Abstract: Disclosed are various embodiments for processing and storing mass data, where the data may include metrics generated based on performance of an event in a monitored system. Metrics describing a state of a monitored system may be received, accessed, and aggregated to generate a data model that describes performance of the monitored system. The metrics utilized in generating the data model may be disregarded after the data model has been generated. An output describing the state of the monitored system may be generated based on the data model, and the output may be communicated over a network, for example, to a requesting service.

Abstract: Disclosed are various embodiments for processing and storing mass data, where the data may include metrics generated based on performance of an event in a monitored system. Metrics describing a state of a monitored system may be received, accessed, and aggregated to generate a data model that describes performance of the monitored system. The metrics utilized in generating the data model may be disregarded after the data model has been generated. An output describing the state of the monitored system may be generated based on the data model, and the output may be communicated over a network, for example, to a requesting service.

Abstract: Disclosed are various in various embodiments are systems and methods providing for storage of mass data such as metrics. A plurality of data models are generated in the server from a stream of metrics describing a state of a system. Each of the metrics is associated with one of a plurality of consecutive periods of time, and each data model represents the metrics associated with a corresponding one of the consecutive periods of time. The data models are stored in a data store and each of the metrics is discarded after use in generating at least one of the data models.

Abstract: Disclosed are various in various embodiments are systems and methods providing for storage of mass data such as metrics. A plurality of data models are generated in the server from a stream of metrics describing a state of a system. Each of the metrics is associated with one of a plurality of consecutive periods of time, and each data model represents the metrics associated with a corresponding one of the consecutive periods of time. The data models are stored in a data store and each of the metrics is discarded after use in generating at least one of the data models.

Abstract: Disclosed are various in various embodiments are systems and methods providing for storage of mass data such as metrics. A plurality of data models are generated in the server from a stream of metrics describing a state of a system. Each of the metrics is associated with one of a plurality of consecutive periods of time, and each data model represents the metrics associated with a corresponding one of the consecutive periods of time. The data models are stored in a data store and each of the metrics is discarded after use in generating at least one of the data models.

Abstract: Disclosed in various embodiments are systems and methods providing for storage of mass data such as metrics. A plurality of data models are generated in the server from a stream of metrics describing a state of a system. Each of the metrics is associated with one of a plurality of consecutive periods of time, and each data model represents the metrics associated with a corresponding one of the consecutive periods of time. The data models are stored in a data store and each of the metrics is discarded after use in generating at least one of the data models.

Abstract: Robust forecasting techniques are relatively immune from anomalies or outliers in observed data, such as a stream of data values reflective of the operation or use of a computer system. One robust technique provides a relatively accurate forecast of seasonal behavior even in the presence of an anomaly in corresponding historical data. Another robust forecasting technique provides a relatively accurate forecast even in the presence of an anomaly that spans multiple recent observations. In one embodiment, both techniques are used in combination to automatically detect anomalies in the operation and/or use of a multi-user computer system.

Abstract: A plurality of data models are generated in a server from a stream of metrics describing a state of at least one system. Each of the data models represents a time grouping of a subset of the metrics. One or more dimensions are associated with each of the metrics. The data models are stored in association with respective ones of the dimensions in a memory. The dimensions with which the data models are associated in the memory are increased based upon an appearance of at least one previously non-existing dimension associated with a metric in the stream.

Abstract: Robust forecasting techniques are relatively immune from anomalies or outliers in observed data, such as a stream of data values reflective of the operation or use of a computer system. One robust technique provides a relatively accurate forecast of seasonal behavior even in the presence of an anomaly in corresponding historical data. Another robust forecasting technique provides a relatively accurate forecast even in the presence of an anomaly that spans multiple recent observations. In one embodiment, both techniques are used in combination to automatically detect anomalies in the operation and/or use of a multi-user computer system.

Abstract: Robust forecasting techniques are relatively immune from anomalies or outliers in observed data, such as a stream of data values reflective of the operation or use of a computer system. One robust technique provides a relatively accurate forecast of seasonal behavior even in the presence of an anomaly in corresponding historical data. Another robust forecasting technique provides a relatively accurate forecast even in the presence of an anomaly that spans multiple recent observations. In one embodiment, both techniques are used in combination to automatically detect anomalies in the operation and/or use of a multi-user computer system.

Abstract: Robust forecasting techniques are relatively immune from anomalies or outliers in observed data, such as a stream of data values reflective of the operation or use of a computer system. One robust technique provides a relatively accurate forecast of seasonal behavior even in the presence of an anomaly in corresponding historical data. Another robust forecasting technique provides a relatively accurate forecast even in the presence of an anomaly that spans multiple recent observations. In one embodiment, both techniques are used in combination to automatically detect anomalies in the operation and/or use of a multi-user computer system.