Managing uncertainty in self-adaptive systems with plan reuse and stochastic search

Many software systems operate in environments where change and uncertainty are the rule, rather than exceptions. Techniques for self-adaptation allow these systems to automatically respond to environmental changes, yet they do not handle changes to the adaptive system itself, such as the addition or...

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Bibliographic Details
Published in:2018 IEEE ACM 13th International Symposium on Software Engineering for Adaptive and Self Managing Systems (SEAMS) pp. 40 - 50
Main Authors: Kinneer, Cody, Coker, Zack, Wang, Jiacheng, Garlan, David, Le Goues, Claire
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 28.05.2018
Series:ACM Conferences
Subjects:
cloud services
Computer science
Computer systems organization > Architectures > Distributed architectures > Cloud computing
Computer systems organization > Dependable and fault-tolerant systems and networks
Computing methodologies > Artificial intelligence > Control methods
Data centers
Genetic programming
plan reuse
Planning
Search problems
self- systems
Servers
Software and its engineering > Software creation and management > Search-based software engineering
Software and its engineering > Software creation and management > Software post-development issues > Software evolution
Uncertainty
planning
cloud services
genetic programming
self- systems
plan reuse
uncertainty
ISBN:9781450357159, 1450357156
Online Access:Get full text
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Summary:Many software systems operate in environments where change and uncertainty are the rule, rather than exceptions. Techniques for self-adaptation allow these systems to automatically respond to environmental changes, yet they do not handle changes to the adaptive system itself, such as the addition or removal of adaptation tactics. Instead, changes in a self-adaptive system often require a human planner to redo an expensive planning process to allow the system to continue satisfying its quality requirements under different conditions; automated techniques typically must replan from scratch. We propose to address this problem by reusing prior planning knowledge to adapt in the face of unexpected situations. We present a planner based on genetic programming that reuses existing plans. While reuse of material in genetic algorithms has recently applied successfully in the area of automated program repair, we find that naïvely reusing existing plans for self-* planning actually results in a loss of utility. Furthermore, we propose a series of techniques to lower the costs of reuse, allowing genetic techniques to leverage existing information to improve planning utility when replanning for unexpected changes.
ISBN:9781450357159
1450357156
DOI:10.1145/3194133.3194145