Riverbed Technology announced its application-aware network performance management (NPM) solution can now address network and application performance issues across the three key pillars of virtualization.
New capabilities within the Cascade 10.0 release deliver the needed performance management for virtualized desktop infrastructure (VDI), server virtualization, and fully virtualized data centers using VXLAN software-defined networks (SDNs).
In addition, the Riverbed Cascade product family is now offered as a comprehensive virtual portfolio ready for deployment by service providers as well as in public, private, and hybrid clouds.
"With the spread of private clouds and virtualization, providers have been pushing for more control over the network. Current software-defined network overlay technologies being introduced by virtualization technology providers have visibility gaps, which in turn causes visibility issues for network engineers responsible for application performance troubleshooting," said Jonah Kowall, research director at Gartner. "Current monitoring and management tools must be updated and improved to see not only the traffic but the relevant additional data presented within these overlay technologies."
Cascade 10.0 provides detailed monitoring into the performance of virtualized environments, including the most popular VDI protocols, and is a NPM solution to provide visibility for VMware View (PCoIP protocol) for actions such as screen refresh, print, type, etc. These details are critical for troubleshooting and optimizing the performance of interactive applications in VDI environments.
Also supported is monitoring of the Common Internet File Systems (CIFS) protocol to ensure the performance of file sharing and collaboration applications such as Microsoft SharePoint.
The latest version of Cascade also provides performance monitoring of VXLAN SDNs by providing visibility into the UDP-encapsulated traffic and virtual tunnel endpoints.
With these VXLAN capabilities, Cascade products provide network operations teams with the ability to:
- Control and understand VXLAN virtual overlay network performance
- Monitor and troubleshoot software-defined data centers (SDDC) and the physical network in a single, easy-to-use solution
- Provide SDDC owners a singular view into their data center's performance and service level agreements (SLAs)
"VXLAN enables enterprises to deploy virtual networks with the same simplicity and operational ease that they have grown to experience with virtual machines," said Jonathan Gohstand, director, product marketing, Networking and Security at VMware. "This latest collaboration between VMware and Riverbed brings the needed integration for visibility and performance management that can allow enterprises to adopt SDNs with confidence."
The new Cascade Profiler Virtual Edition and Cascade Gateway Virtual Edition join the Cascade Shark Virtual Edition for a completely virtualized NPM solution portfolio.
Installed as a virtual machine (VM) on VMware vSphere, Cascade Profiler Virtual Edition is the centralized analysis and reporting console that integrates application and network data from Cascade Shark Virtual Edition and Cascade Gateway Virtual Edition to provide enterprise-wide, service-level monitoring. Cascade Gateway Virtual Edition collects and aggregates flow data and statistics from network infrastructure and virtual environments for delivery to Cascade Profiler Virtual Edition. The fully virtualized Riverbed Cascade product family enables managed service providers (MSPs) to easily deploy and deliver application-aware NPM as a service to their small-to-medium-sized and enterprise customers.
"With these new Cascade products, IT managers have an easy-to-use, integrated solution that empowers them to deliver the high-performing applications and networks their businesses demand," said Dimitri Vlachos, senior director of marketing and products, Cascade Business Unit at Riverbed. "IT operations can now embrace the great benefits of virtualization without giving up the visibility and control required to run today's always-on enterprise networks."
The Latest
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.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...