Abstract: A method comprises obtaining a set of log files for a software system. The set of log files applies to an extended window. A periodic pattern in a first set of error-event surges in the set of log files is identified. The error-event surges in the first set is identified as event noise. A second set of log files for the software system is obtained. The second set of log files applies to a shortened window. Timeseries analysis on the second set of log files is performed. A particular error-event surge in a detection period in the second set of log files that is abnormal as compared to the shortened window is detected based on the timeseries analysis. That the particular error-event surge does not fit into the periodic pattern is determined, the particular error-event surge is characterized as an anomaly, based on the determining.

Abstract: A method comprises receiving a set of log files that correspond to a detected anomaly in a software system. The set of log files are input into a first classification algorithm. A set of classified log events is received from the first classification algorithm. The set of classified log events is input into a second classification algorithm. A classification of the detected anomaly is obtained from the second classification algorithm.

Abstract: A method comprises obtaining a set of log files for a software system. The set of log files applies to an extended window. A periodic pattern in a first set of error-event surges in the set of log files is identified. The error-event surges in the first set is identified as event noise. A second set of log files for the software system is obtained. The second set of log files applies to a shortened window. Timeseries analysis on the second set of log files is performed. A particular error-event surge in a detection period in the second set of log files that is abnormal as compared to the shortened window is detected based on the timeseries analysis. That the particular error-event surge does not fit into the periodic pattern is determined, the particular error-event surge is characterized as an anomaly, based on the determining.

Abstract: Methods and systems for managing computer network traffic are provided. A computer implemented method includes tunneling network traffic between a first network and a second network using a data storage system that is shared by the first network and the second network.

Abstract: A client including: a processor to implement a browser; a network interface; and a cookie manager to reduce transmission bandwidth, wherein prior to transmitting a request to a Uniform Resource Identifier (URI), the browser to use the cookie manager to access a list of cookies used by the URI to prepare a response and to remove from the request to the URI any cookies not on the list.

Abstract: Methods and systems for managing computer network traffic are provided. A computer implemented method includes tunneling network traffic between a first network and a second network using a data storage system that is shared by the first network and the second network.

Abstract: Techniques for processing filter rules are disclosed. To this end, filter rules having one or more attributes where each attribute indicates a condition to qualify whether a filter rule applies to a subsequent event are received. Summary rules are generated where each summary rule has a number of summary conditions. Some filter rules become associated with the generated summary rules. The summary conditions are extended to span the attributes of the associated filter rules.