cPacket Networks announced two new products designed to deliver ultra-low-latency, high-performance, high-density 100Gbps network observability in support of the latest enterprise automation, data center consolidation, and high-performance computing requirements.
Building on cPacket’s existing 100Gbps portfolio, the new cVu® 32100 and cVu 32100E network packet broker+ allow enterprises to acquire, aggregate, observe, and reliably deliver network packet data to IT performance and security tools. Equipped with advanced features with the right economics and scale, these new products provide the best solution for ultra-low-latency monitoring and packet brokering at the same time. This results in faster transaction velocity, better user experience, lower mean-time-to-resolution, and reduced customer churn.
“As we announced the new cStor® 100 appliance a few weeks ago, our financial services and other customers expect a complementing monitoring and brokering solution to meet their demands during the most challenging times. The new cVu 32100/E completes the solution and addresses key requirements in the most challenging environments across the most demanding industries,” said Brendan O’Flaherty, CEO of cPacket Networks.
Together, these products give organizations the deep, real-time, and actionable insights they need to align with industry trends around 100Gbps migration, high-performance but low-latency computing, data center consolidation, and digital transformation.
The cVu 32100/32100E packet brokers extend the role of the existing flagship cVu 16100NG packet broker+ in a scalable 2-tier monitoring fabric architecture by offering 32 ports of 100/40Gbps or 128 ports of 25/10Gbps in a compact 1RU size. The cVu 32100E (enhanced) is specifically designed for ultra-low-latency monitoring applications, delivering nanosecond timestamping with integrated real-time analytics, combined with wire-speed ingress and egress processing at each physical port for unrivaled accuracy, performance, and reliability.
- Built for high-performance computing, financial services, and other intensive workloads. Just like the cVu 16100NG packet broker+, the cVu 32100E packet broker+ is a unique 2-in-1 solution. It is a performance monitoring tool as well as a data delivery broker and reduces the costs and complexity of monitoring architectures. It includes specific additional features for performance monitoring of ultra-low-latency applications such as high-frequency trading, market data gap detection, medical digital imaging, 3D and video animation, healthcare, oil and gas exploration, pharmaceuticals, and other high-performance computing (HPC) applications. Those features include:
* Monitoring with “cBurst” microburst characterization for application-based network utilization, bandwidth capacity planning, and tool utilization
* High-resolution counters that provide full 1-second snapshots of throughput with millisecond resolution
* High-precision timestamping for consistent metrics analysis and fastest transaction velocity
* Cost-effective TAP/SPAN aggregation and multi-speed tool distribution, and key brokering features in a 2-tier architecture with the existing NG-series.
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 ...