Viavi Solutions announced the availability of NITRO® AIOps on Google Cloud, creating a solution that leverages VIAVI network analytics solutions and Google Cloud's native service capabilities.
The collaboration aims to address critical challenges faced by Communication Service Providers (CSPs) and unlock new opportunities for network intelligence and optimization.
NITRO AIOps empowers CSPs to evolve their legacy NOC to Dark NOC and delivers end-to-end network visibility across a wide range of network platforms, including those for cloud and IoT.
The critical challenges addressed by NITRO AIOps include:
- Network Performance Optimization: NITRO AIOps enables CSPs to proactively monitor and optimize network performance. By identifying potential bottlenecks and anomalies, CSPs can take timely actions to ensure seamless network operations and consistent, high-quality service delivery.
- Enhanced Customer Experience: With granular insights into customer behavior and usage patterns, the solution allows CSPs to tailor services to individual preferences. This results in personalized offerings, improved customer satisfaction, and increased loyalty.
- Capacity Planning and Resource Management: The solution's AI-driven analytics aids CSPs in effective capacity planning and resource management. By optimizing network resources, CSPs can achieve better resource utilization and cost efficiency.
- Real-Time Anomaly Detection: AIOps empowers CSPs with real-time issue detection, enabling swift response to network anomalies and potential service disruptions, minimizing downtime and enhancing network reliability.
- Predictive Maintenance and Quality Assurance: By predicting potential network issues and faults, the solution helps CSPs adopt a proactive approach to maintenance and quality assurance, ultimately leading to improved service quality and reduced operational costs.
“VIAVI constantly strives to provide innovative solutions and purposeful industry partnerships that address emerging opportunities in our markets. This partnership enables the transition to cloud-based networks with end-to-end visibility. Working with Google Cloud, we are pleased to offer critical problem-solving solutions for service providers as part of their native cloud services,” said Deepak Shahane, VP and GM, Service Enablement, VIAVI. “As this partner demonstration shows, we aim to enable the industry to leverage VIAVI capabilities to improve operations and management that includes fully validated, provisioned, and observable networks in any use case.”
VIAVI NITRO AIOps offers a comprehensive suite of advanced analytics tools tailored for CSPs. With real-time data collection and analysis, the solution provides deep insights into network performance, customer behavior, and service quality. Utilizing advanced algorithms and machine learning, NITRO AIOps empowers CSPs to make data-driven decisions, optimize their networks, and enhance customer experiences.
Beyond AIOps, the VIAVI portfolio includes a growing line of cloud-hosted and cloud-enabling solutions that are unmatched in breadth and help keep customers’ business moving before, during, and after migration to the cloud. This includes the most comprehensive test suite on the market for lab validation, field deployment and service assurance of O-RAN networks, Fusion automated testing, Private Network Intelligence, the ONMSi remote fiber test system, and Observer network performance monitoring and diagnostics. Many of these solutions are enabled by the Network Integrated Test, Real-Time Analytics and Optimization (NITRO) platform and are available today directly and through critical partners such as Google Cloud.
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.