Riverbed Technology announced that Riverbed SteelCentral, the command center for application performance, is extending its comprehensive performance monitoring and management solution to address emerging blind spots that companies realize as they manage technologies such as mobile and cloud.
To meet these new requirements, Riverbed is introducing a SteelCentral SaaS solution to provide cloud-based performance management and extending monitoring to any endpoint with SteelCentral Aternity.
“Technologies like cloud and mobile are experiencing rapid adoption by enterprises to improve agility and flexibility, and are altering the IT landscape. Traditional approaches to monitoring availability and performance need to adapt to these technologies,” says Mike Sargent, SVP and GM of SteelCentral, Riverbed Technology. “This release will enable companies to leverage these disruptive technologies while eliminating key blind spots, and help companies proactively manage the overall end-to-end performance of their critical applications.”
For complete visibility in or outside the cloud, Riverbed introduces SteelCentral SaaS that integrates end-user experience and application monitoring with network performance monitoring. As companies continue to lean more heavily on the cloud to help them compress application release cycles and reduce costs, their ability to maintain unified visibility for multi-tier applications is challenging. Traditional APM tools are completely blind to network performance issues that often affect performance of cloud-hosted applications. As a SaaS solution, SteelCentral will provide application, end-user and network performance visibility, giving companies a complete monitoring solution for cloud-based applications.
With Riverbed’s recent acquisition of Aternity, a provider of end user experience (EUE) monitoring solutions, Riverbed will extend SteelCentral to include EUE monitoring of all applications running on physical, virtual, or mobile devices. With the addition of SteelCentral Aternity, SteelCentral can gather data from the broadest range of sources, including app servers, application code, UC systems, LANs, WANs, browsers, and end-user devices, enabling customers to quickly find and fix performance problems, no matter where they occur.
In addition, Riverbed is integrating SteelCentral visibility into SteelConnect 2.0, the Company’s new application-defined SD-WAN (software-defined wide area network) solution built for the cloud world. SD-WAN is rapidly gaining momentum as companies seek to provide fast, agile and secure delivery of applications across hybrid cloud environments; however, understanding traffic, both before and after any updates, creates blind spots for IT. Unlike other SD-WAN companies that leverage third party relationships to address this visibility issue, Riverbed is announcing the availability of SteelCentral NetProfiler for SteelConnect 2.0, Riverbed’s new application-defined SD-WAN solution build for the cloud world. This offering leverages REST APIs and SteelCentral SteelFlow data to provide visibility into usage and availability for sites, servers, applications, and users. In addition, easily accessed reports give IT Managers detailed information about sites, links, applications and users. With these capabilities, IT Ops managers will be better able to make informed policy and deployment decisions, monitor and troubleshoot performance issues, and plan for network changes regardless of whether the application’s performance is modified across hybrid WANs, remote LANs or cloud networks.
SteelCentral SaaS is currently available in beta to select participants. It is targeted to be generally available in December 2016.
SteelConnect 2.0 that integrates SteelCentral is targeted to be available starting October 2016, with feature enhancements released throughout the remainder of the year. SteelConnect will be fully available through the Riverbed partner network. SteelCentral Aternity solutions are currently available.
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.