Abstract:
Methods and systems for log management include pre-processing heterogeneous logs and performing a log management action on the pre-processed plurality of heterogeneous logs. Pre-processing the logs includes performing a fixed tokenization of the heterogeneous logs based on a predefined set of symbols, performing a flexible tokenization of the heterogeneous logs based on a user-defined set of rules, converting timestamps in the heterogeneous logs to a single target timestamp format, and performing structural log tokenization of the heterogeneous logs based on user-defined structural information.
Abstract:
Methods for system failure prediction include clustering log files according to structural log patterns. Feature representations of the log files are determined based on the log clusters. A likelihood of a system failure is determined based on the feature representations using a neural network. An automatic system control action is performed if the likelihood of system failure exceeds a threshold.
Abstract:
The present invention enables capturing API level calls using a combination of dynamic instrumentation and library overriding. The invention allows event level tracing of API function calls and returns, and is able to generate an execution trace. The instrumentation is lightweight and relies on dynamic library/shared library linking mechanisms in most operating systems. Hence we need no source code modification or binary injection. The tool can be used to capture parameter values, and return values, which can be used to correlate traces across API function calls to generate transaction flow logic.
Abstract:
Systems and methods are disclosed for detecting error in a cloud infrastructure by running a plurality of training tasks on the cloud infrastructure and generating training execution logs; generating a model miner with the training execution logs to represent one or more correct task executions in the cloud infrastructure; after training, running a plurality of tasks on the cloud infrastructure and capturing live execution logs; and from the live execution logs, if a current task deviates from the correct task execution, indicating an execution error for correction in real-time.
Abstract:
A computer implemented method for maintaining a program's calling context correct even when a monitoring of the program goes out of a scope of a program analysis by validating function call transitions and recovering partial paths before and after the violation of the program's control flow. The method includes detecting a violation of control flow invariants in the software system including validating a source and destination of a function call in the software system, interpreting a pre-violation partial path responsive to a failure of the validating, and interpreting a post violation path after a violation of program flow.
Abstract:
A computer implemented method provides efficient monitoring and analysis of a program's memory objects in the operation stage. The invention can visualize and analyze a monitored program's data status with improved semantic information without requiring source code at runtime. The invention can provide higher quality of system management, performance debugging, and root-cause error analysis of enterprise software in the production stage.
Abstract:
Systems and methods for enabling automated log analysis with controllable resource requirements are provided. A training set for log pattern learning is generated based on heterogeneous logs generated by a computer system. An incremental learning process is implemented to generate a set of log patterns from the training set. The heterogeneous logs are parsed using the set of log patterns. A set of applications is applied to the parsed logs.
Abstract:
Systems and methods for automatically generating a set of meta-parameters used to train invariant-based anomaly detectors are provided. Data is transformed into a first set of time series data and a second set of time series data. A fitness threshold search is performed on the first set of time series data to automatically generate a fitness threshold, and a time resolution search is performed on the set of second time series data to automatically generate a time resolution. A set of meta-parameters including the fitness threshold and the time resolution are sent to one or more user devices across a network to govern the training of an invariant-based anomaly detector.
Abstract:
A computer-implemented method, system, and computer program product are provided for content aware heterogeneous log pattern comparative analysis. The method includes receiving, by a processor-device, a plurality of heterogeneous logs. The method also includes extracting, by the processor-device, a plurality of log syntactic patterns from the plurality of heterogenous logs. The method additionally includes generating, by the processor-device, latent representation vectors for each of the plurality of log syntactic patterns. The method further includes predicting, by the processor-device, an anomaly from the clustered latent representation vectors. The method also includes controlling an operation of a processor-based machine to react in accordance with the anomaly.
Abstract:
Methods and systems for log management include pre-processing heterogeneous logs and performing a log management action on the pre-processed plurality of heterogeneous logs. Pre-processing the logs includes performing a fixed tokenization of the heterogeneous logs based on a predefined set of symbols, performing a flexible tokenization of the heterogeneous logs based on a user-defined set of rules, converting timestamps in the heterogeneous logs to a single target timestamp format, and performing structural log tokenization of the heterogeneous logs based on user-defined structural information.