Who:

A UK-based mobile operator

What:

The operator used the ELK stack to monitor middleware service response times, but the process was tedious, error-prone, and relied heavily on manual observation. Gradual performance degradation often went unnoticed for days, eventually breaching SLAs and impacting other services. This highlighted the limitations of human monitoring in a complex environment.

How:

Torry Harris Integration Solutions (THIS) introduced its 4Sight machine learning solution to detect gradual response time increases, sudden 10% fluctuations, and SLA breaches. By identifying these patterns early, 4Sight minimized manual monitoring and helps maintain SLA compliance.

Results:
  • Cost-effective and efficient solution: the Kafka cluster ran on a commonplace server with 32 Gigabytes of RAM and was able to process about 300 transactions per second.
  • The machine learning capabilities were introduced into the existing ELK environment without any disruption and rapidly detected and sent alerts about anomalies based on service response times.
  • Automatic monitoring of the different services ran 24x7, requiring minimal human interaction and freeing up the workforce.
  • Early detection of problems in any service allowed developers to implement fast fixes, reducing pressure on the server and preventing downtime.

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