Type of Document	Dissertation
Author	Kavimandan, Amogh
Author's Email Address	amoghk@dre.vanderbilt.edu
URN	etd-12032008-180008
Title	Reusable Model Transformation Techniques for Automating Middleware QoS Configuration in Distributed Real-time and Embedded Systems
Degree	PhD
Department	Computer Science
Advisory Committee	Advisor Name Title Aniruddha Gokhale Committee Chair Douglas Schmidt Committee Member Gabor Karsai Committee Member Janos Sztipanovits Committee Member Jeff Gray Committee Member
Keywords	Model Transformation Middleware QoS Configuration MDE Templatized Transformation Program transformation (Computer programming) Embedded computer systems -- Quality control Model-driven software architecture
Date of Defense	2008-11-14
Availability	unrestricted
Abstract Contemporary component middleware platforms provide a high degree of flexibility and configurability to support the development and operational life-cycles of distributed real-time and embedded (DRE) systems. This flexibility of component middleware, however, can also complicate DRE system configuration since assuring their quality of service (QoS) properties requires mapping their QoS requirements onto the right set of configuration options of the underlying middleware platform. This dissertation provides the following contributions to the development of component-based DRE systems. First, it describes the design and implementation of Quality of service pICKER (QUICKER) model-driven toolchain that combines domain-specific modeling to capture system QoS requirements at domain-level abstractions to simplify the QoS requirements specification process, and model transformations to automate the mapping of these requirements to middleware-specific QoS options. Second, it evaluates the domain-specific requirements modeling, and the generated QoS configurations in the context of representative DRE systems. Finally, it describes a templatized model transformation technique for developing general-purpose, platform-independent QoS mappings that can operate across multiple platforms.
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