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Highlights
- Artificial intelligence (AI) in application performance monitoring (APM) leverages the power of AI/ machine learning (ML) techniques to analyze system health data such as logs, metrics, and traces from multiple sources to proactively manage infrastructure and application performance and availability at scale.
- AI and ML help detect anomalies, discover bottlenecks, and analyze performance and outage issues with automated correlation of observed metrics and their behaviors along with log analysis.
- They play a significant role in capacity forecasting, anomaly detection, root cause analysis, cost optimization, intelligent alerting, network security and more.
- Organizations embedding AI along with full-stack visibility of their IT systems and underlying applications in a multi-cloud landscape benefit from a preventive, predictive, and proactive approach rather than a reactive one.
- Organizations accrue return on investment (ROI) from lower operational costs, better productivity and efficiency, reduction in application outage, and efficient resource allocation and utilization.
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Paradigm shift
In the era of digital transformation, new complex infrastructures and applications are being developed with new techniques and modern design patterns. Managing and operating the infrastructure of such modern software is now facing new challenges as traditional monitoring solutions fail to provide complete visibility for IT systems and applications. Also, it falls short of being preventive, proactive, and predictive in nature. Traditionally, APM tools relied on static thresholds and predefined rules to monitor application performance. These methods were often reactive, detecting issues after they had already impacted end users. The integration of AI/ML algorithms into APM tools brings in a paradigm shift by enabling proactive and dynamic monitoring.
AI-enabled application performance monitoring solutions will help to proactively monitor system and application health parameters, logs, and traces to detect and address performance issues, leading to an enhanced system performance and availability. One of the key advantages of AI/ML-powered APM is its ability to learn and adapt. Through continuous analysis of vast datasets, AI/ML algorithms can identify patterns, anomalies, and trends that would be impossible for human operators to detect manually. This learning process allows APM tools to establish baselines, detect deviations, and predict potential performance issues well before they affect end-users.
In our internal analysis, we ascertained that AI-enhanced APM capabilities reduces the resolution time for major incidents by 75%. We achieved a reduction in downtime by 50% and improved mean time to repair (MTTR) by 50%. These statistics highlight the importance of AI/ML in automating and proactively preventing issues.
Some of the key areas where AI/ML can improve application monitoring and performance are highlighted in this blog.
Capacity forecasting
Real-time forecasting of system and application health parameters such as CPU utilization, memory utilization, average response time, user system load, and others can help organizations to foresee the behavior of systems (system slowness, increased latency, or out of memory errors) in real-time to prevent potential system performance issues and outages later. Advanced ML algorithm and statistical intuition enables automatic learning of changing pattern in metrics values, it can correlate with the underlying system logs to pull out the probable reason for spikes in the forecasted values. This helps support teams to proactively size and scale the resources of the applications.
Anomaly detection
Multi-cloud IT Infrastructure and application monitoring are overly complex in nature due to the presence of several virtual machines, application instances, network components, and other IT resources. These systems generate enormous amount of system logs. It becomes difficult for support teams to track these log data and find irregularities and anomalies that may cause performance concerns, network throttling, and even outages. AI-based anomaly detection systems can help detect deviations from the system’s normal behavior and finding outliers in various univariate time series metrics and logs.
Root cause analysis
ML algorithms are used to process enormous amounts of data and to identify patterns and correlations within the system health metric data that are tedious to detect manually. AI models can analyze the metrics, logs, and traces proactively and efficiently and create dependency maps by analyzing the relationship between various components. This helps identify which components are affected when an issue occurs, narrowing down potential root causes.
Preventive measures
AI-applied operations accelerate observability as it predicts issues proactively so that they can be prevented before they happen. AI models can quickly analyze and correlate system health data to predict potential issues before they lead to system unavailability and performance issues. These can fine-tune themselves based on user feedback and generate better predictions of system failure or performance issues. They can suggest preventive measures or predict solutions to avoid such issues.
Intelligent alerting
AI can be leveraged for alert suppression (alert noise reduction) so that support teams can act on real issues instead of noise. Such intelligent alerting method can help in the analysis of logs, correlate metrics and traces to determine the alerts that have potential impact on performance and stability of the application and ignore/eliminate false positives, thereby saving time and resources.
Cost analytics
AI-enabled cloud cost monitoring can enhance cost management and optimization. This is because daily cost reports sent by cloud providers are not enough to detect cost anomalies in real-time at lower granularity as well as to forecast monthly costs with higher precision. Further, AI can analyze spend patterns to detect overspending and spend outliers and suggest cost cutting measures which help organizations to optimize budget, plan, and allocate resources more effectively.
Integrating AI with monitoring solutions can enhance multi-cloud infrastructure and application monitoring through predictive monitoring, capacity forecasting, anomaly detection, root cause analysis, actionable intelligent alerting, and cloud cost analytics. Embedding AI in application performance monitoring offers a wide range of quantifiable benefits, including cost savings through reduced downtime, efficient resource allocation, lower operational costs, better productivity, and cloud cost optimization.
