Abstract: Systems and methods forecast processing capacity by monitoring historical key performance indicators (KPIs). The KPIs include measured values relating to a number of transactions that can be processed in parallel, an average number of transactions currently being processed, and an average amount of time to process each current transaction. One or more query parameters are received to query stored KPI data corresponding to the monitored historical KPIs. A forecasted processing volume level based on predicted values determined from a predictive model of the stored KPI data. The method also comprises generating an alert in response to the processing volume level being forecast to exceed a defined threshold is generated, thereby providing more reliable capacity monitoring and prediction of problems with processing applications that may otherwise go undetected until performance impacts occur.

Abstract: Systems and methods forecast processing capacity by monitoring historical key performance indicators (KPIs). The KPIs include measured values relating to a number of transactions that can be processed in parallel, an average number of transactions currently being processed, and an average amount of time to process each current transaction. One or more query parameters are received to query stored KPI data corresponding to the monitored historical KPIs. A forecasted processing volume level based on predicted values determined from a predictive model of the stored KPI data. The method also comprises generating an alert in response to the processing volume level being forecast to exceed a defined threshold is generated, thereby providing more reliable capacity monitoring and prediction of problems with processing applications that may otherwise go undetected until performance impacts occur.