BT’s Martini automation platform applies machine learning to many areas of OSS.
BT's automation platform boosts network operations
BT’s Martini automation platform applies machine learning to many areas of its operational support system (OSS). It ingests multiple types of data from numerous sources, runs them through analytical models, and recommends remedial actions. They can be acted on automatically, with closed-loop remediation. Martini also helps with planning and provisioning network capacity, factoring in local demand and market forecasts which was not possible with previous generations of automation which used a country-wide average for demand levels.
The challenge to operational teams is growing. Customers expect always-on services with more features, and networks and services are becoming more complex with substantial growth in traffic year on year as services consume more resource (such as the shift from 4K to 8K for HD video).
Previously, automation typically processed data periodically in a batch rather than in near real-time. It was characterized by manual fallouts and hardcoded, inflexible rules that on occasion created inefficiencies. For example, Martini highlighted instances where older automation continuously switched services between two paths while attempting to balance the network, incurring unnecessary cost on multiple service oscillations.
Martini analyzes 3.5TB of data daily from real-time data sources like streaming telemetry and alarms, and batches, such as call data, that include more than 10 types of data and sources. Its analytical model has identified major service outages that before were masked by normal user behavior. For example, an outage impacting broadband services in the core network was severely restricting traffic’s flow through a port. As this was in the early hours of the morning, low levels of traffic are the norm and previously would not have been detected.
The model also automatically generates a case in less than half the time taken before Martin was deployed and by tracking deviations in ‘calls attempted’ and call connected’ data picks up from the baseline profile for voice services, as well as anomalies in the ratio of ‘attempted’ to ‘connected’ calls.
Information framework (SID)
Open APIs
Open Digital Architecture (ODA)
Autonomous networks toolkit