Java Web Development (JSP/Servlets) Services

Enterprise Java applications still serve business-critical processes but are becoming vulnerable to changing security threats and regulatory demands. Traditional compliance-based security methods tend to respond to audits or attacks, instead of stopping them. This paper introduces a risk-based security architecture, which focuses on protection according to the impact of the business, the probability of the threat, and exposure. The threat modeling, dependency risk analysis, and layered security controls help organizations to minimize the attack surfaces beforehand without impacting on performance and delivery velocity. The strategy is explained with the help of real-life examples of enterprise Java to facilitate its use in practice.

Intended Audience

The audience targeted in the article is those an enterprise architect, senior Java developer, security architect, and DevSecOps teams who are required to design, modernize or secure large-scale Java applications. In recent years, there are a number of breaches of enterprises that have not been initiated by a zero-day exploit but a known vulnerability, which has not been prioritized e.g. an outdated library, an open API, or a poorly configured integration In a number of instances, the organizations were technically compliant but still exposed because of the homogenous, checklist-driven security measures that did not concentrate on the high-risk elements. 

Memory tuning in Java has evolved over years and whenever each version was released, we anticipate some magic. If you worked with Java 6 or 7, you probably remember spending hours tweaking PermGen, experimenting with CMS flags, and nervously watching GC logs in production. But with Java 25, Memory Optimization and Utilization are more mature.

Modern Java gives us better garbage collectors, improved container awareness, stronger tooling, and smarter runtime ergonomics. But despite all that progress, memory optimization is something that you can't ignore. In a cloud-native environment where every gigabyte costs money, memory efficiency directly affects both performance and money spent on infrastructure as well.

In this article I am trying to summarize some of the best practices for memory utilization, so developers can use it as a reference guide.

1. Start with Measurement, Not Assumptions

The most common mistake that we could usually see is increasing heap size without understanding allocation patterns. A bigger heap often delays a problem rather than solving it.

In the previous articles, we discussed how to perform data-driven API automation testing with different approaches, including object arrays, iterators, CSV files, and JSON files.

An Excel file can also be used to perform data-driven API testing. It allows testers to store multiple test data in one place, where we can easily add, update, or remove test cases without changing the automation code. It allows non-technical members, such as Business Analysts and Product owners, to understand and edit the test data to perform robust testing.

"Modernize the legacy stack" is a phrase that strikes dread into every senior engineer's heart — and for good reason. Migration projects fail at a notoriously high rate. They balloon in scope, break running systems, and produce tech debt that rivals what they replaced. I led successful migrations of critical microservices to modern runtimes, containerized deployments, and event-driven architectures — on time, without downtime, and with measurable gains in performance and reliability.

This article distills the frameworks, patterns, and hard lessons from those engagements into a practical guide for teams facing similar challenges.

In the first part of this two-part series on tuning Java applications for Ampere®- powered cloud instances, we concentrated on tuning your Java environment for cloud applications, including picking the right Java version, tuning your default heap and garbage collector, and some options that enable your application to take advantage of underlying Arm64 features. In this article, we will look more closely at the operating system and Kubernetes configuration. In particular, we take a deep dive into container awareness in recent versions of Java, how to restrict the system resources made available to Java containers, and some common Linux configuration options to optimize your system for specific workloads. Much of the advice related to operating system tuning and workload placement applies to all workloads, not just JVM workloads, but since our focus is on the deployment of Java applications on Arm64 to Kubernetes, we will focus on that use-case here.

Resource Allocation in Kubernetes

In this section, we’ll step outside the JVM and look at the infrastructure layer. Understanding how Kubernetes allocates resources, and how your Java application perceives those allocations, is fundamental to ensuring that you allocate the right amount of resources to your JVM.