Leveraging AI to Enhance Performance and Resilience of Java Applications

Java applications are expected to deliver high performance, scalability, and reliability, especially in today’s high-traffic environments. At the heart of every Java application lies the Java Virtual Machine (JVM), responsible for executing bytecode and managing system resources. Tuning the JVM is essential to ensure optimal performance, and with the rise of AI-powered tools, developers now have smarter ways to analyze, optimize, and maintain JVM configurations.

This need has further been catalyzed by the growth in the number of services/microservices around High-Throughput, Low-Latency internet-scale platforms (social media, eCommerce, etc.), which are looking for near 100% resiliency and availability.

Why JVM Tuning Matters

As Java applications grow in complexity and scale, suboptimal JVM settings can lead to various technical challenges impacting business:

• Increased response times
• High memory consumption
• Inefficient garbage collection
• Poor thread management

These issues directly impact user experience and system stability.

JVM tuning helps mitigate these problems by optimizing resource allocation and management. These are some of the key areas for JVM Optimization, as showing in the diagram below:

(Fig 1 – Key areas for JVM Tuning)

Using AI for JVM Tuning

AI and machine learning tools are transforming how JVM tuning is approached:

• Automated Profiling and Recommendation: AI-based profilers analyse runtime behaviour and suggest optimal configurations.
• Predictive Tuning: ML models forecast performance bottlenecks based on historical data and usage patterns.
• Anomaly Detection and Recommendation: AI monitors JVM metrics in real-time to detect and respond to unusual behaviour before it affects users.

While building bespoke solutions with the above features is possible, many existing SaaS platforms offer similar AI capabilities to tune JVM.

This article will cover one such SaaS platform—Dynatrace—and provide an approach/mechanism to achieve data-driven tuning decisions supported by AI. It will also explain how Coforge can assist in this pursuit by using its in-house tools and frameworks.

How can Dynatrace assist in JVM tuning?

Dynatrace has an AI engine (Davis AI), which automatically detects anomalies, pinpoints root causes, and provides real-time insights into JVM metrics like memory usage, garbage collection, and thread activity. It enables teams to take proactive actions before performance degrades both in cloud-native and hybrid environments.

With natural language support via Davis CoPilot, users can easily query JVM metrics and automate remediation workflows.

(Fig 2 – DavidAI console in Dynatrace)

How can Dynatrace assist in “Anomaly Detection” for JVM tuning?

Depending on your needs, Davis AI offers three distinct methods for anomaly detection directly within any time series chart:

• Auto-Adaptive Threshold
  This method uses machine learning to adjust thresholds dynamically based on a rolling seven-day analysis. It continuously learns and adapts to changes in metric behavior.
  Example: If your network traffic averages 500 Mbps over the past week, the system sets adaptive thresholds around this baseline. A sudden drop below 200 Mbps or a spike above 800 Mbps would be flagged as an anomaly, helping you detect unexpected fluctuations.

(Fig 3 – Auto-Adaptive Threshold anomaly detection)

(Fig 4 – Threshold Breached)

• Seasonal Baseline
  Best suited for metrics with recurring patterns, this approach builds a confidence band using historical data to account for expected seasonal variations.
  Example: In an e-commerce setting, sales may vary by day of the week. Davis AI can compare current Friday sales to previous Fridays over the past two weeks. A significant drop would be flagged as an anomaly, signaling a potential issue.

(Fig 5 – Seasonal Baseline anomaly detection)

• Static Threshold
  This method allows you to define fixed thresholds, ideal for well-understood processes or when specific limits are critical.
  Example: If your SLA requires 95% uptime, you can set a static threshold to trigger alerts whenever uptime falls below this level, ensuring compliance with service commitments.

(Fig 6– Static Baseline anomaly detection)

How can Dynatrace assist in “Predictive Tuning” of JVM ?

Dynatrace leverages AI-driven insights to proactively optimize JVM performance. With Predictive Tuning, it continuously analyses runtime behaviour, detects anomalies, and recommends fine-tuned JVM configurations to ensure optimal resource utilization and application responsiveness.

(Fig 7 – Predictive Tuning)

JVM Tuning Best Practices

• Continuously monitor JVM metrics using APM tools.
• Profile applications under realistic workloads.
• Use AI-assisted recommendations to fine-tune GC, memory, and thread settings.
• Test changes in staging before applying to production.
• Stay updated with JVM advancements and new tuning flags.

Coforge’s Proven Approach & Methodology for self-healing through inhouse AI powered tools / frameworks

JVM related issue resolution and JVM Tuning is one of the common use cases for which Coforge has demonstrated their strong expertise with various clients in past. We’ve delivered modern use cases like AI-powered release decisioning, self-healing pipelines, and regulatory-grade compliance automation.

Leveraging our deep expertise in Platform Engineering and insights gained from working with numerous clients, we've developed a reusable framework for building Internal Developer Portals “NorthStar”

NorthStar is our three-pane internal developer portal, enabling IaC, DevSecOps orchestration, infrastructure quality validation, and adaptive performance engineering. It auto creates AI-led observability and performance engineering and manages operational efficiency.

We’ve delivered modern use cases like AI-powered release decisioning, self-healing pipelines and runbooks, and regulatory-grade compliance automation. From the AI-led observability layer, NorthStar, by default, uses the Grafana LGTM stack but also has automated scripts and an integration layer to set up observability with other industry-established tools like Datadog, Dynatrace, New Relic, Splunk, Open Telemetry, etc.

How Coforge can help in achieving JVM tuning via Dynatrace?

Coforge demonstrates strong expertise in resolving performance bottlenecks and enhancing system scalability, security, and user experience. Our deep technical capabilities ensure optimal resource utilization and improved application responsiveness. In addition, Coforge leverages observability platforms such as Datadog, Dynatrace, and New Relic to deliver comprehensive performance monitoring across cloud and hybrid infrastructures. These tools can be integrated as part of “NorthStar” adoption, which brings pre-coded SLI/SLO templates for your application, or having expertise to build observability with a ground-up approach from experts in this industry, established tools.

We proactively minimize downtime and streamline operations by harnessing AI-driven features like anomaly detection, root cause analysis, and predictive insights. Our robust integration capabilities and strategic technology partnerships enable seamless automation, real-time analytics, and superior digital experiences. This end-to-end approach empowers enterprises to drive agility, resilience, and sustained performance across their digital ecosystems.