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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. An application performance analyzer adapted to analyze the performance of one or more applications running on IT infrastructure, said application performance analyzer comprising: a data collection engine adapted to collect performance metrics for said one or more applications running on said IT infrastructure; and an anomaly detection engine adapted to analyze said performance metrics and to detect anomalies, i.e. performance metrics whose values deviate from historic values with a deviation that exceeds a predefined threshold, wherein said application performance analyzer further comprises: a correlation engine adapted to detect dependencies between plural anomalies, and to generate anomaly clusters, each anomaly cluster consisting of anomalies that are correlated through one or more of said dependencies; a ranking engine adapted to rank anomalies within an anomaly cluster; and a source detection engine adapted to pinpoint a problem source from the lowest ranked anomaly in said anomaly cluster. 2. The application performance analyzer defined by claim 1, wherein said performance metrics comprise at least one of: CPU usage; disk space occupancy; memory usage; requests per second; response time; and error count value. 3. The application performance analyzer defined by claim 1, wherein said anomaly detection engine is adapted to apply the k-Nearest Neighbor algorithm or k-NN algorithm to detect said anomalies. 4. The application performance analyzer defined by claim 1, wherein said data collection engine is further adapted to collect communication path data indicative for communication paths between nodes of said IT infrastructure. 5. The application performance analyzer defined by claim 1, further comprising: a lightweight agent (AG) installed on one or more nodes of said IT infrastructure and configured to obtain said performance metrics and/or said communication path data, and to send said performance metrics and/or said communication path data to said data collection engine. 6. The application performance analyzer defined by claim 4, wherein said correlation engine is further adapted to cluster said anomalies based on said communication path data. 7. The application performance analyzer defined by claim 4, wherein said ranking engine is further adapted to rank anomalies within an anomaly cluster based on said communication path information. 8. The application performance analyzer defined by claim 1, wherein said correlation engine is further adapted to cluster said anomalies based on heuristics defining communication paths between nodes of said IT infrastructure or defining application insights of said one or more applications. 9. A method to analyze the performance of one or more applications running on IT infrastructure, said method comprising the steps of: collecting performance metrics for said one or more applications running on said IT infrastructure; and analyzing said performance metrics and detecting anomalies, i.e. performance metrics whose values deviate from historic values with a deviation that exceeds a predefined threshold, wherein said method further comprises: detecting dependencies between plural anomalies, and generating anomaly clusters, each anomaly cluster consisting of anomalies that are correlated through one or more of said dependencies; ranking anomalies within an anomaly cluster; and pinpointing a problem source from the lowest ranked anomaly in said anomaly cluster. 10. A data processing system comprising means for carrying out the method of claim 9. 11. A computer program comprising software code adapted to perform the method of claim 9. 12. A computer readable storage medium comprising the computer program of claim 11.