Type of Document Master's Thesis Author Heffner, Michael Alan Author's Email Address mheffner@vt.edu URN etd-05162004-212101 Title A Runtime Framework for Adaptive Compositional Modeling Degree Master of Science Department Computer Science Advisory Committee
Advisor Name Title Ramakrishnan, Naren Committee Co-Chair Varadarajan, Srinidhi Committee Co-Chair Ribbens, Calvin J. Committee Member Keywords
- Object-Level Patching
- Adaptive Compositional Modeling
- Runtime Framework
Date of Defense 2004-05-07 Availability unrestricted Abstract The rapid emergence of embedded devices and sensor networks thatfrequently exchange object-level images foretells an increasing reliance
on object-level systems. Additionally, nearly all computing systems,
including control systems, enterprise applications, scientific codes and
dynamic libraries operate eventually at the object code level. Studying
adaptivity and runtime composition issues in such systems is becoming an
important focus of systems research. In this thesis, we describe an
object-level framework that will manipulate an object module to
instrument control functionality and adaptivity in order to realize
complex compositional scenarios. Using function and parameter remapping
capabilities, our framework transcends programming language and design
boundaries, and enables applications to adapt dynamically during
runtime. We introduce the capability to ``restart'' an application
automatically, a feature we utilize to support adaptivity not only
spatially, over the algorithm domain, but temporally as well. A
high-level adaptive control language based on XML is presented that
allows complex adaptive scenarios to be expressed
concisely. Additionally, the construction of several adaptive scenarios
using our framework is illustrated, along with several experiments in
``learning adaptivity`` using reinforcement learning techniques.
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