Increasingly software systems operate in environments of change and uncertainty, where the system's ability to satisfy its quality objectives depends on its ability to adapt. Self-adaptation allows these systems to manage this challenge by autonomously adapting to changes in their environments. While self-* systems are designed precisely to manage uncertainty, unexpected changes may violate design assumptions, resulting in the system failing to satisfy its quality attribute requirements. When this occurs, the planner must generate a new plan, an expensive operation for large systems. As autonomous systems increase in size, interconnectedness, and complexity, this cost can quickly become prohibitive.

This thesis addresses this problem by leveraging information contained in prior plans to reduce the replanning necessary to respond to an unexpected change. Even in the face of an unexpected change, some of the insights contained in existing plans are likely to remain applicable. For example, an autonomous aerial vehicle encountering an unexpected obstacle will need to replan to avoid the obstacle, but the drone may be able to return to its prior plan after this maneuver. A larger change will reduce the amount of reuse that is possible, for example changing the drone's mission to fly to a new location, but still, the takeoff and landing procedures may be reused. This thesis reuses existing adaptation plans by seeding a genetic algorithm with these plans. This enables a scalable self-* planner that can replan in complex systems with large search spaces.

While the idea of plan reuse is intuitive, in practice plan reuse is difficult and may even be worse than replanning from scratch if not performed carefully. This dissertation provides reuse enhancing approaches to reduce the evaluation time of candidate plans, an approach for building reusable repertoires of plans and identifying generalizable plan fragments, and a co-evolutionary extension to enable plan reuse for security. The thesis is evaluated on three simulated case study systems, including a cloud-based web service provider, a team of autonomous aerial vehicles, and an enterprise business system under a cyber attack. Ultimately, plan reuse will enable large self-* systems to replan even after unexpected changes.

122 pages

Thesis Committee:
Claire Le Goues (Co-Chair)
David Garlan (Co-Chair)
Fei Fang
Betty Cheng (Mighigan State University)

James D. Herbsleb, Director, Institute for Software Research
Martial Hebert, Dean, School of Computer Science

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