Calnex Solutions (LSE: CLX) and VIAVI Solutions (Nasdaq: VIAV) have expanded a partnership that combines network emulation, traffic generation and performance-testing products for AI, data center and enterprise deployments. The companies said the combined architecture is meant to let engineering teams recreate production-like network conditions and measure the effect on applications and user experience before a service goes live.
The pairing brings together Calnex’s SNE, SNE-X and SNE-Ignite network-emulation products with VIAVI’s ONE LabPro, TestCenter and CyberFlood platforms. According to the companies, the combined tools can generate application and user traffic, introduce network impairments and measure resulting network and application behavior in a single testing design.
That is relevant to automation and operations because deployment validation often depends on more than a basic throughput test. AI clusters, distributed applications and data center services can be sensitive to latency, jitter, packet loss and changes in traffic patterns. A test environment that can simulate those conditions may help teams discover a configuration or capacity issue before it affects users, although the companies did not release independent results showing how the combined architecture changes deployment outcomes.
The companies said the expanded arrangement is aimed at AI, data center, defense, satellite communications and enterprise markets. It builds on their existing work around Open RAN testing, where they have combined products for protocol, signal and timing validation. The announcement does not identify a new integrated product name, a common management interface or a date when customers can buy the combined configuration as a packaged offering.
Calnex said many validation environments do not adequately mirror real-world network conditions, a vendor assessment that underpins the expansion. VIAVI said realistic traffic generation and network emulation can provide a foundation for performance assurance. Those are the companies’ positions, rather than independently verified comparative findings.
For network and platform teams, the practical use case is pre-deployment testing of the path between an application, its users and the infrastructure that connects them. Traffic generation can model demand, while emulation can introduce conditions such as delay or loss and measurement tools can capture the impact. The value of that approach depends on whether the test design actually represents the organization’s workloads, protocols, security controls and production topology.
The announcement also does not make a claim about automated remediation or production monitoring. This is a validation and test-stack expansion, not an operations platform that acts on live network events. Teams will need to assess separately whether results from the lab can be connected to their existing observability, change-management and incident-response workflows.
Still, the partnership addresses a recurring operational constraint: newer infrastructure is difficult to validate when test environments lack realistic traffic and impairment conditions. If the companies’ combined architecture is adopted, it could give engineering teams a broader way to test AI and data center services before rollout. The critical evaluation will be integration effort, fidelity to production conditions and whether findings meaningfully reduce post-deployment performance issues.
Change-control practices will remain important even when a pre-deployment test is comprehensive. Production environments can differ as routes, software versions, workloads and security policies change. Teams therefore need repeatable scenarios, documented baselines and a process for rerunning validation when a material infrastructure or application change occurs.