SLAML — October 23, 2011
Session 1
Practical Experiences with Chronics Discovery in Large Telecommunications Systems
Chronics are recurrent problems that fly under the radar of operations teams because they do not perturb the system enough to set off alarms or violate service-level objectives. The discovery and diagnosis of never-before seen chronics poses new challenges as they are not detected by traditional threshold-based techniques, and many chronics can be present in a system at once, all starting and ending at different times. In this paper, we describe our experiences diagnosing chronics using server logs on a large telecommunications service. Our technique uses a scalable Bayesian distribution learner coupled with an information theoretic measure of distance (KL divergence), to identify the attributes that best distinguish failed calls from successful calls. Our preliminary results demonstrate the usefulness of our technique by providing examples of actual instances where we helped operators discover and diagnose chronics.
BLR-D: Applying Bilinear Logistic Regression to Factored Diagnosis Problems
In this paper, we address a pattern of diagnosis problems in which each of J entities produces the same K features, yet we are only informed of overall faults from the ensemble. Furthermore, we suspect that only certain entities and certain features are leading to the problem. The task, then, is to reliably identify which entities and which features are at fault. Such problems are particularly prevalent in the world of computer systems, in which a datacenter with hundreds of machines, each with the same performance counters, occasionally produces overall faults. In this paper, we present a means of using a constrained form of bilinear logistic regression for diagnosis in such problems. The bilinear treatment allows us to represent the scenarios with J+K instead of JK parameters, resulting in more easily interpretable results and far fewer false positives compared to treating the parameters independently. We develop statistical tests to determine which features and entities, if any, may be responsible for the labeled faults, and use false discovery rate (FDR) analysis to ensure that our values are meaningful. We show results in comparison to ordinary logistic regression (with L1 regularization) on two scenarios: a synthetic dataset based on a model of faults in a datacenter, and a real problem of finding problematic processes/features based on user-reported hangs.
Session 2
Mining Temporal Invariants from Partially Ordered Logs
A common assumption made in log analysis research is that the underlying log is totally ordered. For concurrent systems, this assumption constrains the generated log to either exclude concurrency altogether, or to capture a particular interleaving of concurrent events. This paper argues that capturing concurrency as a partial order is useful and often indispensable for answering important questions about concurrent systems. To this end, we motivate a family of event ordering invariants over partially ordered event traces, give three algorithms for mining these invariants from logs, and evaluate their scalability on simulated distributed system logs.
Adaptive Event Prediction Strategy with Dynamic Time Window for Large-Scale HPC Systems
In this paper, we analyse messages generated by different HPC large-scale systems in order to extract sequences of correlated events which we lately use to predict the normal and faulty behaviour of the system. Our method uses a dynamic window strategy that is able to find frequent sequences of events regardless on the time delay between them. Most of the current related research narrows the correlation extraction to fixed and relatively small time windows that do not reflect the whole behaviour of the system. The generated events are in constant change during the lifetime of the machine. We consider that it is important to update the sequences at runtime by applying modifications after each prediction phase according to the forecast’s accuracy and the difference between what was expected and what really happened. Our experiments show that our analysing system is able to predict around 60% of events with a precision of around 85% at a lower event granularity than before.
Mining large distributed log data in near real time
Analyzing huge amounts of log data is often a difficult task, especially if it has to be done in real time (e.g., fraud detection) or when large amounts of stored data are required for the analysis. Graphs are a data structure often used in log analysis. Examples are clique analysis and communities of interest (COI). However, little attention has been paid to large distributed graphs that allow a high throughput of updates with very low latency.

In this paper, we present a distributed graph mining system that is able to process around 39 million log entries per second on a 50 node cluster while providing processing latencies below 10 ms. We validate our approach by presenting two example applications, namely telephony fraud detection and internet attack detection. A thorough evaluation proves the scalability and near real-time properties of our system.

Session 3
Web Analytics and the Art of Data Summarization
Web Analytics has become a critical component of many business decisions. With an ever growing number of transactions happening through web interfaces, the ability to understand and introspect web site activity is critical. In this paper, we describe the importance and intricacies of summarization for analytics and report generation on web log data. We specifically elaborate on how summarization is exposed in Splunk and discuss analytics search design trade-offs.
Panel: Assessing and improving the quality of program logs
Session 4
PAL: Propagation-aware Anomaly Localization for Cloud Hosted Distributed Applications
Distributed applications running inside cloud are prone to performance anomalies due to various reasons such as insufficient resource allocations, unexpected workload increases, or software bugs. However, those applications often consist of multiple interacting components where one component anomaly may cause its dependent components to exhibit anomalous behavior as well. It is challenging to identify the faulty components among numerous distributed application components. In this paper, we present a Propagation-aware Anomaly Localization (PAL) system that can pinpoint the source faulty components in distributed applications by extracting anomaly propagation patterns. PAL provides a robust critical change point discovery algorithm to accurately capture the onset of anomaly symptoms at different application components. We then derive the propagation pattern by sorting all critical change points in chronological order. PAL is completely application-agnostic and nonintrusive, which only relies on system-level metrics. We have implemented PAL on top of the Xen platform and tested it on a production cloud computing infrastructure using the RUBiS online auction benchmark application and the IBM System S data streaming processing application with a range of common software bugs. Our experimental results show that PAL can pinpoint faulty components in distributed applications with high accuracy and low overhead.