Virtana has been granted two new patents for the use of artificial intelligence (AI) and machine learning (ML) application dependency mapping, cloud migration, and cost optimization.
The first patent introduces an innovative method for the automated grouping of related workloads at the data layer, a critical enhancement for monitoring and observability in complex IT infrastructures. This innovation simplifies migration planning and provides a deeper, AI-powered insight into application interdependencies and behaviors, which is vital for proactive monitoring and performance optimization.
The second patent utilizes AI to precisely determine cloud configurations and cost estimates, which is essential for effective workload migrations. This AI-driven approach ensures optimal resource allocation and financial planning for cloud migrations, ensuring clients have an integrated view of cost, performance, and resource allocation.
These patents fortify Virtana's IP portfolio, which is centered on leveraging AI and ML to address the evolving challenges of hybrid cloud visibility, application monitoring, and infrastructure observability faced by enterprises globally.
"These patents symbolize Virtana's dedication to advancing the field of IT infrastructure monitoring and observability through intelligent automation and AI-driven insights," said Kash Shaikh, President and CEO of Virtana. "Our innovations in application dependency mapping, cloud migration, and cost optimization are not just add-ons but are central to our core monitoring solutions, providing our clients with a comprehensive and efficient toolset for managing complex IT environments."
The patents enhance Virtana's solutions to address the needs of modern systems, which include a blend of open-source, in-house, data center, and cloud-based components. By dynamically understanding these elements and their interrelations in real time, Virtana's technology provides an essential layer of intelligence for monitoring and observability, aiding in the quick identification and resolution of anomalies and issues.
For enterprise IT leaders, this means visibility and informed decision-making, accelerating operational flexibility and simplifying cost management in moments ranging from routine operations to complex cloud transitions. These patents also answer critical questions regarding the operational aspects of applications, including their locations, functions, and associated costs.
The Latest
I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...