Netuitive is previewing its enhanced virtual data center dashboard available in the next release of the software scheduled for Q2.
Netuitive’s new virtual infrastructure dashboard provides a unified, real-time view of performance and capacity of an entire virtual data center delivering rich cross-platform insight across VMs, hosts, storage, and networks, in a single screen.
Netuitive eliminates manual, rules-based approaches with advanced mathematics and predictive analytics that automatically correlates and self-learns the operational behavior of systems and applications across an entire IT environment. By taking this holistic approach, Netuitive improves visibility across applications, platforms and vendors, and because its’ learning is adaptive, it excels in dynamic, virtualized environments. This includes the ability to interchange hypervisor licenses across Microsoft Hyper-V, Xen Hypervisor and VMware.
In addition to providing an overall picture of performance and capacity management in virtual infrastructures, Netuitive correlates end user experience and application metrics based on real-time data provided from existing monitoring tools such as VMware, Microsoft, BMC, IBM Tivoli, CA (Wily), HP, NetApp, Oracle, Compuware (Gomez), etc. Data is collected and normalized in Netuitive’s integration hub, analyzed by Netuitive’s predictive IT analytics engine, with actionable outputs delivered based on the analysis. The new dashboard graphically shows this analysis in a simplified view based on health and workload of the IT environment. From here, IT workers can easily drill down for details in particular areas or respond to alarms that have been triggered from the comprehensive analysis.
Categorized as “transformational” by Gartner, Behavior Learning technology, which is at the core of the Netuitive predictive analytics software, is being recognized as a key advancement for overcoming major performance issues connected to virtualization management and cloud computing.
The Latest
As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...
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 ...