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Gigamon and JDSU Announce Integration of Software Defined Visibility APIs

JDSU is developing closed loop integration plugins that support Gigamon’s Software Defined Visibility, a framework that allows customers, security and network equipment vendors, as well as managed service providers, to control and program Gigamon’s Visibility Fabric via REST-based APIs.

“We are excited to be working with Gigamon to integrate with APIs that support Software Defined Visibility,” said Charles Thompson, senior director, Product Line Management for JDSU. “With this integration, we envision a multitude of possibilities where our customers can automate traffic visibility, so that they can focus their resources on other mission-critical activities.”

There are several use cases in which JDSU plans to utilize Software Defined Visibility. The following will be presented at Cisco Live, Cisco’s annual IT and communications conference:

- Session-based Filtering – JDSU will take advantage of Gigamon’s Adaptive Packet Filtering capabilities to dynamically drop unwanted ‘streaming’ traffic such as Netflix or YouTube, thereby reducing the demand on monitoring appliance storage capacity.

- IPv4 and IPv6 Traffic Filtering – As more networks begin deployment of IPv6 along with legacy IPv4 infrastructure, JDSU expects customers will need to selectively filter v4 and v6 traffic. Leveraging the intelligent filtering of the Gigamon Visibility Fabric, network administrators can rest assured knowing that they have comprehensive visibility and a future-proof path to simplify IPv6 adoption without creating new blind spots.

= Reduce ‘Mean Time To Resolution’ – To assist with faster diagnosis of traffic anomalies, the ability to enhance captured packets that are subsequently stored on JDSU’s GigaStor appliances will improve diagnosis accuracy and focus. Through the addition of Gigamon’s “Flow Mapping” meta-data and “network neighbor” discovery information, Network Operators will be able to evaluate network incidents more effectively and isolate issues to specific segments of the network infrastructure.

“Having partners like JDSU on board and adopting our recently released APIs accelerates our Software Defined Visibility market momentum,” said Ananda Rajagopal, VP of Product Line Management at Gigamon. “Not only is JDSU at the forefront of adoption, they are validating the market need for pervasive and active visibility. Their API-based development demonstrates the true power and flexibility that a programmatic framework can provide to quickly and automatically react to changes in network conditions.”
Software Defined Visibility – Enabled by GigaVUE-FM Fabric Manager

GigaVUE-FM 3.0 Fabric Manager enables Software Defined Visibility. It delivers a single pane-of-glass view of both physical and virtual nodes across the Visibility Fabric, while providing an easy-to-use, wizard-based approach for configuring Flow Mapping and GigaSMART traffic policies. A single instance of GigaVUE-FM can manage hundreds of visibility nodes across multiple locations delivering more than a quarter of a million physical and virtual ports.

Flow Mapping technology reduces traffic and improves analyzer performance. Flow Mapping is a patented technology at the heart of Gigamon’s GigaVUE Visibility Fabric nodes that takes line-rate traffic at 1Gb, 10Gb, 40Gb or 100Gb from a network TAP or a SPAN/mirror port (physical or virtual) and then optimizes flows based on individual traffic profiles of the tools and applications that secure, monitor, and analyze the network infrastructure.

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Gigamon and JDSU Announce Integration of Software Defined Visibility APIs

JDSU is developing closed loop integration plugins that support Gigamon’s Software Defined Visibility, a framework that allows customers, security and network equipment vendors, as well as managed service providers, to control and program Gigamon’s Visibility Fabric via REST-based APIs.

“We are excited to be working with Gigamon to integrate with APIs that support Software Defined Visibility,” said Charles Thompson, senior director, Product Line Management for JDSU. “With this integration, we envision a multitude of possibilities where our customers can automate traffic visibility, so that they can focus their resources on other mission-critical activities.”

There are several use cases in which JDSU plans to utilize Software Defined Visibility. The following will be presented at Cisco Live, Cisco’s annual IT and communications conference:

- Session-based Filtering – JDSU will take advantage of Gigamon’s Adaptive Packet Filtering capabilities to dynamically drop unwanted ‘streaming’ traffic such as Netflix or YouTube, thereby reducing the demand on monitoring appliance storage capacity.

- IPv4 and IPv6 Traffic Filtering – As more networks begin deployment of IPv6 along with legacy IPv4 infrastructure, JDSU expects customers will need to selectively filter v4 and v6 traffic. Leveraging the intelligent filtering of the Gigamon Visibility Fabric, network administrators can rest assured knowing that they have comprehensive visibility and a future-proof path to simplify IPv6 adoption without creating new blind spots.

= Reduce ‘Mean Time To Resolution’ – To assist with faster diagnosis of traffic anomalies, the ability to enhance captured packets that are subsequently stored on JDSU’s GigaStor appliances will improve diagnosis accuracy and focus. Through the addition of Gigamon’s “Flow Mapping” meta-data and “network neighbor” discovery information, Network Operators will be able to evaluate network incidents more effectively and isolate issues to specific segments of the network infrastructure.

“Having partners like JDSU on board and adopting our recently released APIs accelerates our Software Defined Visibility market momentum,” said Ananda Rajagopal, VP of Product Line Management at Gigamon. “Not only is JDSU at the forefront of adoption, they are validating the market need for pervasive and active visibility. Their API-based development demonstrates the true power and flexibility that a programmatic framework can provide to quickly and automatically react to changes in network conditions.”
Software Defined Visibility – Enabled by GigaVUE-FM Fabric Manager

GigaVUE-FM 3.0 Fabric Manager enables Software Defined Visibility. It delivers a single pane-of-glass view of both physical and virtual nodes across the Visibility Fabric, while providing an easy-to-use, wizard-based approach for configuring Flow Mapping and GigaSMART traffic policies. A single instance of GigaVUE-FM can manage hundreds of visibility nodes across multiple locations delivering more than a quarter of a million physical and virtual ports.

Flow Mapping technology reduces traffic and improves analyzer performance. Flow Mapping is a patented technology at the heart of Gigamon’s GigaVUE Visibility Fabric nodes that takes line-rate traffic at 1Gb, 10Gb, 40Gb or 100Gb from a network TAP or a SPAN/mirror port (physical or virtual) and then optimizes flows based on individual traffic profiles of the tools and applications that secure, monitor, and analyze the network infrastructure.

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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.