Detecting outliers in server transaction time as a form of time series data
Described embodiments provide systems and methods for detecting outliers on a series of data. A device receives a plurality of data points and adds a received data point to a first window of data comprising at least a predetermined number of received data points from the plurality of data points, responsive to detecting that the received data point is not an outlier from the first window of data. The device detects that one or more next data points of the received plurality of data points are outliers from the first window of data and determines that a count of the one or more next data points that are outliers exceeds a predetermined threshold. In response, the device establishes a replacement window of data.
1. A method comprising:
establishing, by a device, one or more windows of data comprising data points from a plurality of data points that fall within a boundary;
determining, by the device, that a count of one or more next data points that fall outside the boundary exceeds a threshold of a metric indicative of performance of a monitored device;
identifying, by the device, a change in performance of the monitored device based at least on the one or more windows of data and the count exceeding the threshold, the one or more windows of data including at least one next data point; and
providing, by the device, an action executable to address the change in performance of the monitored device.
2. The method of claim 1 , further comprising establishing, by the device, a first window of data of the one or more windows to include a predetermined number of the plurality of data points that fall within the boundary.
3. The method of claim 2 , further comprising establishing, by the device responsive to determining that the count of one or more next data points that are outliers exceeds the threshold, a second window of data of the one or more windows of data that includes the at least one of the one or more next data points that are outliers.
4. The method of claim 2 , further comprising modifying, by the device responsive to determining that the count of one or more next data points that are outliers exceeds the threshold, the data points of the first window of data.
5. The method of claim 2 , further comprising establishing, by the device, the first window of data as a baseline to evaluate the metric indicative of performance of the monitored device.
6. The method of claim 1 , further comprising identifying, by the device, the change in performance of the monitored device by comparison of the one or more windows of data to a baseline of values of the metric indicative of performance of the monitored device.
7. The method of claim 1 , wherein providing the action to take to address the change in performance comprises one of generating an alert or triggering a corrective action.
8. A system comprising:
a device having one or more processors, coupled to memory, and configured to:
establish one or more windows of data comprising data points from a plurality of data points that fall within a boundary;
determine that a count of one or more next data points that fall outside the boundary exceeds a threshold of a metric indicative of performance of a monitored device;
identify a change in performance of the monitored device based at least on the one or more windows of data and the count exceeding the threshold, the one or more windows of data including at least one next data point; and
provide an action executable to address the change in performance of the monitored device.
9. The system of claim 8 , wherein the device is further configured to establish a first window of data of the one or more windows to include a predetermined number of the plurality of data points that fall within the boundary.
10. The system of claim 9 , wherein the device is further configured to establish, responsive to determining that the count of one or more next data points that are outliers exceeds the threshold, a second window of data of the one or more windows of data that includes the at least one of the one or more next data points that are outliers.
11. The system of claim 9 , wherein the device is further configured to modify, responsive to determining that the count of one or more next data points that are outliers exceeds the threshold, the data points of the first window of data.
12. The system of claim 8 , wherein the device is further configured to identify the change in performance of the monitored device by comparison of the one or more windows of data to a baseline of values of the metric indicative of performance of the monitored device.
13. The system of claim 8 , wherein the device is further configured to provide the action to take to address the change in performance comprising one of generating an alert or triggering a corrective action.
14. The system of claim 8 , wherein the monitored device comprises one of a client, a server, an appliance or a virtualized environment.
15. A system comprising:
a device having one or more processors, coupled to memory, and configured to:
establish a sliding window of data comprising data points from a plurality of data points that fall within a lower boundary and upper boundary of the sliding window;
identify one or more next data points that are outliers based on falling outside the lower boundary and the upper boundary, the one or more next data points represent a measurement associated with the monitored device;
determine that a count of the one or more next data points exceeds a threshold of a metric indicative of a performance of a monitored device;
adjust, responsive to the determination, one of the lower boundary or the upper boundary of the sliding window;
identify a change in behavior of the monitored device based at least on the sliding window and the count exceeding the threshold; and
trigger an action executable to address the change in behavior of the monitored device.
16. The system of claim 15 , wherein the device is further configured to establish the sliding window of data to comprise a predetermined number of the plurality of data points that fall between the lower boundary and the upper boundary.
17. The system of claim 15 , wherein the device is further configured to establish the sliding window of data from a plurality of data points received via a log file.
18. The system of claim 15 , wherein the device is further configured to modify, responsive to determining that the count of one or more next data points that are outliers falling outside the lower boundary and the upper boundary exceeds the threshold, the sliding window to include the at least one of the one or more next data points that are outliers.
19. The system of claim 15 , wherein the device is further configured to identify the change in behavior of the monitored device by comparison of values of one or more next data points of the sliding window to a baseline of values of measurements of the monitored device.
20. The system of claim 15 , wherein the device is further configured to trigger the action comprising one of an alert or a corrective action.