Abstract:
A system for automatically instrumenting and tracing an application program and related software components achieves a correlated tracing of the program execution. It includes tracing of endpoints that are the set of functions in the program execution path that the developers are interested. The tracing endpoints and related events become the total set of functions to be traced in the program (called instrument points). This invention automatically analyzes the program and generates such instrumentation points to enable correlated tracing. The generated set of instrumentation points addresses common questions that developers ask when they use monitoring tools.
Abstract:
A method includes generating a normal trace in a training stage for the monitored software systems and a monitored trace in the deployment stage for anomaly detection, applying resource transfer functions to traces to convert them to resource features, and system call categorization to traces to convert them to program behavior features, performing anomaly detection in a global scope using the derived resource features and program behavior features, in case the system finds no anomaly, generating no anomaly report, in case the anomaly is found, including the result in an anomaly report; and performing conditional anomaly detection.
Abstract:
This invention provides a new mechanism for “Hot-Tracing” using a novel placeholder mechanism and binary rewriting techniques, which leverages existing compiler flags in order to enable light-weight and highly flexible dynamic instrumentation. Broadly, I-Probe can be divided in 2 distinct workflows—1. Pre-processing (ColdPatch), and 2. Hot Tracing. The first phase is a pre-processing mechanism to prepare the binary for phase 2. The second phase is the actual hot-tracing mechanism, which allows users to dynamically instrument functions (more specifically symbols) of their choice.
Abstract:
A method includes generating a normal trace in a training stage for the monitored software systems and a monitored trace in the deployment stage for anomaly detection, applying resource transfer functions to traces to convert them to resource features, and system call categorization to traces to convert them to program behavior features, performing anomaly detection in a global scope using the derived resource features and program behavior features, in case the system finds no anomaly, generating no anomaly report, in case the anomaly is found, including the result in an anomaly report; and performing conditional anomaly detection.
Abstract:
Systems and methods are disclosed for parsing logs from arbitrary or unknown systems or applications by capturing heterogeneous logs from the arbitrary or unknown systems or applications; generating one pattern for every unique log message; building a pattern hierarchy tree by grouping patterns based on similarity metrics, and for every group it generates one pattern by combing all constituting patterns of that group; and selecting a set of patterns from the pattern hierarchy tree.
Abstract:
A heterogeneous log pattern editing recommendation system and computer-implemented method are provided. The system has a processor configured to identify, from heterogeneous logs, patterns including variable fields and constant fields. The processor is also configured to extract a category feature, a cardinality feature, and a before-after n-gram feature by tokenizing the variable fields in the identified patterns. The processor is additionally configured to generate target similarity scores between target fields to be potentially edited and other fields from among the variable fields in the heterogeneous logs using pattern editing operations based on the extracted category feature, the extracted cardinality feature, and the extracted before-after n-gram feature. The processor is further configured to recommend, to a user, log pattern edits for at least one of the target fields based on the target similarity scores between the target fields in the heterogeneous logs.
Abstract:
Systems and methods for automatically generating failure signatures in a computer system for performing computer system fault diagnosis are provided. The method includes receiving log data, converting each log in the log data into a collection of log pattern sequences including one or more log pattern sequences corresponding to one or more respective failure categories associated with the computer system, generating a collection of seed patterns by computing a global set of patterns from the collection of log pattern sequences, and extracting the collection of seed patterns from the global set of patterns, generating a log pattern grammar representation for each of the one or more log pattern sequences, generating a failure signature for each of the one or more failure categories based on the log pattern grammar representation and the collection of seed patterns, and employing the failure signatures to perform computer system fault diagnosis on new log data.
Abstract:
A method is provided that includes transforming training data into a neural network based learning model using a set of temporal graphs derived from the training data. The method includes performing model learning on the learning model by automatically adjusting learning model parameters based on the set of the temporal graphs to minimize differences between a predetermined ground-truth ranking list and a learning model output ranking list. The method includes transforming testing data into a neural network based inference model using another set of temporal graphs derived from the testing data. The method includes performing model inference by applying the inference and learning models to test data to extract context features for alerts in the test data and calculate a ranking list for the alerts based on the extracted context features. Top-ranked alerts are identified as critical alerts. Each alert represents an anomaly in the test data.
Abstract:
A computer-implemented method for generating patterns from a set of heterogeneous log messages is presented. The method includes collecting the set of heterogenous log messages from arbitrary or unknown systems or applications or sensors or instruments, splitting the log messages into tokens based on a set of delimiters, identifying datatypes of the tokens, identifying a log structure of the log messages by generating pattern-signatures of all the tokens and the datatypes based on predefined pattern settings, generating a pattern for each of the log structures and enabling users to edit the pattern for each of the log structures based on user requirements.
Abstract:
Systems and methods for contextual event sequence analysis of system failure that analyzes heterogeneous system event record logs are disclosed. The disclosure relates to analyzing event sequences for system failure in ICT and other computerized systems and determining their causes and propagation chains.