An Ontology-Based Framework for Model-Driven Analysis of Situations in Data Centers

The capability to analyze systems and applications is commonly needed in data centers to address diverse problems such as root cause analysis of performance problems and failures, investigation of security attack propagation, and problem determination for predictive maintenance. Such analysis is typically automated using a hodgepodge of procedural code and scripts representing heuristics to be applied, and configuration databases representing state. As entities in the data center and relationships among them change, it is a challenge to keep the analysis tools up-to-date. Typically, such changes are reflected by adhoc extensions to the code and state or not reflected at all. There is a strong need for a structured, knowledge-based approach to performing such analyses where updates to entities and their relationships in the data center are reflected easily, and preferably with some degree of automation. We describe a framework based primarily on the principle of interpreting declarative representations of knowledge rather than capturing such knowledge in procedural code, and a variety of techniques for facilitating the continuous update of knowledge and state. A metamodel representing data center-specific domain knowledge forms the foundation for the framework. A model of the data center topological elements is an instantiation of the metamodel. Both metamodel and model are created through a bootstrapping process, but continuously updated using semi-automated techniques. Using the framework, we present a methodology for conducting any data center analysis activity (e.g., root cause analysis) as a model-driven topology subtree traversal, governed by knowledge embedded in the corresponding metamodel nodes. We show how to apply this methodology successfully using two use cases drawn from diverse domains: performance problem determination of a 3-tier Web application running in a virtualized environment, and an InfoSphere Streams processing application.

By: Yu Deng, Ronnie Sarkar, Harigovind Ramasamy, Rafah Hosn, Ruchi Mahindru

Published in: RC25437 in 2014


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