ices. Performance data is usually represented in graphical plots on the NMS as the data is huge. Since performance analysis would require comparison with historical data, the graphical plots help to get results and make intelligent decisions faster. In most NMS solutions it is also possible to specifically monitor a particular performance parameter by setting thresholds. The process of "thresholding" involves identifying appropriate performance thresholds (values) for each important variable (KPI), SO that exceeding these thresholds indicates a network problem worthy of attention. When the threshold is exceeded, an event (log) is automatically generated to notify the operator, helping the operator to keep track of multiple performance variables in the network at the same time. Rather than generating a log, the NMS can also take corrective actions when a threshold is exceeded. Let us consider an example of the thresholding process where the variable to be monitored is the utilization for trunk-X. When the utilization of trunk-X is 50%, a log is generated with severity "minor," for 70% a log with severity "major" is generated, and for 80% utilization a log with severity "critical" is generated. Traffic routing algorithms are then applied to ensure that the utilization of trunk-X does not exceed 85% to achieve maximum efficiency of operation. The NMS functions that are part of network performance management are: Monitor performance: After performance data collection, the key performance parameters can be monitored by an operator using the NMS. Graphical plots, aggregated records, and summary reports help in monitoring a large bulk of data. In most cases, data is collected off-line as a bulk download at regular intervals rather than polling on a regular basis. Streaming of performance data as events using protocols like Net-flow or IP flow information export (IPFIX) is also popular. Setting thresholds on specific performance variables as discussed earlier is also an effective method for performance monitoring. Analyz