Exploiting Local and Global Invariants for the Management of Large Scale Information Systems

This paper presents a data oriented approach to modeling the complex computing systems, in which an ensemble of correlation models are discovered to represent the system status. If the discovered correlations can continually hold under different user scenarios and workloads, they are regarded as invariants of the information system. In our previous work, we have developed an algorithm to automatically search the invariants between any pair of system attributes, which we call local invariants. However that method is unable to deal with the high order dependency models due to the combinatorial explosion of search space. In this paper we use Bayesian regression technique to discover those high order correlation models, called global invariants. We treat each attribute as a response variable in turn and express its dependency with the other attributes in a regression model. By adding the prior constraint of Laplacian distribution to the regression coefficients, we can find the solution in which only the correlated attributes with respect to the response have nonzero regression coefficients. After that we further consider the temporal dependencies of those extracted attributes by incorporating their past observations. We also provide a confidence metric and a validation procedure to measure the reliability of learned models. If the model does not break down in the validation, it is regarded as a true invariant of the system. Experimental results on a real wireless networking system show that the discovered invariants can be used to effectively detect system failures as well as provide valuable information about the failure source.