Abstract: Techniques are disclosed for determining inbound and outbound external queue backlogs, internal queue backlogs, active counts, and external system interaction data values in systems employing asynchronous communication techniques, which may be used for comparative performance analysis and bottleneck detection in service-oriented applications. Key statistics related to key points in a SOA system, for key activities during processing of transactions in the SOA system, are collected and aggregated. The statistics for the time interval may be added to corresponding cumulative statistics, and persisted to storage. Reports may be generated based upon the statistics to present to a user. Automated processes may be implemented to use the statistics to identify the existence and likely cause of SOA application performance issues, and potentially to attempt to remediate the issues.

Abstract: Techniques are disclosed for assembling statistics for diagnosing performance and scalability issues in SOA systems. Key statistics related to key points in a SOA system, for key activities during processing of transactions in the SOA system, are collected and aggregated. The statistics may include message flow rates and latency at key points in the system during a time interval, and execution times for key activities in the system during the time interval. The statistics for the time interval may be added to corresponding cumulative statistics, and persisted to storage. Reports may be generated based upon the statistics to present to a user. Automated processes may be implemented to use the statistics to identify the existence and likely cause of SOA application performance issues, and potentially to attempt to remediate the issues.

Abstract: Techniques are disclosed for determining inbound and outbound external queue backlogs, internal queue backlogs, active counts, and external system interaction data values in systems employing asynchronous communication techniques, which may be used for comparative performance analysis and bottleneck detection in service-oriented applications. Key statistics related to key points in a SOA system, for key activities during processing of transactions in the SOA system, are collected and aggregated. The statistics for the time interval may be added to corresponding cumulative statistics, and persisted to storage. Reports may be generated based upon the statistics to present to a user. Automated processes may be implemented to use the statistics to identify the existence and likely cause of SOA application performance issues, and potentially to attempt to remediate the issues.

Abstract: Techniques are disclosed for assembling statistics for diagnosing performance and scalability issues in SOA systems. Key statistics related to key points in a SOA system, for key activities during processing of transactions in the SOA system, are collected and aggregated. The statistics may include message flow rates and latency at key points in the system during a time interval, and execution times for key activities in the system during the time interval. The statistics for the time interval may be added to corresponding cumulative statistics, and persisted to storage. Reports may be generated based upon the statistics to present to a user. Automated processes may be implemented to use the statistics to identify the existence and likely cause of SOA application performance issues, and potentially to attempt to remediate the issues.