PULSensor appliances (virtual and physical) are designed for active and affordable network-wide probing and performance tracking. Data they provide is used for active network assurance, proactive operations, and automation. They are managed with the cutting edge PULScore performance management system.
Value of active network performance tracking data increases as the level of automation and complexity increases. But there are strongholds for both. Traditional passive performance tracking is useful for detecting large-scale issues that affect masses of customers through signaling data or statistical analysis of a specific part of the network.
Passive assurance and performance tracking are typically done “passively” in real-time for up-and-running and well-set networks. The intention is to monitor and keep constantly the performance within the acceptable and averaged pre-set parameters on the level. AI/ML is used to analyse and correlate big data and provide insights for users serving customers and operating the networks. Reactive, passive assurance and performance tracking suits perfectly networks and services that are rather homogeneous and targeted for standardised, mass services.
Active assurance and performance tracking are used to simulate and mimic the actual network experience. In today’s cloud-based, virtually limitless, and very agile networks proactive assurance and automation are the new normal. Active data is mandatory for that. Being very affordable (compared to passive) it suits better for network-wide deployments. One of the benefits is also the capability to actively track performance even when there is no live data available like in case of many critical communication networks and when designing, testing and simulating services before they are activated.
AI/ML is used for powering closed-loop automation and to be able to set, monitor, and track intent-based SLAs and QoE for individual customers with better confidence.
Quote from our client
"Creanord’s solution gives us accurate statistics and visibility, helping us determine where we are nearing the limits about bandwidth availability. Our engineering team can now also target their time and efforts better, and act in a more proactive way"