•   When: Thursday, April 30, 2015 from 12:00 PM to 02:00 PM
•   Speakers: John M. Ewing
•   Location: Nguyen Engineering, Room 2901
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ABSTRACT

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. Autonomic computing is a set of technologies that enable 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.

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. A detailed set of experiments demonstrates the effectiveness and scalability of the approaches, and also reveals some of the challenges in applying meta-optimization and metacontrol to the autonomic management of SOA systems. Many of the techniques described in this dissertation have broad applicability in autonomic computing and related fields.