When was the last time operations came to a halt due to an unplanned server downtime? Do you remember the moments when you had to work extra hours to make up for productivity loss? Well, those are the times we point fingers at the Infrastructure team for the unexpected server failures or for not being more prepared.
Organizations tried to solve this issue by moving from a corrective maintenance approach to a preventative maintenance approach. This is when one goes from reacting to a problem to being more proactive by replacing components before its full lifetime. One problem with that is poor asset utilization.
This is where predictive maintenance analytics come in to help organizations achieve balance, high asset utilization, and savings in operational costs, plus experience a boost in productivity with just-in-time component replacements.
How does Predictive Maintenance work?
Customer servers: A Predictive Maintenance case study
Now the question is: how do you implement predictive analytics in this business operation? To predict probable downtimes for a customer’s servers, we used Analance Advanced Analytics for data modeling and Analance Business Intelligence for reporting, dashboarding, and alerts.
We first monitored the customer’s server utilization and laid out a solution plan by forecasting key system metrics like CPU, RAM, and memory utilization. We then combined the values of these metrics to perform a multiclass classification analysis to predict possible server downtimes.
We also defined server utilization thresholds. If usage exceeded the set threshold, auto alerts would be sent to the required stakeholders for corrective action.
Sourcing data
We had data from an ELK stream, which had indexes that were dedicated to capturing the system’s key metric values in real-time. We sourced this data through Analance’s Elasticsearch connector, which allows for live streaming data to be made available inside the platform.
- Data source – Metricbeat, ELK stream
- Connector – Analance Elasticsearch Live
The data had the real-time values of all the major system metrics like CPU, Memory, RAM, inbound and outbound traffic, number of processes, etc.