Abstract: According to an example, aggregation based event identification may include aggregating each of a plurality of source events by an event type of event types that represent dusters of the source events and/or a host of a source event of the source events to generate a reduced number of the source events. Aggregation based event identification may further include analyzing a characteristic for each of the reduced number of the source events, and assigning, based on the analysis of the characteristic for each of the reduced number of the source events, a characteristic weight to each of the reduced number of the source events. Further, aggregation based event identification may include aggregating the characteristic weights for each of the reduced number of the source events to determine an aggregated event issue weight for each of the reduced number of the source events.

Abstract: In some examples, a first pair of parameters in respective first and second log message streams associated with respective first and second source components and a second pair of parameters in the respective first and second log message streams may be identified. The first pair may be identical and the second pair may be identical. It may be determined that first pair of parameters was simultaneously generated and that the second pair of parameters was simultaneously generated in the first and in the second log message streams. A linkage score may be determined between the first and the second source components. The linkage score may be based on the determination that each of the respective first and the second pairs of parameters was simultaneously generated. It may be determined that that the first and second source components are topologically linked based on the linkage score.

Abstract: In a method for inferring a topology of components in a network, at least one operation parameter is provided for each of a plurality of components in a network, and a similarity measure is computed between at least two of said components based on values of said operation parameters. Based on said similarity measure, it is determined whether said two components are topologically connected, wherein said similarity measure is computed in terms of a normalized mutual information between said operation parameters pertaining to said two components.

Abstract: In a method for inferring a topology of components in a network. At least one operation parameter is provided for each of a plurality of components in a network, and a similarity measure is computed between at least two of said components based on values of said operation parameters. Based on said similarity measure, it is determined whether said two components are topological connected, wherein a said similarity measure is computed in terms of a normalized mutual information between said operation parameters pertaining to said two components.

Abstract: According to an example, aggregation based event identification may include aggregating each of a plurality of source events by an event type of event types that represent dusters of the source events and/or a host of a source event of the source events to generate a reduced number of the source events. Aggregation based event identification may further include analyzing a characteristic for each of the reduced number of the source events, and assigning, based on the analysis of the characteristic for each of the reduced number of the source events, a characteristic weight to each of the reduced number of the source events. Further, aggregation based event identification may include aggregating the characteristic weights for each of the reduced number of the source events to determine an aggregated event issue weight for each of the reduced number of the source events.

Abstract: In some examples, a first pair of parameters in respective first and second log message streams associated with respective first and second source components and a second pair of parameters in the respective first and second log message streams may be identified. The first pair may be identical and the second pair may be identical. It may be determined that first pair of parameters was simultaneously generated and that the second pair of parameters was simultaneously generated in the first and in the second log message streams. A linkage score may be determined between the first and the second source components. The linkage score may be based on the determination that each of the respective first and the second pairs of parameters was simultaneously generated. It may be determined that that the first and second source components are topologically linked based on the linkage score.

Abstract: A system and method for reputation-based service valuation are provided herein. The method includes receiving, within a computing system, subjective evaluation criteria relating to a service from any of a number of users of the service. The method also includes scaling the subjective evaluation criteria based on a reputation of each of the users to produce reputation-based subjective evaluation criteria. The method further includes generating a service valuation for the service based on the reputation-based subjective evaluation criteria.

Abstract: A system for monitoring the post-deployment performance of a web-based or other transactional server is disclosed. The monitoring system includes an agent component that monitors the performance of the transactional server as seen from one or more geographic locations and reports the performance data to a reports server and/or centralized database. The performance data may include, for example, transaction response times, server response times, network response times and measured segment delays along network paths. Using the reported performance data, the system provides a breakdown of time involved in completion of a transaction into multiple time components, including a network time and a server time. Users view the transaction breakdown data via a series of customizable reports, which assist the user in determining whether the source of the performance problem. Additional features permit the source to be identified with further granularity.

Abstract: A system for monitoring and analyzing the post-deployment performance of a web-based or other transactional server is disclosed. The monitoring system includes agent components that monitor and report various performance parameters associated with the transactional server, such as response times seen by end users, server and network times, and various server resource utilization parameters. A web-based reports server displays the data collected by the agents through a series of charts and graphs that indicate whether correlations exist between the response times and lower level parameters. A root cause analysis system applies statistical algorithms to the collected data to detect performance degradations in specific parameters, and uses predefined parameter dependency rules to correlate high level performance problems to likely sources or causes of such problems.

Abstract: A system for monitoring and analyzing the post-deployment performance of a web-based or other transactional server is disclosed. The monitoring system includes agent components that monitor and report various performance parameters associated with the transactional server, such as response times seen by end users, server and network times, and various server resource utilization parameters. A web-based reports server displays the data collected by the agents through a series of charts and graphs that indicate whether correlations exist between the response times and lower level parameters. A root cause analysis system applies statistical algorithms to the collected data to detect performance degradations in specific parameters, and uses predefined parameter dependency rules to correlate high level performance problems to likely sources or causes of such problems.

Abstract: A system for monitoring the post-deployment performance of a web-based or other transactional server is disclosed. The monitoring system includes an agent component that monitors the performance of the transactional server as seen from one or more geographic locations and reports the performance data to a reports server and/or centralized database. The performance data may include, for example, transaction response times, server response times, network response times and measured segment delays along network paths. Using the reported performance data, the system provides a breakdown of time involved in completion of a transaction into multiple time components, including a network time and a server time. Users view the transaction breakdown data via a series of customizable reports, which assist the user in determining whether the source of the performance problem. Additional features permit the source to be identified with further granularity.

Abstract: A system for monitoring and analyzing the post-deployment performance of a web-based or other transactional server is disclosed. The monitoring system includes an agent component that monitors and reports the performance of the transactional server over time as seen from one or more user locations. The performance data includes response times indicative of delays experienced by end users. Concurrently with the monitoring of end user performance, a server agent component monitors and reports server resource utilization parameter measurements. A web-based reports server displays the data collected by the two types of agents through a series of charts and graphs that indicate whether correlations exist between the response times and specific server resource utilization parameters. The collected data may also be analyzed using a root cause analysis system that detects performance degradations and identifies likely sources of such degradations.