
Gigamon announced the upcoming release of a suite of solutions offering Active Visibility for high-volume traffic in both scale-out and scale-up architectures for next-generation business infrastructures.
Data center networks are experiencing increasing levels of east/west traffic from virtualization and distributed applications, which is driving higher traffic volumes from the leaf to the spine, spurring migration to 40Gb and 100Gb network connectivity. At the same time, data center administrators are moving to monitoring “below the spine switch” to provide visibility for security, operations monitoring and performance monitoring, increasing the number of points from which data has to be acquired. These considerations are directly influencing the most appropriate design and architecture to deliver essential visibility to network traffic and ‘virtual-machine to virtual-machine’ communications.
“We’re seeing an increase in distributed mission-critical applications driving more east/west traffic below the spine of next gen data centers as well as an increase in the desire to gain a more pervasive view of network and VM-VM activity,” said Ananda Rajagopal, VPof Product Line Management at Gigamon. “Unlike repurposed Ethernet switches that can only offer narrow visibility with no traffic intelligence, our new Visibility Fabric capabilities address higher traffic volume, allowing the best combination of cost, scale, reach and intelligence to be simultaneously achieved by administrators seeking uniform and pervasive visibility across their physical and virtual infrastructure.”
The upcoming new enhancements to the Gigamon Visibility Fabric include:
- Clustering capabilities for the GigaVUE-TA1 Traffic Aggregation nodes, enabling multiple nodes at the edge to act as a single logical unit when combined with an intelligent GigaVUE-H Series node at the core, for seamless extension of visibility to the edge. This extends the premium functionality of the Fabric all the way to the low-cost edge device and allows the GigaVUE-TA1 to be managed in an identical manner to the intelligent H Series node. This market-first architectural approach to visibility avoids overloading the operational and security tools that need access to the traffic.
- The addition of 40Gb BiDi TAPs, offered in compact 1 RU and 3 RU enclosures, and 40Gb BiDi optics into the Gigamon portfolio. This solution is ideal for customers considering a cost-effective approach upgrade from 10Gb to 40Gb using BiDi.
- Combination of multiple 100Gb and 10Gb ports on a single blade allowing a higher degree of network and tool port consolidation to be achieved. The new 2-port 100Gb + 8-port 10Gb combination blade will be available on the GigaVUE-HD4 and GigaVUE-HD8 platforms.
- Gigamon’s Fabric Manager, GigaVUE-FM, will offer fabric-wide reporting capabilities, summarized and customizable dashboards, enhanced reporting to visualize the most/least utilized network/tool ports and traffic maps across the Visibility Fabric, audit trail support for security compliance and the ability to export reports for offline review.
- Intelligent traffic visibility solution to automatically discover the topology of a connected network running Cisco Discovery Protocol (CDP) or Link Layer Discovery Protocol (LLDP). This unique capability allows automatic trace-back of network interfaces detected to be at fault by the Visibility Fabric and drastically cuts down mean time to resolution.
“These enhancements are a tremendous development in the monitoring fabric space. Extending visibility deeper into the datacenter will provide great information on demand,” said Ted Turner, Senior Network Engineer at Intuit Inc. "Extending the management fabric will increase the capabilities of all organizations across all levels of application, performance and security monitoring.”
These features and capabilities are available to be ordered now and will be generally available this quarter.
The Latest
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.