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Empirical systems research is facing a dilemma. Minor aspects of an experimental setup can have a significant impact on its associated performance measurements and potentially invalidate conclusions drawn from them. Examples of such influences, often called hidden factors, include binary link order, process environment size, compiler generated randomized symbol names, or group scheduler assignments...
In recent years autonomic computing, specifically autonomic data centre management has gained significant attention. Human intervention be minimized to reduce the operating costs of business applications. In this paper we focus our attention to the self-repair dimension and present a flexible probabilistic framework to develop agents for self-repair in the context of business-information-system components...
The normal operation of enterprise software systems can be modeled by stable correlations between various system metrics; errors are detected when some of these correlations fail to hold. The typical approach to diagnosis (i.e., pinpoint the faulty component) based on the correlation models is to use the Jaccard coefficient or some variant thereof, without reference to system structure, dependency...
The manual process to identifying causes of failure in distributed information systems is difficult and time-consuming. The underlying reason is the large size and complexity of these systems, and the vast amount of monitoring data they generate. Despite its high cost, this manual process is necessary in order to avoid the detrimental consequences of system downtime. Several studies and operator practice...
Self-adaptive and self-organizing systems must be self-monitoring. Recent research has shown that self-monitoring can be enabled by using correlations between monitoring variables (metrics). However, computer systems often make a very large number of metrics available for collection. Collecting them all not only reduces system performance, but also creates other overheads related to communication,...
Modern software systems expose management metrics to help track their health. Recently, it was demonstrated that correlations among these metrics allow faults to be detected and their causes localized. In particular, linear regression models have been used to capture metric correlations. We show that for many pairs of correlated metrics in software systems, such as those based on Java Enterprise Edition...
Management metrics of complex software systems exhibit stable correlations which can enable fault detection and diagnosis. Current approaches use specific analytic forms, typically linear, for modeling correlations. In this paper we use normalized mutual information as a similarity measure to identify clusters of correlated metrics, without knowing the specific form. We show how we can apply the Wilcoxon...
A correctly functioning enterprise-software system exhibits long-term, stable correlations between many of its monitoring metrics. Some of these correlations no longer hold when there is an error in the system, potentially enabling error detection and fault diagnosis. However, existing approaches are inefficient, requiring a large number of metrics to be monitored and ignoring the relative discriminative...
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