Using AI and machine learning for APM

Marketing hype fuels AI predictive network troubleshooting

Organizations are reassessing traditional application performance monitoring (APM) and taking a more holistic, tactical approach to acquiring useful operational data. Today, DevOps and IT teams are employing automation, machine learning and AI to bridge the gap between traditional APM approaches and new application demands, such as availability and constant updates.

Moreover, today’s apps comprise microservices and open source components, as well as multiple cloud services, complicating the search for root causes of errors. This article examines how APM can benefit from AI and machine learning and enable new potential for application monitoring and protection. Key implementation steps IT leaders must consider and the future of APM and AI are also explored.

AI, APM and end-to-end visibility

Traditional APM relies on monitoring code execution to indicate problems, an approach that used to be enough for consistent application performance. However, modern apps typically consist of millions of lines of code often running in containers. Moreover, these application environments are interconnected and encompass both on-premises and multi-cloud environments. For example, research found that a single application transaction crosses an average of 35 different technology systems or components.

To further complicate troubleshooting, IT teams must manage a broad spectrum of noncritical components that affect application performance as well as complex hybrid ecosystems that include Kubernetes orchestrations and innumerable containers. Simply put, single-purpose APM tools lack the integrations necessary for full stack visibility. To gain the insights they need, IT teams are adopting agile DevOps approaches, along with AI and machine learning, to handle the sprawl of application components, analyze vast volumes of data and gain actionable insights.

AI-powered APM systems offer real-time, proactive remediation to resolve performance and availability issues in today’s highly complex, modern IT environments. Employing algorithms, analytics and automation to provide comprehensive visibility and map interdependencies, AI can quickly detect and repair issues before IT and DevOps teams are aware that problems exist. For example, using computational power, AI systems can instantly compare different combinations of security layers to pinpoint vulnerabilities in web applications.

The fragmented transaction paths of modern applications make end-to-end visibility nearly impossible without AI and machine learning capabilities. Moreover, these unstructured data points are too numerous and obscure to be of any use to operations teams. However, machine learning algorithms can mine these vast data stores to pinpoint critical patterns. Through AI adoption, IT teams can find anomalies and quickly resolve performance issues. And, in addition to data consolidation, AI can automatically observe, correlate and analyze data from multiple sources to improve application performance.

AI-powered APM systems offer real-time, proactive remediation to resolve performance and availability issues in today’s highly complex, modern IT environments.

How IT teams combines AI with APM

IT teams can streamline operations, release software faster and deliver better business outcomes by employing AI for full-stack monitoring, root cause analysis and continuous automation. By using native AI features, teams can accelerate and simplify management workflows, in addition to gaining visibility across infrastructure, networks and users. Administrators can automate processes to make monitoring easier and more reliable, troubleshoot software more quickly and deliver better business outcomes.

For example, team members can apply AI and machine learning on specific components and perform pattern analysis to gain precise answers and proactively solve issues before they affect performance. The result is more effective microservices and containerized environments. Another critical goal for AI and machine learning adoption is to avoid overwhelming operational capacity. Teams can generate technical indicators that function as dynamic signals, enabling them to instrument and monitor application KPIs.

They can also use AI to automatically adjust warning thresholds and avoid alert storms triggered by fluctuations in scaling. By providing context, team members can use AI and machine learning to ensure that they respond to the right alerts quickly and efficiently. Organizations also rely on these technologies to clarify key elements in the relationship between operations and business goals. By identifying repetitive operational patterns, IT teams can reveal the connections between these patterns and extend the value and benefits of AI-enabled APM to help meet projected business outcomes. These include ensuring that all business- and customer-facing applications are efficient and highly responsive.

Future of AI and applications

There are numerous innovations on the horizon as organizations seek to ensure consistently high application performance. Improved AI capabilities will continue to deliver operational analytics that cover specific uses cases, whether they extend to network and database monitoring or log, container and user monitoring. Indeed, according to a 2021 research report from Global Industry Analysts Inc., the global APM market is projected to rise to $12 billion by 2026 from its 2020 value of $6.3 billion.

Both machine learning innovations and deep AI analytics promise new levels of visibility and automation that will far exceed simple application debugging and tuning. New monitoring methods will optimize how users experience software, and enhanced machine-to-machine interactions, enabled by IoT advances at the edge, will bring new levels of precision to automation.

The ongoing move toward distributed services, both in terms of IT infrastructure and applications, will further enhance development and operations, enabling businesses to track the user experience across multiple applications and platforms. Of course, issues around privacy and security will continue to prompt closer scrutiny and vigilance. However, improved AI-powered automation capabilities will enable organizations to more strategically control costs while refining their software deployments.

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