Abstract: An application performance analyzer adapted to analyze the performance of one or more applications running on IT infrastructure, comprises: a data collection engine collecting performance metrics for one or more applications running on the IT infrastructure; an anomaly detection engine analyzing the performance metrics and detecting anomalies, i.e. performance metrics whose values deviate from historic values with a deviation that exceeds a predefined threshold; a correlation engine detecting dependencies between plural anomalies, and generating anomaly clusters, each anomaly cluster consisting of anomalies that are correlated through one or more of the dependencies; a ranking engine ranking anomalies within an anomaly cluster; and a source detection engine pinpointing a problem source from the lowest ranked anomaly in an anomaly cluster.

Abstract: An application performance analyzer adapted to analyze the performance of one or more applications running on IT infrastructure, comprises: a data collection engine collecting performance metrics for one or more applications running on the IT infrastructure; an anomaly detection engine analyzing the performance metrics and detecting anomalies, i.e. performance metrics whose values deviate from historic values with a deviation that exceeds a predefined threshold; a correlation engine detecting dependencies between plural anomalies, and generating anomaly clusters, each anomaly cluster consisting of anomalies that are correlated through one or more of the dependencies; a ranking engine ranking anomalies within an anomaly cluster; and a source detection engine pinpointing a problem source from the lowest ranked anomaly in an anomaly cluster.

Abstract: An application performance management system is provided, which is adapted to analyze the performance of one or more applications running on information technology (IT) infrastructure. The application performance management system includes a data collector, an anomaly detector, an anomaly correlator, an anomaly ranking unit, and a source problem detector. The data collector collects performance metrics for one or more applications running on the IT infrastructure. The anomaly detector analyzes the performance metrics and detects anomalies, which may include performance metrics whose values deviate from historic values with a deviation that exceeds a predefined threshold. The anomaly correlator detects dependencies between plural anomalies and generates anomaly clusters. Each anomaly cluster includes anomalies that are correlated through one or more of the dependencies. The anomaly ranking unit ranks anomalies within an anomaly cluster.

Abstract: An application performance analyzer adapted to analyze the performance of one or more applications running on IT infrastructure, comprises: a data collection engine collecting performance metrics for one or more applications running on the IT infrastructure; an anomaly detection engine analyzing the performance metrics and detecting anomalies, i.e. performance metrics whose values deviate from historic values with a deviation that exceeds a predefined threshold; a correlation engine detecting dependencies between plural anomalies, and generating anomaly clusters, each anomaly cluster consisting of anomalies that are correlated through one or more of the dependencies; a ranking engine ranking anomalies within an anomaly cluster; and a source detection engine pinpointing a problem source from the lowest ranked anomaly in an anomaly cluster.

Abstract: Methods for identifying and analyzing performance traces are provided. Temporal logic formulas are patterns that can be compared with traces and individual events to identify the existence of certain behavior. Traces, sequences of time-stamped events in time order, are compared with one or more temporal logic formulas to identify the event sequences that match the formulas. The temporal logic formulas can be written in the simple temporal logic language that is presented. When a formula matches an event sequence, attributes from the event sequence are extracted and metric expressions are evaluated based on these attributes. The extracted attributes and the results of the metric expression are returned. This temporal logic pattern matching process can efficiently identify and analyze performance traces and significantly reduce manual effort for identifying performance problems.

Abstract: A system for monitoring network traffic and resource usage in a data center. The system comprises a node comprising an input terminal for capturing network packets. The node comprises a processor for running a packet analyzer for analyzing the network packets classifying packets in request/response pairs per type based on deep packet inspection and storing the request/response pair types together with time stamps. The node comprises a memory configured for storing the request/response pairs. The system comprises a collector module configured for collecting the request/response pairs. The system comprises at least one server running a simple agent programmed for transferring resource usage. The system comprises a correlation module programmed for correlating the request/response pairs information with the resource usage.