Why traditional event correlation falls short in modern IT and how AIOps can help

02-Mar-2025 08:05 PM by Ramkumar Ramaswamy

Modern IT environments span multiple cloud deployments, on-premises infrastructure, and microservices, sharing dynamic workloads. With such complexity, where every application or service depends on the entire chain, comprehensive modern IT observability is critical to avoiding gaps or oversight.

A lot happens when a server request or API call occurs. In observability terms, these are events: significant occurrences or changes detectable in an IT system. Events include errors (e.g., disk exhaustion or connection timeouts), performance issues (e.g., latency or memory leaks), security incidents (e.g., failed logins or attacks), operational changes (e.g., deployments or backups), and informational updates (e.g., successful order placements).

A typical enterprise generates terabytes of event data daily from applications, servers, networks, and databases, creating a vast pool for observability. An effective observability solution must store, retrieve, analyze, and correlate these events. Event correlation is fundamental for understanding system health, behavior, and performance—a critical responsibility for DevOps and IT engineers.

Why traditional methods fall short

Modern IT involves expanding AI adoption, DevOps practices, containers, virtual machines, microservices, and multi-cloud environments. Traditional monitoring tools struggle to keep pace with these due to their reliance on static, rule-based event correlation, which cannot handle the scale, complexity, and speed of modern operations. For tech leaders, recognizing these limitations is key to adopting observability solutions that provide rich context and actionable insights across all infrastructure layers for fixing issues faster and better.

Traditional event correlation systems rely on predefined rules to connect incidents. While simple and predictable, these systems fall short in modern IT for the following reasons:

Inefficiencies and rigidity : Modern IT systems constantly scale and reconfigure, but rule-based systems are rigid. Updating rules to match changes is tedious and error-prone. For example, anomalies outside predefined rules, like unexpected application behavior, go undetected, leaving critical issues unnoticed.
Alert deluges and false positives : Static rules trigger excessive, often irrelevant alerts, causing alert fatigue. This overwhelms IT teams, diverting focus from genuine threats. Over time, the constant noise dulls IT teams' ability to prioritize critical incidents, delaying action.
Slow incident response : In fast-changing IT environments, delays are costly. Traditional systems fail to adapt in real time, missing anomalies that don’t match rule books. For instance, a network issue following a service interaction may go unflagged until it causes significant disruptions.

AI-led event correlation: The scope, depth, and benefits

AIOps revolutionizes event correlation by processing vast data volumes in real time, using machine learning to uncover patterns, assess the relevance, and deliver actionable insights. Site24x7’s Problems feature consolidates related events, such as response time spikes, CPU threshold breaches, and application exceptions, into a single problem, reducing noise and enabling rapid root cause analysis. Here’s how AIOps-led event correlation helps:

Real-time analysis and proactive insights : Unlike static systems, AI continuously learns from data, identifying correlations and anomalies in real time. Site24x7’s Problems feature groups related events within a configurable time window (the default being 10 minutes), enabling proactive incident management. For example, the feature might detect early performance declines and link them to recent changes, prompting immediate action.
Scalability for complex architectures : Modern IT spans multi-cloud services, microservices, and hybrid setups. Site24x7’s Smart Groups automatically organize interdependent monitors based on the network topology or communication patterns, correlating events across diverse sources—logs, metrics, and traces—to provide a unified view of system health and performance.

More benefits of AI-led event correlation

AIOps drives precision in security and performance alerts, enhances troubleshooting, helps ensure compliance, and boosts customer satisfaction.

Cuts noise and spots issues better : Site24x7’s Problems feature uses AI to filter alerts based on the historical data, context, and severity, prioritizing high-impact issues. For example, when an alert aligns with past incidents that caused downtime, it is flagged for immediate attention, reducing alert fatigue and improving decision-making.
Reduces the mean time to resolution : AIOps accelerates troubleshooting by pinpointing root causes. For instance, when latency spikes, Site24x7 analyzes metrics and recent changes to identify whether a configuration update, traffic surge, or infrastructure issue is responsible. For supported application performance monitors, the Problems feature's Trace Analysis drills down to code-level issues, helping minimize downtime.
Helps ensure SLA compliance : Predictive analytics detects potential SLA breaches before they occur. By analyzing trends and anomalies, Site24x7 can flag risks like resource exhaustion, enabling teams to take proactive measures to maintain compliance with the GDPR, HIPAA, and SLAs.
Improves customer satisfaction : Faster remediation ensures near-uninterrupted services, helps teams maintain a high quality, and boosts app rankings and customer trust.

How a DevOps professional might monitor an application’s performance

Consider a DevOps team setting a two-second threshold for the app response time to flag anything higher as troubled. Typically, the app loads in 300ms, but a sudden lag exceeds one second, impacting the user experience yet staying within the static limit. Traditional monitoring would miss this.

Site24x7’s AIOps-powered Problems feature analyzes the performance history over 15 days, identifying the lag as a problem despite the normal status. It groups related events (e.g., database query spikes and API delays) into a single problem, highlighting it on a dashboard for immediate attention. Resolution follows two approaches:

The domain-aware approach : A checklist entered by the team guides troubleshooting, with Site24x7 checking components like database queries or remote calls to pinpoint infrastructure issues causing delays.
Smart Group correlation : Smart Groups organize interdependent monitors, correlating events within a configurable time window to identify root causes and presenting actionable insights on dashboards.

Site24x7’s Problems feature, supported by Trace Analysis for application monitors, drills down to code-level issues, significantly reducing the mean time to resolution. It also enables automated remediation, enhancing the customer experience. This makes AI-led event correlation a game-changer for managing modern IT complexity, improving efficiency, reducing downtime, and ensuring a high service quality.

The future of IT management lies in intelligent systems that predict and prevent issues instead of just reacting to them. For tech leaders, adopting AI-driven observability is critical for staying competitive. ManageEngine Site24x7 delivers comprehensive observability with AI-powered event correlation, empowering IT teams to thrive in complex environments. Try Site24x7 today.

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