APPLICATION PERFORMANCE ANALYSIS THAT IS ADAPTIVE TO BUSINESS ACTIVITY PATTERNS
The present invention relates to a system and method for assessing application performance. hi some embodiments, the analysis considers external factors, such as business hours, time zone, etc., to identify or recognize distinctive intervals of application performance. These distinctive intervals correspond to different periods of activity by an enterprise or business and may occur in a cyclical manner or other type of pattern. The distinctive intervals defined by external factors are employed in the analysis to improve aggregating of statistics, setting of thresholds for performance monitoring and alarms, correlating business and performance, and the modeling of application performance. The metrics measured can include, among other things, measures of CPU and memory utilization, disk transfer rates, network performance, queue depths and application module throughput. Key performance indicators, such as transaction rates and round-trip response times may also be monitored.
1 . A method for dynamically generating at least one metric threshold associated with a metric in a monitored system, the method comprising:
receiving data associated with a metric;
determining distinct intervals of time that result from external factors influencing activity of the monitored system;
statistically analyzing the received data for each distinct interval separately; and
determining at least one alarm threshold for future, similar intervals based on the statistical analysis.
2 . The method of claim 1 wherein receiving the data comprises aggregating the received data based on an aggregation period for each distinct interval.
3 . The method of claim 1 , wherein determining the distinct intervals comprises receiving an input specifying the distinct intervals of time.
4 . The method of claim 1 , wherein determining the distinct intervals comprises receiving an input specifying hours of operations for the monitored system.
5 . The method of claim 1 , wherein determining the distinct intervals comprises receiving an input specifying a set of external factors that influence the activity of the monitored system.
6 . The method of claim 1 , wherein determining the distinct intervals comprises determining the distinct intervals of time from a heuristic analysis of the received data.
7 . The method of claim 1 , wherein statistically analyzing the received data:
receiving data at a first time scale; and
extrapolating received data at a second time scale for the distinct interval.
8 . The method of claim 1 , further comprising triggering an alarm on receipt of received data within a similar interval that violates the at least one alarm threshold.
9 . The method of claim 1 , further comprising:
statistically analyzing received data for at least one additional metric for one of the distinct intervals of time; and
correlating the metric and the at least one additional metric based on the statistical analysis within the distinct interval of time.
10 . A method for monitoring a system, wherein activity of the system is influenced by external factors that result in intervals of different activity, the method comprising:
receiving data associated with a metric of the monitored system;
identifying time intervals of the received data based on information indicating the external factors;
determining, for each identified Urns interval, at least one value indicating a statistical distribution of values of the metric; and
determining, for subsequent intervals that are similar to the identified intervals of time, at least one threshold indicating a boundary of an abnormal value for the metric.
11 . The method of claim 10 , wherein identifying the time intervals comprises receiving information specifying a range of hours in a day.
12 . The method of claim 10 , wherein identifying the time intervals comprises receiving information specifying days of a week.
13 . The method of claim 10 , wherein identifying the time intervals comprises receiving information specifying days of a year.
14 . The method of claim 10 , wherein determining the at least one value indicating the statistical distribution comprises determining a mean value of the metric within each identified time interval.
15 . The method of claim 10 , wherein determining the at least one value indicating the statistical distribution comprises determining a standard deviation value of the metric within each identified time interval.
16 . The method of claim 10 , further comprising triggering an alarm event on receipt of received data within a subsequent interval that is similar to one of the identified intervals that violates the at least one threshold.
17 . A system for monitoring a system, said system comprising:
a collector for receiving data for at least one metric of performance by the monitored system; and
a monitoring server configured to aggregate the received data, recognize time intervals of the received data corresponding to a distinct period of activity in the monitored system, determine, for each identified time interval, at least one value indicating a statistical distribution of values of the metric, and determining, for subsequent intervals that are similar to the identified intervals of time, at least one threshold indicating a boundary of an abnormal value for the metric.
18 . The system of claim 17 , wherein the monitoring server further comprises a data aggregator configured to aggregate the received data based on the identified intervals of time.
19 . The system of claim 17 , wherein the monitoring server is configured to determine the at least one threshold for each identified interval based on a mean value and standard deviation of the metric for each interval.
20 . The system of claim 17 , further comprising an alarm engine configured to generate an alarm event on receipt of received data within a subsequent interval that is similar to one the identified intervals that violates the at least one threshold.