<p> Service Oriented Architectures (SOA) are an emerging software engineering discipline that builds software systems and applications by connecting and integrating well-defined, distributed, reusable software service instances. SOA can speed development time and reduce costs by encouraging reuse, but this new service paradigm presents significant challenges. Many SOA applications are dependent upon service instances maintained by vendors and/or separate organizations. Applications and composed services using disparate providers typically demonstrate limited autonomy with contemporary SOA approaches. Availability may also suffer with the proliferation of possible points of failure—restoration of functionality often depends upon intervention by human administrators. </p><p> Autonomic computing is a set of technologies that enables self-management of computer systems. When applied to SOA systems, autonomic computing can provide automatic detection of faults and take restorative action. Additionally, autonomic computing techniques possess optimization capabilities that can leverage the features of SOA (e.g., loose coupling) to enable peak performance in the SOA system's operation. This dissertation demonstrates that autonomic computing techniques can help SOA systems maintain high levels of usefulness and usability. </p><p> This dissertation presents a centralized autonomic controller framework to manage SOA systems in dynamic service environments. The centralized autonomic controller framework can be enhanced through a second meta-optimization framework that automates the selection of optimization algorithms used in the autonomic controller. A third framework for autonomic meta-controllers can study, learn, adjust, and improve the optimization procedures of the autonomic controller at run-time. Within this framework, two different types of meta-controllers were developed. The <b>Overall Best</b> meta-controller tracks overall performance of different optimization procedures. <b>Context Best</b> meta-controllers attempt to determine the best optimization procedure for the current optimization problem. Three separate Context Best meta-controllers were implemented using different machine learning techniques: 1) K-Nearest Neighbor (<b>KNN MC</b>), 2) Support Vector Machines (SVM) trained offline (<b>Offline SVM</b>), and 3) SVM trained online (<b>Online SVM</b>). </p><p> A detailed set of experiments demonstrated the effectiveness and scalability of the approaches. Autonomic controllers of SOA systems successfully maintained performance on systems with 15, 25, 40, and 65 components. The <b>Overall Best</b> meta-controller successfully identified the best optimization technique and provided excellent performance at all levels of scale. Among the <b>Context Best</b> meta-controllers, the <b>Online SVM</b> meta-controller was tested on the 40 component system and performed better than the <b>Overall Best</b> meta-controller at a 95% confidence level. Evidence indicates that the <b>Online SVM</b> was successfully learning which optimization procedures were best applied to encountered optimization problems. The <b>KNN MC</b> and <b>Offline SVM</b> were less successful. The <b>KNN MC</b> struggled because the KNN algorithm does not account for the asymmetric cost of prediction errors. The <b>Offline SVM</b> was unable to predict the correct optimization procedure with sufficient accuracy—this was likely due to the challenge of building a relevant offline training set. The meta-optimization framework, which was tested on the 65 component system, successfully improved the optimization techniques used by the autonomic controller. </p><p> The meta-optimization and meta-controller frameworks described in this dissertation have broad applicability in autonomic computing and related fields. This dissertation also details a technique for measuring the overlap of two populations of points, establishes an approach for using penalty weights to address one-sided overfitting by SVM on asymmetric data sets, and develops a set of high performance data structure and heuristic search templates for C++.</p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:3706982 |
| Date | 11 July 2015 |
| Creators | Ewing, John M. |
| Publisher | George Mason University |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
Page generated in 0.0025 seconds