Software Connector Classification and Selection for Data-Intensive Systems

Authors:
Chris A. Mattmann;David Woollard;Nenad v;Reza Mahjourian
Affiliations:
California Institute of Technology;Univ. of Southern California;Univ. of Southern California;Univ. of Florida
Venue:
IWICSS '07 Proceedings of the Second International Workshop on Incorporating COTS Software into Software Systems: Tools and Techniques
Year:
2007

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Abstract

Data-intensive systems and applications transfer large volumes of data and metadata to highly distributed users separated by geographic distance and organizational boundaries. An influential element in these large volume data transfers is the selection of the appropriate software connector that satisfies user constraints on the required data distribution scenarios. Currently, this task is typically accomplished by consulting "gurus", who rely on their intuitions, at best backed by anecdotal evidence. In this paper we present a systematic approach for selecting software connectors based on eight key dimensions of data distribution that we use to represent the data distribution scenarios. Our approach, dubbed DISCO, has been implemented as a Java-based framework. The early experience with DISCO indicates good accuracy and scalability.