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We present a method for automatically extracting from a running system an indexable <i>signature</i> that distills the essential characteristic from a system state and that can be subjected to automated clustering and similarity-based retrieval to identify when an observed system state is similar to a previously-observed state. This allows operators to(More)
The effects of the L-type Ca2+ channel blocker nimodipine on the spectrum of the spontaneous neural noise (SNN) and the waveform of the gross sound-evoked compound action potential (CAP) were investigated by perilymphatic perfusion in the guinea pig cochlea. Both the SNN and the CAP were reversibly suppressed by nimodipine. The percentage reduction in SNN(More)
Violations of service level objectives (SLO) in Internet services are urgent conditions requiring immediate attention. Previously we showed [1] that Tree-Augmented Bayesian Networks or TAN models are effective at identifying which low-level system properties were correlated to high-level SLO violations (the metric attribution problem) under stable(More)
This paper demonstrates that the dependability of generic, evolving J2EE applications can be enhanced through a combination of a few recovery-oriented techniques. Our goal is to reduce downtime by automatically and efficiently recovering from a broad class of transient software failures without having to modify applications. We describe here the integration(More)
Intracochlear perfusion and gross potential recording of sound-evoked neural and hair cell responses were used to study the site of action of the L-type Ca(2+) channel blocker nimodipine in the guinea pig inner ear. In agreement with previous work nimodipine (1-10 microM) caused changes in both the compound auditory nerve action potential (CAP) and the DC(More)
Recent research activity [2, 12, 27, 10, 1] has shown encouraging results for performance debugging and failure diagnosis and detection in systems by using approaches based on automatically inducing models and deriving correlations from observed data. This paper explores research questions and preliminary results regarding the next steps in advancing this(More)
Most modern systems generate abundant and diverse log data. With dwindling storage costs, there are fewer reasons to summarize or discard data. However, the lack of tools to efficiently store and cross-correlate heterogeneous datasets makes it tedious to mine the data for analytic insights. In this paper, we present Splunk, a semi-structured time series(More)