Cutting Application Performance Issue Resolution Time by 64% with ML

Application Performance Monitoring (APM) is evolving out of traditional systems into the realm of machine learning, which enables continuous and efficient monitoring of platforms. Many companies are realizing the advantage of using machine learning in performance monitoring, while many others are concerned about its scope in their business. This blog aims to walk you through a real business case that has successfully integrated machine learning (ML) into its performance monitoring system, resulting in a 64% reduction in mean time to resolution.

Executive Summary

A cloud-based financial reporting platform processed 2 billion data points daily over 40 microservices. They monitored application performance issues through threshold-based alerting, manual dashboards, and reactive troubleshooting. However, they missed critical issues that seriously impacted customer experience and revenue loss. As a result, they approached SayOne to upgrade their existing application performance monitoring tool to perform real-time monitoring with machine learning.

The challenges

Below are the major challenges the company faced owing to the inefficiency of its reactive APM tool.

Alert fatigue

As their APM tool depended heavily on fixed thresholds, temporary spikes that occur normally during end-of-month reporting, scheduled imports, and regional traffic surges were flagged as issues. This led to alert fatigue, ignored warnings, and difficulty spotting real incidents.

Late detection

Most often, their reporting platform realized issues only after users complained, and by the time the team reacted and solved it, the customers had already experienced losses.

Slow resolution

Even after detecting an issue, the teams took too long to identify the root cause as the tools produced disconnected metrics. The combined effect of late detection and slow resolution led to 20-30 minutes of customer downtime on average per incident.

Missing hidden patterns

Their APM system missed slow memory leaks, periodic spikes, and region-specific degradation, as they could not analyze patterns and monitor issues unless values crossed the pre-defined limits.

The Solution

As a solution to the recurring incidents that affected their business significantly, SayOne integrated machine learning capabilities into its existing APM system to enable real-time performance monitoring.

Reduction in false and duplicate alerts

The machine learning system learned normal traffic cycles, usage patterns, seasonal behaviors, and other patterns to identify unusual behavior for a specific time, workload or environment. Through these learning abilities, the system significantly reduced false positives and duplicate alerts.

Efficient performance analysis

The ML systems detected abnormal resource consumption, gradual latency increases and degradation patterns well before failure occurred. This enabled the team to proactively solve these issues before they affected customer experience.

Faster resolution

The mean time to resolution (MTTR) reduced significantly as the ML system correlated the signals automatically across microservices, databases, infrastructure, user transactions, etc., to identify the root cause without delay.

Cloud cost reduction

In addition to making performance monitoring efficient, machine learning also optimized cloud costs by identifying inefficient workloads, abnormal compute usage, and under-utilized resources.

The implementation that ensured unified visibility, real-time monitoring, and integration capability was completed in 3 weeks. SayOne’s development and integration team solved the following implementation changes during the process.

• The platform’s performance data was scattered across different systems, which made developing accurate insights difficult. To overcome this, our team began by building a unified observability pipeline that consolidated live performance data into a single platform.

• Modern cloud environments constantly change, which affects the stability of the monitoring baselines. The team addressed this by implementing adaptive ML models that continuously learned changing workload patterns.

• During the initial stages, when the models were still learning operational behaviour patterns, the system was prone to generating excessive alerts. To avoid this, we used progressive model tuning and staged deployment. We also allowed model improvement by using real production data and feedback loops.

Results

• The mean time to detection was reduced by 85%, from an average of 30 minutes per incident.
• The false positive rate reduced from 70% to 15% during the early stage of implementation, which further reduced to as low as 2% as the model learned continuously.
• As the root cause identification speed increased by 70%, the MTTR decreased by 64%.
• The cloud infrastructure waste has been reduced by 42% through smarter optimization of workload.

Key Learnings

• The quality of data is more important than quantity because machine learning performance depends heavily on quality and consistency.
• Rather than replacing at once, layering ML capabilities onto existing systems is favourable to reduce operational risk.
• Correlating application, infrastructure, and user behaviour data produces more meaningful operational insights rather than focusing on individual infrastructure metrics.

Looking Ahead

As the company can now monitor their application performance efficiently, they are aiming to integrate AI systems that can even perform self-healing actions after detecting anomalies and identifying the root cause.

Is your existing APM system performing to your expectations?

Make your APM system work smarter by integrating machine learning abilities into the system, without replacing the system completely. SayOne’s performing monitoring services help you identify simple and critical issues before they reach your customers. Contact our team to learn how you can make APM work smarter for your business.