APM to AIOps - Core Transformation

Abstract

Purpose: The health of the modern application ecosystem depends on many
complex processes that are impossible to monitor and manage manually. A
disruption to these services will cost millions and will put at risk customer loyalty
and satisfaction. A few analytical solutions can scan through humungus data,
detect issues over time, and proactively inform the IT Operations team about
issues that might severely impact business systems.
Design/Methodology/Approach: Applying business monitoring solutions at
large-scale for gaining insights is ground-breaking. Leveraging machine learning
technologies, the solution will be able to analyze millions of parameters that
affect business metrics over time intervals. They continuously detect anomalies,
trends, and correlations and present the most relevant insights. Unlike prior
generations of solutions, these new solutions excel at separating signals from
noise and quickly learn and deliver critical insights.
Findings: Unlike prior generations of solutions, these new solutions excel at
separating signals from noise and quickly learn and deliver critical insights.
These solutions identify correlations, detect anomalies, and display potential root
causes.
Originality/Value: The study brings to light the core transformation needed to
migrate from the current generation APM to the Next Generation of AIOps and
the various building blocks and principles that need to be taken into account
while undergoing this transformation.
Paper Type: Research Thought