Abstract: Methods and systems that manage large volumes of metric data generation by cloud-computing infrastructures are described. The cloud-computing infrastructure generates sets of metric data, each set of metric data may represent usage or performance of an application or application module run by the cloud-computing infrastructure or may represent use or performance of cloud-computing resources used by the applications. The metric data management methods and systems are composed of separate modules that perform sequential application of metric data reduction techniques on different levels of data abstraction in order to reduce volume of metric data collected. In particular, the modules determine normalcy bounds, delete highly correlated metric data, and delete metric data with highly correlated normalcy bound violations.

Abstract: The current document is directed to methods and subsystems within computing systems, including distributed computing systems, that collect, store, process, and analyze population metrics for types and classes of system components, including components of distributed applications executing within containers, virtual machines, and other execution environments. In a described implementation, a graph-like representation of the configuration and state of a computer system included aggregation nodes that collect metric data for a set of multiple object nodes and that collect metric data that represents the members of the set over a monitoring time interval. Population metrics are monitored, in certain implementations, to detect outlier members of an aggregation.

Abstract: Methods and systems to evaluate data center performance and prioritize data center objects and anomalies for remedial actions are described. Methods rank data center objects and determine object performance trends. Methods calculate an object rank of each object of the data center over a period of time and calculate an object trend of each object of the data center based on relative frequencies of alerts at different times. The objects may be prioritized for remedial actions based on the object ranks and object trends.

Abstract: Methods and systems to evaluate importance of metrics generated in a data center and ranking metric in order of relevance to data center performance are described. Methods collect sets of metric data generated in a data center over a period of time and categorize each set of metric data as being of high importance, medium importance, or low importance. Methods also calculate a rank ordering of each set of high importance and medium importance metric data. By determining importance of data center metrics, an optimal usage and distribution of computational and storage resources of the data center may be determined.

Abstract: Methods determine a capacity-forecast model based on historical capacity metric data and historical business metric data. The capacity-forecast model may be to estimate capacity requirements with respect to changes in demand for the data center customer's application program. The capacity-forecast model provides an analytical “what-if” approach to reallocating data center resources in order to satisfy projected business level expectations of a data center customer and calculate estimated capacities for different business scenarios.

Abstract: Methods recommend to data center customers those attributes of a data center infrastructure and application program that are associated with service-level objective (“SLO”) metric degradation and may be recorded in problem definitions. In other words, a data center customer is offered to “codify” problems primarily with atomic abnormality conditions on indicated attributes that decrease the SLO by some degree that the data center customer would like to be aware. As a result, the data center customer is warned of potentially significant SLO decline in order to prevent unwanted loss and take any necessary actions to prevent active anomalies. Methods also generate patterns of attributes that constitute core structures highly associated with degradation of the SLO metric.

Abstract: A problem in a cloud infrastructure may be identified when a server computer deviates from a normal level of operation based on anomaly scores, which generates an alert and an alert time that indicates when the alert is generated. Methods then determine which virtual machine (“VM”) and other IT objects/resources or their pools contribute to the problem within a time window surrounding the estimated problem start time and calculate which objects show similar, related anomalous behavior. Method also generate ranked remediation recommendations on an object level and server computer-to-object level. The methods generate results that enable a system administrator to identify the start time of the problem and identify the objects that are responsible for the problem.

Abstract: This disclosure is directed to data-agnostic computational methods and systems for adjusting hard thresholds based on user feedback. Hard thresholds are used to monitor time-series data generated by a data-generating entity. The time-series data may be metric data that represents usage of the data-generating entity over time. The data is compared with a hard threshold associated with usage of the resource or process and when the data violates the threshold, an alert is typically generated and presented to a user. Methods and systems collect user feedback after a number of alerts to determine the quality and significance of the alerts. Based on the user feedback, methods and systems automatically adjust the hard thresholds to better represent how the user perceives the alerts.

Abstract: The current document is directed to a multi-stage metric-data compression method and subsystem for compressing metric data collected and stored within distributed computing systems to facilitate computer-system management and administration. In a described implementation, metric data is partitioned into constant metric data, low-variability metric data, and high-variability metric data. High-variability metric data is compressed by identifying a set of basis metrics, or independent metrics, with respect to which a remaining set of dependent metrics can be expressed using coefficient multipliers. The high-variability metric data can then be stored as a set of independent metrics and set of coefficients, along with a small amount of additional data.

Abstract: This disclosure is directed to data-agnostic computational methods and systems for adjusting hard thresholds based on user feedback. Hard thresholds are used to monitor time-series data generated by a data-generating entity. The time-series data may be metric data that represents usage of the data-generating entity over time. The data is compared with a hard threshold associated with usage of the resource or process and when the data violates the threshold, an alert is typically generated and presented to a user. Methods and systems collect user feedback after a number of alerts to determine the quality and significance of the alerts. Based on the user feedback, methods and systems automatically adjust the hard thresholds to better represent how the user perceives the alerts.

Abstract: Methods and systems that estimate a degree of abnormality of a complex system based on historical time-series data representative of the complex system's past behavior and using the historical degree of abnormality to determine whether or not a degree of abnormality determined from current time-series data representative of the same complex system's current behavior is worthy of attention. The time-series data may be metric data that represents behavior of a complex system as a result of successive measurements of the complex system made over time or in a time interval. A degree of abnormality represents the amount by which the time-series data violates a threshold. The larger the degree of abnormality of the current time-series data is from the historical degree of abnormality, the larger the violation of the thresholds and the greater the probability the violation in the current time-series data is worthy of attention.

Abstract: Methods and systems that manage large volumes of metric data generation by cloud-computing infrastructures are described. The cloud-computing infrastructure generates sets of metric data, each set of metric data may represent usage or performance of an application or application module run by the cloud-computing infrastructure or may represent use or performance of cloud-computing resources used by the applications. The metric data management methods and systems are composed of separate modules that perform sequential application of metric data reduction techniques on different levels of data abstraction in order to reduce volume of metric data collected. In particular, the modules determine normalcy bounds, delete highly correlated metric data, and delete metric data with highly correlated normalcy bound violations.

Abstract: This disclosure is directed to computational, closed-loop user feedback systems and methods for ranking or updating beliefs for a user based on user feedback. The systems and methods are based on a data-agnostic user feedback formulation that uses user feedback to automatically rank beliefs for a user or update the beliefs. The methods and systems are based on a general statistical inference model, which, in turn, is based on an assumption of convergence in user opinion. The closed-loop user feedback methods and systems may be used to rank or update beliefs prior to inputting the beliefs to a recommender engine. As a result, the recommender engine is expected to be more responsive to customer environments and efficient at deployment and reducing the level of unnecessary user recommendations.

Abstract: This disclosure is directed to automated methods and systems for calculating hard thresholds used to monitor time-series data generated by data-generating entity. The methods are based on determining a cumulative distribution that characterizes the probability that data values of time-series data generated by the data-generating entity violate a hard threshold. The hard threshold is calculated as an inverse of the cumulative distribution based on a user defined risk confidence level. The hard threshold may then be used to generate alerts when time-series data generated later by the data-generating entity violate the hard threshold.

Abstract: This disclosure is directed to data-agnostic computational methods and systems for adjusting hard thresholds based on user feedback. Hard thresholds are used to monitor time-series data generated by a data-generating entity. The time-series data may be metric data that represents usage of the data-generating entity over time. The data is compared with a hard threshold associated with usage of the resource or process and when the data violates the threshold, an alert is typically generated and presented to a user. Methods and systems collect user feedback after a number of alerts to determine the quality and significance of the alerts. Based on the user feedback, methods and systems automatically adjust the hard thresholds to better represent how the user perceives the alerts.

Abstract: This disclosure presents computational systems and methods for detecting anomalies in data output from any type of monitoring tool. The data is aggregated and sent to an alerting system for abnormality detection via comparison with normalcy bounds. The anomaly detection methods are performed by construction of normalcy bounds of the data based on the past behavior of the data output from the monitoring tool. The methods use data quality assurance and data categorization processes that allow choosing a correct procedure for determination of the normalcy bounds. The methods are completely data agnostic, and as a result, can also be used to detect abnormalities in time series data associated with any complex system.