User preference based autonomic generation of self-adaptive rules
Tianqi Zhao, Haiyan Zhao, Wei Zhang, Zhi Gang Jin · 2014
The internetware system is a complex and distributed self-adaptive system, which challenges the method for making adaptation plans. Rule based approaches are very efficient to make plans in adaptive systems. To enable effective rule-based adaptation, we need to write a set of well behaved self-adaptive rules which could always lead to desirable states. This adaptive rules-set needs to be correct, com- plete, conflicts-free and well satisfy user goals, and it should updates according to user preferences. However, it is a difficult task for sys- tem users to define such a set of rules. To resolve this problem, we provide an rule generation engine, which could automatically generate well behaved self-adaptive rules according to user pref- erences. The rule generation engine is realized by a three-stage algorithm: stage 1 integrates user goals and user preferences, stage 2 establishes 1-1 tracing relationship between a context state and its desirable software configuration, stage 3 extracts self-adaptive rules from the tracing relationship between context states and software configurations. We will apply this engine to generate self-adaptive rules for a smart phone system, and evaluate the quality of generated self-adaptive rules.