Abstract:
Disclosed is a method and apparatus for performing capacity planning and resource optimization in a distributed system. In particular, the capacity needs of individual components (e.g., server, operating system, CPU, application software, memory, networking device, storage device, etc.) in a distributed system can±>e analyzed using relationships between measurements collected from the distributed system. These relationships, called invariants, do not change over time. From these measurements, a network of invariants are determined. The network of invariants characterize the relationships between the measurements. The capacity need of at least one component in the distributed system can be determined from the network of invariants.
Abstract:
A computer-implemented method, computer program product, and computer processing system are provided. The method includes preprocessing, by a processor, a set of heterogeneous logs by splitting each of the logs into tokens to obtain preprocessed logs. Each of the logs in the set is associated with a timestamp and textual content in one or more fields. The method further includes generating, by the processor, a set of regular expressions from the preprocessed logs. The method also includes performing, by the processor, an unsupervised parsing operation by applying the regular expressions to the preprocessed logs to obtain a set of parsed logs and a set of unparsed logs, if any. The method additionally includes storing, by the processor, the set of parsed logs in a log analytics database and the set of unparsed logs in a debugging database.
Abstract:
Methods and systems for log management include pre-processing heterogeneous logs and performing a log management action (112) on the pre-processed plurality of heterogeneous logs. Pre-processing the logs includes performing a fixed tokenization (104) of the heterogeneous logs based on a predefined set of symbols, performing a flexible tokenization (106) of the heterogeneous logs based on a user-defined set of rules, converting timestamps (108) in the heterogeneous logs to a single target timestamp format, and performing structural log tokenization (110) of the heterogeneous logs based on user-defined structural information.
Abstract:
Mobile phones and methods for mobile phone failure prediction include receiving respective log files from one or more mobile phone components, including at least one user application. The log files have heterogeneous formats. A likelihood of failure of one or more mobile phone components is determined based on the received log files by clustering the plurality of log files according to structural log patterns and determining feature representations of the log files based on the log clusters. A user is alerted to a potential failure if the likelihood of component failure exceeds a first threshold. An automatic system control action is performed if the likelihood of component failure exceeds a second threshold.
Abstract:
Methods and systems for reporting anomalous events include intra-host clustering a set of alerts based on a process graph that models states of process-level events in a network. Hidden relationship clustering is performed on the intra-host clustered alerts based on hidden relationships between alerts in respective clusters. Inter-host clustering is performed on the hidden relationship clustered alerts based on a topology graph that models source and destination relationships between connection events in the network. Inter-host clustered alerts that exceed a threshold level of trustworthiness are reported.
Abstract:
A method is provided that is performed in a network having nodes that generate heterogeneous logs including performance logs and text logs. The method includes performing, during a heterogeneous log training stage, (i) a log-to-time sequence conversion process for transforming clustered ones of training logs, from among the heterogeneous logs, into a set of time sequences that are each formed as a plurality of data pairs of a first configuration and a second configuration based on cluster type, (ii) a time series generation process for synchronizing particular ones of the time sequences in the set based on a set of criteria to output a set of fused time series, and (iii) an invariant model generation process for building invariant models for each time series data pair in the set of fused time series. The method includes controlling an anomaly-initiating one of the plurality of nodes based on the invariant models.
Abstract:
An exemplary method for detecting one or more anomalies in a system includes building a temporal causality graph describing functional relationship among local components in normal period; applying the causality graph as a propagation template to predict a system status by iteratively applying current system event signatures; and detecting the one or more anomalies of the system by examining related patterns on the template causality graph that specifies normal system behaviors. The system can aligning event patterns on the causality graph to determine an anomaly score.
Abstract:
Systems and methods are disclosed for analyzing logs generated by a machine by analyzing a log and identifying one or more abstract landmark delimiters (ALDs) representing delimiters for log tokenization; from the log and ALD, tokenizing the log and generating an increasingly tokenized format by separating the patterns with the ALD to form an intermediate tokenized log; iteratively repeating the tokenizing of the logs until a last intermediate tokenized log is processed as a final tokenized log; and applying the tokenized logs in applications.
Abstract:
Systems and methods for quality control for physical systems, including a quality control engine for transforming raw time series data collected from each of a plurality of sensors in the physical system into one or more sets of feature series by extracting features from the raw time series. Feature ranking scores are generated for each of the sensors by ranking each of the features using an ensemble of feature rankers, and fused importance scores are generated by aggregating the feature ranking scores for each of the sensors and combining ranking scores from each ranker in the ensemble. System quality is controlled by identifying sensors responsible for quality degradation based on the fused importance scores.
Abstract:
Methods and systems for process constraint include collecting system call information for a process. It is detected whether the process is idle based on the system call information and then whether the process is repeating using autocorrelation to determine whether the process issues system calls in a periodic fashion. The process is constrained if it is idle or repeating the limit an attack surface presented by the process.