Inferring meta-models for runtime system data from the clients of management APIs

Authors:
Hui Song;Gang Huang;Yingfei Xiong;Franck Chauvel;Yanchun Sun;Hong Mei
Affiliations:
Key Lab of High Confidence Software Technologies, Ministry of Education, School of Electronic Engineering & Computer Science, Peking University, China;Key Lab of High Confidence Software Technologies, Ministry of Education, School of Electronic Engineering & Computer Science, Peking University, China;Generative Software Development Lab, University of Waterloo, Canada;Key Lab of High Confidence Software Technologies, Ministry of Education, School of Electronic Engineering & Computer Science, Peking University, China;Key Lab of High Confidence Software Technologies, Ministry of Education, School of Electronic Engineering & Computer Science, Peking University, China;Key Lab of High Confidence Software Technologies, Ministry of Education, School of Electronic Engineering & Computer Science, Peking University, China
Venue:
MODELS'10 Proceedings of the 13th international conference on Model driven engineering languages and systems: Part II
Year:
2010

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Abstract

A new trend in runtime system monitoring is to utilize MOF-based techniques in analyzing the runtime system data. Approaches and tools have been proposed to automatically reflect the system data as MOF compliant models, but they all require users to manually build the meta-models that define the types and relations of the system data. To do this, users have to understand the different management APIs provided by different systems, and find out what kinds of data can be obtained from them. In this paper, we present an automated approach to inferring such meta-models by analyzing client code that accesses management APIs. A set of experiments show that the approach is useful for realizing runtime models and applicable to a wide range of systems, and the inferred meta-models are close to the reference ones.