Abstract: Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments of a software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.

Abstract: Techniques for machine-learning of long-term seasonal patterns are disclosed. In some embodiments, a network service receives a set of time-series data that tracks metric values of at least one computing resource over time. Responsive to receiving the time-series data, the network service detects a subset of metric values that are outliers and associated with a plurality of timestamps. The network service maps the plurality of timestamps to one or more encodings of at least one encoding space that defines a plurality of encodings for different seasonal patterns. Based on the mapped encodings, the network service generates a representation of a seasonal pattern. Based on the representation of the seasonal pattern, the network service may perform one or more operations in association with the at least one computing resource.

Abstract: Techniques are disclosed for summarizing, diagnosing, and correcting the cause of anomalous behavior in computing systems. In some embodiments, a system identifies a plurality of time series that track different metrics over time for a set of one or more computing resources. The system detects a first set of anomalies in a first time series that tracks a first metric and assigns a different respective range of time to each anomaly. The system determines whether the respective range of time assigned to an anomaly overlaps with timestamps or ranges of time associated with anomalies from one or more other time series. The system generates at least one cluster that groups metrics based on how many anomalies have respective ranges of time and/or timestamps that overlap. The system may preform, based on the cluster, one or more automated actions for diagnosing or correcting a cause of anomalous behavior.

Abstract: Techniques are described for automatically detecting and accommodating state changes in a computer-generated forecast. In one or more embodiments, a representation of a time-series signal is generated within volatile and/or non-volatile storage of a computing device. The representation may be generated in such a way as to approximate the behavior of the time-series signal across one or more seasonal periods. Once generated, a set of one or more state changes within the representation of the time-series signal is identified. Based at least in part on at least one state change in the set of one or more state changes, a subset of values from the sequence of values is selected to train a model. An analytical output is then generated, within volatile and/or non-volatile storage of the computing device, using the trained model.

Abstract: Techniques for predictive system remediation are disclosed. Based on attributes associated with applications of one or more system-selected remedial actions to one or more problematic system behaviors in a system (e.g., a database system), the system determines a predicted effectiveness of one or more future applications of a remedial action to a particular problematic system behavior, as of one or more future times. The system determines that the predicted effectiveness of the one or more future applications of the remedial action is positive but does not satisfy a performance criterion. Responsive to determining that the predicted effectiveness is positive but does not satisfy the performance criterion, the system generates a notification corresponding to the predicted effectiveness not satisfying the performance criterion.

Abstract: Generating persistent multifaceted statistical distributions of event data associated with computing nodes is disclosed. From a data stream, events are identified that occur during a first time interval. Characteristics associated with the events are determined. Based on a primary characteristic, it is determined that an event corresponds to an event cluster. The event count for that cluster is incremented. It is determined that the characteristics correspond to an event descriptor of events in the cluster. Responsive to requests to view the event cluster, information about descriptors from the cluster are displayed indicating events having a particular event descriptor, or a summary of characteristics that distinguish the descriptor from other event descriptors.

Abstract: Techniques are described for generating seasonal forecasts. According to an embodiment, a set of time-series data is associated with one or more classes, which may include a first class that represent a dense pattern that repeats over multiple instances of a season in the set of time-series data and a second class that represent another pattern that repeats over multiple instances of the season in the set of time-series data. A particular class of data is associated with at least two sub-classes of data, where a first sub-class represents high data points from the first class, and a second sub-class represents another set of data points from the first class. A trend rate is determined for a particular sub-class. Based at least in part on the trend rate, a forecast is generated.

Abstract: Techniques are disclosed for summarizing, diagnosing, and correcting the cause of anomalous behavior in computing systems. In some embodiments, a system identifies a plurality of time series that track different metrics over time for a set of one or more computing resources. The system detects a first set of anomalies in a first time series that tracks a first metric and assigns a different respective range of time to each anomaly. The system determines whether the respective range of time assigned to an anomaly overlaps with timestamps or ranges of time associated with anomalies from one or more other time series. The system generates at least one cluster that groups metrics based on how many anomalies have respective ranges of time and/or timestamps that overlap. The system may preform, based on the cluster, one or more automated actions for diagnosing or correcting a cause of anomalous behavior.

Abstract: Systems and methods for unsupervised training and evaluation of anomaly detection models are described. In some embodiments, an unsupervised process comprises generating an approximation of a data distribution for a training dataset including varying values for a metric of a computing resource. The process further determines, based on the size of the training dataset, a first quantile probability and a second quantile probability that represent an interval for covering a prescribed proportion of values for the metric within a prescribed confidence level. The process further trains a lower limit of the anomaly detection model using a first quantile that represents the first quantile probability in the approximation of the data distribution and an upper limit using a second quantile that represents the second quantile probability in the approximation. The trained upper and lower limits may be used to monitor input data for anomalous behavior and, if detected, trigger responsive action(s).

Abstract: Techniques are disclosed for summarizing, diagnosing, and correcting the cause of anomalous behavior in computing systems. In some embodiments, a system identifies a plurality of time series that track different metrics over time for a set of one or more computing resources. The system detects a first set of anomalies in a first time series that tracks a first metric and assigns a different respective range of time to each anomaly. The system determines whether the respective range of time assigned to an anomaly overlaps with timestamps or ranges of time associated with anomalies from one or more other time series. The system generates at least one cluster that groups metrics based on how many anomalies have respective ranges of time and/or timestamps that overlap. The system may preform, based on the cluster, one or more automated actions for diagnosing or correcting a cause of anomalous behavior.

Abstract: Techniques for predictive system remediation are disclosed. Based on attributes associated with applications of one or more system-selected remedial actions to one or more problematic system behaviors in a system (e.g., a database system), the system determines a predicted effectiveness of one or more future applications of a remedial action to a particular problematic system behavior, as of one or more future times. The system determines that the predicted effectiveness of the one or more future applications of the remedial action is positive but does not satisfy a performance criterion. Responsive to determining that the predicted effectiveness is positive but does not satisfy the performance criterion, the system generates a notification corresponding to the predicted effectiveness not satisfying the performance criterion.

Abstract: Techniques for predictive system remediation are disclosed. Based on attributes associated with applications of one or more system-selected remedial actions to one or more problematic system behaviors in a system (e.g., a database system), the system determines a predicted effectiveness of one or more future applications of a remedial action to a particular problematic system behavior, as of one or more future times. The system determines that the predicted effectiveness of the one or more future applications of the remedial action is positive but does not satisfy a performance criterion. Responsive to determining that the predicted effectiveness is positive but does not satisfy the performance criterion, the system generates a notification corresponding to the predicted effectiveness not satisfying the performance criterion.

Abstract: Systems and methods for trending patterns within a set of time-series data are described. In one or more embodiments, a set of one or more groups of data points that are associated with a particular seasonal pattern are generated within volatile and/or non-volatile storage. A set of pairwise slopes is determined for data point pairs within the set of one or more groups of data points. Based, at least in part on the plurality of pairwise slopes, a representative trend rate for the particular seasonal pattern is determined. A set of forecasted values is then generated within volatile or non-volatile storage based, at least in part, on the representative trend rate for the particular seasonal pattern.

Abstract: Generating persistent multifaceted statistical distributions of event data associated with computing nodes is disclosed. From a data stream, events are identified that occur during a first time interval. Characteristics associated with the events are determined. Based on a primary characteristic, it is determined that an event corresponds to an event cluster. The event count for that cluster is incremented. It is determined that the characteristics correspond to an event descriptor of events in the cluster. Responsive to requests to view the event cluster, information about descriptors from the cluster are displayed indicating events having a particular event descriptor, or a summary of characteristics that distinguish the descriptor from other event descriptors.

Abstract: Systems and methods for performing unsupervised baselining and anomaly detection using time-series data are described. In one or more embodiments, a baselining and anomaly detection system receives a set of time-series data. Based on the set of time-series, the system generates a first interval that represents a first distribution of sample values associated with the first seasonal pattern and a second interval that represents a second distribution of sample values associated with the second seasonal pattern. The system then monitors a time-series signals using the first interval during a first time period and the second interval during a second time period. In response to detecting an anomaly in the first seasonal pattern or the second seasonal pattern, the system performs a responsive action, such as generating an alert.

Abstract: Techniques for training and evaluating seasonal forecasting models are disclosed. In some embodiments, a network service generates, in memory, a set of data structures that separate sample values by season type and season space. The set of data structures may include a first set of clusters corresponding to different season types in the first season space and a second set of clusters corresponding to different season types in the second season space. The network service merges two or more clusters the first set and/or second set of clusters. Clusters from the first set are not merged with clusters from the second set. After merging the clusters, the network service determines a trend pattern for each of the remaining clusters in the first and second set of clusters. The network service then generates a forecast for a metric of a computing resource based on the trend patterns for each remaining cluster.

Abstract: Generating persistent multifaceted statistical distributions of event data associated with computing nodes is disclosed. From a data stream, events are identified that occur during a first time interval. Characteristics associated with the events are determined. Based on a primary characteristic, it is determined that an event corresponds to an event cluster. The event count for that cluster is incremented. It is determined that the characteristics correspond to an event descriptor of events in the cluster. Responsive to requests to view the event cluster, information about descriptors from the cluster are displayed indicating events having a particular event descriptor, or a summary of characteristics that distinguish the descriptor from other event descriptors.

Abstract: Techniques for artificial intelligence driven configuration management are described herein. In some embodiments, a machine-learning process determines a feature set for a plurality of deployments of a software resource. Based on varying values in the feature set, the process clusters each of the plurality of deployments into a cluster of a plurality of clusters. Each cluster of the plurality of clusters comprises one or more nodes and each node of the one or more nodes corresponds to at least a subset of values of the feature set that are detected in at least one deployment of the plurality of deployments of the software resource. The process determines a representative node for each cluster of the plurality of clusters. An operation may be performed based on the representative node for at least one cluster.

Abstract: Systems and methods for trending patterns within a set of time-series data are described. In one or more embodiments, a set of one or more groups of data points that are associated with a particular seasonal pattern are generated within volatile and/or non-volatile storage. A set of pairwise slopes is determined for data point pairs within the set of one or more groups of data points. Based, at least in part on the plurality of pairwise slopes, a representative trend rate for the particular seasonal pattern is determined. A set of forecasted values is then generated within volatile or non-volatile storage based, at least in part, on the representative trend rate for the particular seasonal pattern.

Abstract: Systems and methods for performing unsupervised baselining and anomaly detection using time-series data are described. In one or more embodiments, a baselining and anomaly detection system receives a set of time-series data. Based on the set of time-series, the system generates a first interval that represents a first distribution of sample values associated with the first seasonal pattern and a second interval that represents a second distribution of sample values associated with the second seasonal pattern. The system then monitors a time-series signals using the first interval during a first time period and the second interval during a second time period. In response to detecting an anomaly in the first seasonal pattern or the second seasonal pattern, the system performs a responsive action, such as generating an alert.