welcome contributions in the form of patches and bug reports (preferably with a minimal test case that reliably reproduces the error) to the official mailing lists. Many thanks to SourceForge and SVN for hosting the project. You can find out what is currently happening in the development branch by checking out the homepage, and you can see how many people are downloading the library on the statistics page.

Authors: Eduardo Ogasawara, Jonas Dias, Vitor Silva, Fernando Chirigati, Daniel de Oliveira, Fabio Porto, Patrick Valduriez, and Marta Mattoso

Main paper: https://dx.doi.org/10.1002/cpe.3032

Project homepage: http://chironengine.sourceforge.net/index.php/home

 

Eduardo Ogasawara

Eduardo Ogasawara has been a professor at the Department of Computer Science at the Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ) since 2010. He holds a D.Sc. in Systems and Computer Engineering from COPPE/UFRJ. Between 2000 and 2007, he worked in the Information Technology (IT) sector, gaining extensive experience in workflows and project management. With a strong background in Data Science, he is currently focused on Data Mining and Time Series Analysis. He is a member of IEEE, ACM, and SBC. Throughout his career, he has authored numerous published articles and led projects funded by agencies such as CNPq and FAPERJ. Currently, he heads the Data Analytics Lab (DAL) at CEFET/RJ, where he continues to advance research in Data Science.