Server Resources And Capacity Planning Forecasting

There can be huge impact internally as well as externally if we lose our services. There can be many root causes that can impact services like Network failure or device down. Now the device availability or reachability can be affected by having high resource utilization which it can be Memory, CPU or Logical Disk. If any of these resources utilized more than the need, it will affect the device and then the services which are hosted on this server. The services can be any type of portal, and tool or any kind of medium connecting to different clients.  What if we can reduce these downtimes? What if we can predict the failure? To be more precisely, What if we can predict these resource Utilization and have better planning on the capacity?

As of now, we are monitoring servers and performing Reactive Approach. Meaning, when any threshold breaches on the server for these resources, then the alert will be sent out to the team and then they start taking corrective actions only at the time issue happened. If there is any kind of delay in the process, that will directly lead to service disruptions and downtime.

 Now, with this approach, we are heading up to the Pro-active approach where it has been proven that the solution is capable to predict and forecast CPU, Memory and Logical Disk Average Utilization resource utilization 1 Month in advance and IT Infra Ops Team will be able to take corrective actions 1 month before even the issue actually happens and thus reduce the downtime and any kind of disruptions in the services. It will not only help us to reduce the downtime and disruptions in the services but also help us to take better Capacity planning. This marks a new milestone in our quest for making internal IT operations more intelligent

This ML Solution covers all the different flavors of OS and Server types. It has been trained using historical data for all the servers monitoring under any monitoring tool and is predicting and forecasting for all the servers which are under monitoring tool.

Model’s Accuracy: ~ 96%