Riverbed Technology introduced new capabilities to its Cascade application-aware network performance management (NPM) solution, which delivers the needed visibility into virtualized data centers and data centers that use load balancing application delivery controllers (ADCs).
This new release, Cascade 9.5, extends end-to-end service performance monitoring and troubleshooting – from the remote LAN, across the wide area network (WAN), and now deeper into the data center. With this new release, the Cascade product line provides capabilities for complex, global networks that include load-balanced and virtualized data centers.
Enterprise customers deploy ADCs to optimize application delivery, flexibility and resiliency. The challenges presented by load balancing ADCs create significant visibility problems for network operations—by masking the necessary metrics and application dependencies — limiting their ability to monitor for end-to-end performance or troubleshoot degradations. Cascade 9.5 automates the discovery and monitoring of load balanced applications. By integrating with ADCs—F5 Local Traffic Manager, Riverbed Stingray Traffic Manager, and others—Cascade bridges the visibility gap between the client-side and server-side connections of load balancers, providing IT operations with a view of application performance.
In addition to deploying ADCs to optimize application delivery, enterprise customers are also virtualizing their data centers and consolidating IT resources to achieve flexibility and cost savings. However, as with ADCs, virtualization creates visibility challenges for IT operations teams because once an application enters a virtualized environment, they lose visibility into application performance. The new Virtual Cascade Shark software provides the real-time visibility needed to manage and troubleshoot application performance as it traverses the virtual switch in VMware ESX environments.
Virtual Cascade Shark monitors all inter-virtual machine traffic, exporting application-enhanced traffic metrics to the Cascade Profiler performance management dashboard, for centralized service-level discovery and monitoring and accelerated troubleshooting. It also continuously captures packets and stores them on the local server or in a storage area network (SAN) for back-in-time analysis with Cascade Pilot network analysis software.
This release of Cascade also includes a number of new features, including:
* Multi-segment analysis – Cascade Pilot simplifies the task of correlating and analyzing related traffic streams captured from multiple locations or sources to quickly identify where on the network performance issues are occurring – whether it is on the remote LAN, the WAN or within the data center.
* Precision time stamping support – Cascade Shark can now adopt nanosecond time stamps from SPAN port aggregators for greater precision and accuracy. Adopting time stamps from existing network equipment is more cost-efficient and enables coordinated time stamping across the network for performance troubleshooting. It can also be used with the new multi-segment analysis capabilities within Cascade Pilot for monitoring low-latency trading environments and other time-sensitive applications for business critical operations. Supported network taps include Gigamon GigaVUE, cPacket Networks CVU and cTap, and VSS Monitoring Distributed Traffic Capture.
* VoIP quality reporting – Cascade 9.5 tightens the integration between Cascade Shark and Cascade Profiler by bringing support for voice over IP (VoIP) protocols and quality metrics into Cascade Profiler. This information enables IT managers to determine how VoIP services are performing in conjunction with data resources in order to make effective capacity planning and optimization decisions, and improve end user experience. Supported VoIP quality metrics include mean opinion score (MOS), R-Factor, packet loss and jitter. In addition, Cascade supports the Cisco Skinny Client Control Protocol (SCCP).
Availability: The enhanced Cascade solution is expected to be generally available in Q1 2012.
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 ...