Home > Storage > PowerScale (Isilon) > Product Documentation > Storage (general) > Dell PowerScale: Key Performance Prediction using Artificial Intelligence for IT operations (AIOps) > Results
In this solution, we use NFS latency from PowerScale as the indicator to be predicted. The AI model uses the performance data from the previous four hours to predict the trend and spikes of NFS latency in the next four hours. If the software identifies a five-minute period when a >10ms latency spike would occur more than 70% of the time, it will trigger a configurable alert to the user.
The following diagram shows an example. At 8:55 a.m., the AI model predicts the NFS latency from 8:55 a.m. to 12:55 p.m., based on the input of performance data from 4:55 a.m. to 8:55 a.m. The AI model makes predictions for each five-minute period over the prediction duration. The model predicts a few isolated spikes in latency, with a large consecutive cluster of high latency occurrences between around 12 p.m. and 12:55 p.m. A software system can use this prediction to alert the user about the expected increase in latency, giving them over three hours to get ahead of the problem and reduce the server load. In the graph, the dotted line shows the AI model’s prediction, whereas the solid line shows actual performance.
In summary, the solution has achieved the following: