Abstract: Embodiments identify heap-hoarding stack traces to optimize memory efficiency. Some embodiments can determine a length of time when heap usage by processes exceeds a threshold. Some embodiments may then determine heap information of the processes for the length of time, where the heap information comprise heap usage information for each interval in the length of time. Next, some embodiments can determine thread information of the one or more processes for the length of time, wherein determining the thread information comprises determining classes of threads and wherein the thread information comprises, for each of the classes of threads, thread intensity information for each of the intervals. Some embodiments may then correlate the heap information with the thread information to identify code that correspond to the heap usage exceeding the threshold. Some embodiments may then initiate actions associated with the code.

Abstract: Embodiments provide a thread classification method that represents stack traces in a compact form using classification signatures. Some embodiments can receive a stack trace that includes a sequence of stack frames. Some embodiments may generate, based on the sequence of stack frames, a trace signature that represents the set. Some embodiments may receive one or more subsequent stack traces. For each of the one or more subsequent stack traces, some embodiments may determine whether a subsequent trace signature has been generated to represent the sequence of stack frames included within the subsequent stack trace. If not, some embodiments may generate, based on the trace signature and other subsequent trace signatures that were generated based on the trace signature, the subsequent trace signature to represent the subsequent sequence of stack frames.

Abstract: Embodiments of the invention provide systems and methods for managing and processing large amounts of complex and high-velocity data by capturing and extracting high-value data from low value data using big data and related technologies. Illustrative database systems described herein may collect and process data while extracting or generating high-value data. The high-value data may be handled by databases providing functions such as multi-temporality, provenance, flashback, and registered queries. In some examples, computing models and system may be implemented to combine knowledge and process management aspects with the near real-time data processing frameworks in a data-driven situation aware computing system.

Abstract: Embodiments provide techniques for estimating seasonal indices for multiple periods. Some embodiments can receive a signal comprising a plurality of measures sampled over a span of time from an environment in which one or more processes are being executed. Some embodiments may then extract a seasonal effector and a de-seasonalized component from the signal. Next, some embodiments can apply one or more spline functions to the seasonal effector to generate a first model. Some embodiments may then apply a linear regression technique to the de-seasonalized component to generate a second model. Some embodiments may then initiate actions associated with the code. Some embodiments may then generate a forecast of the signal based on the first model and the second model. Next, some embodiments may initiate, based at least in part on the forecast, one or more actions associated with the environment.

Abstract: Embodiments provide techniques for estimating seasonal indices for multiple periods. Some embodiments can receive a signal comprising a plurality of measures sampled over a span of time from an environment in which one or more processes are being executed. Some embodiments may then extract a seasonal effector and a de-seasonalized component from the signal. Next, some embodiments can apply one or more spline functions to the seasonal effector to generate a first model. Some embodiments may then apply a linear regression technique to the de-seasonalized component to generate a second model. Some embodiments may then initiate actions associated with the code. Some embodiments may then generate a forecast of the signal based on the first model and the second model. Next, some embodiments may initiate, based at least in part on the forecast, one or more actions associated with the environment.

Abstract: Embodiments identify heap-hoarding stack traces to optimize memory efficiency. Some embodiments can determine a length of time when heap usage by processes exceeds a threshold. Some embodiments may then determine heap information of the processes for the length of time, where the heap information comprise heap usage information for each interval in the length of time. Next, some embodiments can determine thread information of the one or more processes for the length of time, wherein determining the thread information comprises determining classes of threads and wherein the thread information comprises, for each of the classes of threads, thread intensity information for each of the intervals. Some embodiments may then correlate the heap information with the thread information to identify code that correspond to the heap usage exceeding the threshold. Some embodiments may then initiate actions associated with the code.

Abstract: Embodiments provide a thread classification method that represents stack traces in a compact form using classification signatures. Some embodiments can receive a stack trace that includes a sequence of stack frames. Some embodiments may generate, based on the sequence of stack frames, a trace signature that represents the set. Some embodiments may receive one or more subsequent stack traces. For each of the one or more subsequent stack traces, some embodiments may determine whether a subsequent trace signature has been generated to represent the sequence of stack frames included within the subsequent stack trace. If not, some embodiments may generate, based on the trace signature and other subsequent trace signatures that were generated based on the trace signature, the subsequent trace signature to represent the subsequent sequence of stack frames.

Abstract: Embodiments identify heap-hoarding stack traces to optimize memory efficiency. Some embodiments can determine a length of time when heap usage by processes exceeds a threshold. Some embodiments may then determine heap information of the processes for the length of time, where the heap information comprise heap usage information for each interval in the length of time. Next, some embodiments can determine thread information of the one or more processes for the length of time, wherein determining the thread information comprises determining classes of threads and wherein the thread information comprises, for each of the classes of threads, thread intensity information for each of the intervals. Some embodiments may then correlate the heap information with the thread information to identify code that correspond to the heap usage exceeding the threshold. Some embodiments may then initiate actions associated with the code.

Abstract: Embodiments provide a thread classification method that represents stack traces in a compact form using classification signatures. Some embodiments can receive a stack trace that includes a sequence of stack frames. Some embodiments may generate, based on the sequence of stack frames, a trace signature that represents the set. Some embodiments may receive one or more subsequent stack traces. For each of the one or more subsequent stack traces, some embodiments may determine whether a subsequent trace signature has been generated to represent the sequence of stack frames included within the subsequent stack trace. If not, some embodiments may generate, based on the trace signature and other subsequent trace signatures that were generated based on the trace signature, the subsequent trace signature to represent the subsequent sequence of stack frames.

Abstract: Embodiments of the invention provide systems and methods for managing and processing large amounts of complex and high-velocity data by capturing and extracting high-value data from low value data using big data and related technologies. Illustrative database systems described herein may collect and process data while extracting or generating high-value data. The high-value data may be handled by databases providing functions such as multi-temporality, provenance, flashback, and registered queries. In some examples, computing models and system may be implemented to combine knowledge and process management aspects with the near real-time data processing frameworks in a data-driven situation aware computing system.

Abstract: Embodiments of the invention provide systems and methods for managing and processing large amounts of complex and high-velocity data by capturing and extracting high-value data from low value data using big data and related technologies. Illustrative database systems described herein may collect and process data while extracting or generating high-value data. The high-value data may be handled by databases providing functions such as multi-temporality, provenance, flashback, and registered queries. In some examples, computing models and system may be implemented to combine knowledge and process management aspects with the near real-time data processing frameworks in a data-driven situation aware computing system.

Abstract: The disclosed embodiments relate to a system for analyzing telemetry data. During operation, the system obtains telemetry data gathered from sensors during operation of a monitored system. Next, the system applies a univariate model to the telemetry data to identify an operational phase for the monitored system, wherein the univariate model analyzes an individual signal in the telemetry data without reference to other signals in the telemetry data. The system then selects a phase-specific multivariate model based on the identified operational phase, wherein the phase-specific multivariate model was previously trained based on telemetry data gathered while the system was operating in the identified operational phase. Finally, the system uses the phase-specific multivariate model to monitor the telemetry data to detect incipient anomalies associated with the operation of the monitored system.

Abstract: Embodiments identify heap-hoarding stack traces to optimize memory efficiency. Some embodiments can determine a length of time when heap usage by processes exceeds a threshold. Some embodiments may then determine heap information of the processes for the length of time, where the heap information comprise heap usage information for each interval in the length of time. Next, some embodiments can determine thread information of the one or more processes for the length of time, wherein determining the thread information comprises determining classes of threads and wherein the thread information comprises, for each of the classes of threads, thread intensity information for each of the intervals. Some embodiments may then correlate the heap information with the thread information to identify code that correspond to the heap usage exceeding the threshold. Some embodiments may then initiate actions associated with the code.

Abstract: Embodiments provide a thread classification method that represents stack traces in a compact form using classification signatures. Some embodiments can receive a stack trace that includes a sequence of stack frames. Some embodiments may generate, based on the sequence of stack frames, a trace signature that represents the set. Some embodiments may receive one or more subsequent stack traces. For each of the one or more subsequent stack traces, some embodiments may determine whether a subsequent trace signature has been generated to represent the sequence of stack frames included within the subsequent stack trace. If not, some embodiments may generate, based on the trace signature and other subsequent trace signatures that were generated based on the trace signature, the subsequent trace signature to represent the subsequent sequence of stack frames.

Abstract: Embodiments identify heap-hoarding stack traces to optimize memory efficiency. Some embodiments can determine a length of time when heap usage by processes exceeds a threshold. Some embodiments may then determine heap information of the processes for the length of time, where the heap information comprise heap usage information for each interval in the length of time. Next, some embodiments can determine thread information of the one or more processes for the length of time, wherein determining the thread information comprises determining classes of threads and wherein the thread information comprises, for each of the classes of threads, thread intensity information for each of the intervals. Some embodiments may then correlate the heap information with the thread information to identify code that correspond to the heap usage exceeding the threshold. Some embodiments may then initiate actions associated with the code.

Abstract: Embodiments provide a thread classification method that represents stack traces in a compact form using classification signatures. Some embodiments can receive a stack trace that includes a sequence of stack frames. Some embodiments may generate, based on the sequence of stack frames, a trace signature that represents the set. Some embodiments may receive one or more subsequent stack traces. For each of the one or more subsequent stack traces, some embodiments may determine whether a subsequent trace signature has been generated to represent the sequence of stack frames included within the subsequent stack trace. If not, some embodiments may generate, based on the trace signature and other subsequent trace signatures that were generated based on the trace signature, the subsequent trace signature to represent the subsequent sequence of stack frames.

Abstract: Methods, systems and computer readable medium are provided for sequentially analyzing a series of thread dump samples to estimate the intensity statistic of newly classified stack segments of stack frames. According to one embodiment, a branch point along one or more linearly connected stack frames of a stack segment can be detected, where the stack segment is associated with one or more thread intensity statistic parameters. Upon detecting the branch point along the one or more linearly connected stack frames of the stack segment, the system can split the stack segment into a plurality of new stack segments that each include a subset of the stack frames, where the plurality of new stack segments are referenced by the stack segment. The system can then initialize the one or more thread intensity statistic parameters for each of the new stack segments.

Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems.

Abstract: The disclosed embodiments provide a system that detects anomalous events in a virtual machine. During operation, the system obtains time-series garbage-collection (GC) data collected during execution of a virtual machine in a computer system. Next, the system generates one or more seasonal features from the time-series GC data. The system then uses a sequential-analysis technique to analyze the time-series GC data and the one or more seasonal features for an anomaly in the GC activity of the virtual machine. Finally, the system stores an indication of a potential out-of-memory (OOM) event for the virtual machine based at least in part on identifying the anomaly in the GC activity of the virtual machine.

Abstract: Data can be categorized into facts, information, hypothesis, and directives. Activities that generate certain categories of data based on other categories of data through the application of knowledge which can be categorized into classifications, assessments, resolutions, and enactments. Activities can be driven by a Classification-Assessment-Resolution-Enactment (CARE) control engine. The CARE control and these categorizations can be used to enhance a multitude of systems, for example diagnostic system, such as through historical record keeping, machine learning, and automation. Such a diagnostic system can include a system that forecasts computing system failures based on the application of knowledge to system vital signs such as thread or stack segment intensity and memory heap usage. These vital signs are facts that can be classified to produce information such as memory leaks, convoy effects, or other problems.