Inferring Meta-models for Runtime System Data from the Clients of Management APIs

Abstract. A new trend in runtime system monitoring is to utilize MOFbased 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.

• Type: Journal
• Year: 2010
• Where: MODELS
• Authors: Hui Song, Gang Huang, Yingfei Xiong, Franck Chauvel, Yanchun Sun, Hong Mei
• Keywords: Analyzing, Inferred Meta-models, MODELS 2010, Natural Language Processing, Runtime Models