I’m excited to announce that today we’re changing our company name to Virtana. Over the past three years, we have been steadily reinventing Virtual Instruments. You will still find the same deep infrastructure expertise, the same commitment to mission critical workloads, but now our portfolio offers a lot more breadth across the data center and into the public cloud.

Virtual Instruments today announced that it has become Virtana, a company focused on providing customers with the industry’s most robust AI-powered hybrid IT infrastructure management platforms for mission-critical workloads. Underscoring this commitment to helping enterprises manage their complex multi-cloud environments, today Virtana introduced CloudWisdom, a SaaS-based cloud cost optimization and monitoring platform.

Over the last decade, DevOps has become an important part of software engineering culture, influenced by the wide adoption of microservices, containers and cloud computing. A recent step in the evolution of cloud-based and microservice architecture is the serverless computing – a code execution model where the cloud provider takes total responsibility for the operating system and hardware management.

Competition is a funny thing. To gain or keep an edge against your competitors, you may need to push the envelope to use newer technologies and processes. But done wrong, that very technology or process could lead to your ruin. This is where we are with DevOps. The methodology that became popular in the mid-to-late 2000s is embraced by many companies . Done the right way, it will help your organization challenge its competition to keep up with customer and user demands.

Not long ago, we talked about the need to maintain visibility across all of your multicloud investments so you can keep track of how well they are working for you as well as how much they are costing you. Historically, this has meant building reports on numbers of VMs in use, system utilization, storage consumption, and the like. But much has changed since the early days of hypervisors, and organizations creating cloud-based services today are moving quickly toward serverless models.

Windows Support went GA for Kubernetes in version 1.14 and represented years of work. This has been the effort of excellent engineers from companies including Microsoft, Pivotal, VMWare, RedHat, and the now-defunct Apprenda, among others. I’ve been a lurker and occasional contributor to the sig-windows community going back to my days with Apprenda, and I’ve continued to follow it in my current role with Rancher Labs.

What is the bare minimum you need to build, test and run your Java application in Docker container? The recipe: Create a separate Docker image for each step and optimize the way you are running it. I started working with Java in 1998, and for a long time, it was my main programming language. It was a long love–hate relationship. During my work career, I wrote a lot of code in Java. Despite that fact, I don’t think Java is usually the right choice for microservices.

It has taken much longer than usual for the CentOS team to provide us with a new version of their operating system; however, the wait is over. The new CentOS 8 is here. CentOS, or Community ENTerprise Operating System, is a binary-level clone of the RHEL (Red Hat Enterprise Linux) distribution that can be accessed for free. For those unfamiliar with Red Hat, Red Hat offers open source, enterprise-oriented software solutions with enterprise-level support.

Chaos engineering (CE) is the discipline of experimenting on a system in order to build confidence in the system’s capability to withstand turbulent conditions in production. This approach is becoming commonplace in software development and operations (DevOps) practices. But how would its application extend to ITOps? CE for ITOps offers a similar framework for stress-testing a technology platform to understand its weak points and performance pitfalls under heavy pressure.

Debugging performance issues in production can be a pain and, in some cases, impossible without the right tools. Java profilers have been around forever, but the profilers most developers think about are only one type: standard JVM profilers. However, using one type of profiler is not enough. Suppose you’re analyzing your application’s performance. There are multiple profiling activities which you may execute.