There are many instances in our lives where we are stuck in issues and try to understand what caused them. Our initial thoughts are to identify the reason and the cause. We aim to trace the issue back to the origin and try to address them from where it all started. Just like, when we get common cold, we try to figure out where we contracted it.
Was it the late-night smoothie or exposure to someone with COVID symptoms? We never know until we figure out.
The actual cause can be analyzed only when we reflect on tracing back to when, where and what.
A similar analogy can be used to identify issues and root causes in IT operations. During outage, related teams get into a “war room” to debate the underlying cause. They swift through the symptoms and with the help of issue trace, they identify the root cause.
One commonly used technique in root cause identification is leveraging the help of topology and tracking the data flow. The topology data is correlated with the real-time monitoring data, that helps narrow down the problematic layer and initiate a comprehensive sequence of events.
However, not always one has access to up-to-date topology or CMDB data, right?
In the new age applications, that are based on containers and microservices, tracking the right Topology is quite tough.
So, how do we find the right root cause when Topology is unavailable?
An effective technique can be utilized, that is usage of time series-based causation analysis. In simple terms, this means running pattern-matching analysis on all the available data like metrics, logs, events, and traces from various entities on a timeline. Repeated occurrences of the same patterns on a timeline are more likely to match the current issue.
The starting point of the problem can be identified as a root cause. Performing this technique manually is quite impossible and hence the need is to rely on machine learning.
Machine learning helps in identifying and matching patterns or problem chains from historical data. Comparing these chains with the current sequence of events forms a pattern-matching.
Based on this predictability, the following are possible.
- What causes the issue?
- What is the impact point?
- When will the outage occur?
Causation analysis is based on causal graphs, that create a path diagram and help to identify what caused on what-based dependency.
“In statistics, econometrics, epidemiology, genetics, and related disciplines, causal graphs (also known as path diagrams, causal Bayesian networks or DAGs) are probabilistic graphical models used to encode assumptions about the data-generating process.”
—- An excepts from Wikipedia
Analyzing the event or metric flow and running the data across causation analysis quickly uncovers the root cause and impact points. It also helps in predicting an outage or when an entity in the network will degrade or cause downtime.
Enterprises should adopt these modern techniques to quickly identify and address the day-to-day issues operations teams face. This helps reduce the Mean Time To Investigate and eventually reduce the Mean Time To Resolve.
Also Read The Five Data Pillars of Effective Root-Cause Analysis
About HEAL Software
HEAL Software is a renowned provider of AIOps (Artificial Intelligence for IT Operations) solutions. HEAL Software’s unwavering dedication to leveraging AI and automation empowers IT teams to address IT challenges, enhance incident management, reduce downtime, and ensure seamless IT operations. Through the analysis of extensive data, our solutions provide real-time insights, predictive analytics, and automated remediation, thereby enabling proactive monitoring and solution recommendation. Other features include anomaly detection, capacity forecasting, root cause analysis, and event correlation. With the state-of-the-art AIOps solutions, HEAL Software consistently drives digital transformation and delivers significant value to businesses across diverse industries.