Network & Service Operations
Improve Operational Efficiency for Better Customer Experience (CX) & Lower OPEX with AI/ML-driven Operational Analytics
Guavus solutions use advanced algorithms and scoring techniques, based on artificial intelligence (AI) and machine learning (ML), to dramatically reduce the mean-time-to-acknowledge, diagnose and resolve network incidents for faster alarm triage with greater network uptime and service availability. Even without network alarms, CX-impact scores automate incident resolution.
Most Popular Use Cases
for Network & Service Operations
Root Issue Analysis & Fault Localization
Are you able to rapidly and proactively isolate and localize service-impacting network faults in a multi-vendor dynamic virtualized service-delivery environment that generates massive volumes of alarms and telemetry?
Localizing a fault, engaging the right fix team and resolving the problem are delayed due to large, disparate, dynamic, virtualized network infrastructures, where valuable time is spent understanding and dismissing a wide range of symptomatic alarms while service quality-of-experience (QoE) and CX suffer.
Automatically group and consolidate alarms associated with the same likely fault into clusters, then identify the true root alarm to rapidly resolve the core issue without relying on network topology.
Significant Network Events / Routing Analytics
Without network alarms to rely on, do you know when your subscribers are impacted by routing/topology changes made by peer IP networks or content providers?
Subscribers sometimes experience degraded network performance due to routing changes before their CSP is aware of it, because there are no alarms for CX-impact problems that originate from routing updates.
Correlate application performance fingerprints through cross-layer analysis of QoE and related KPIs with significant peer routing/topology changes or events to calculate CX-impact scores, which are easily searched during troubleshooting.
Alarm Prioritization from Incident Prediction
Do you know which alarms have a high probability of leading to network incidents and should be prioritized due to likely subscriber QoE issues?
Physical/virtual network-generated alarm volumes and dynamic topologies make it increasingly difficult to find and fix alarms associated with poor subscriber QoE. Alarms with a high probability of leading to network incidents aren’t quickly resolved, causing high operational costs and long delays in resolving negative QoE-impacting incidents.
Network alarms with a high probability of leading to network incidents are automatically prioritized/escalated, so that the highest risk alarms predicting subscriber QoE problems are trouble ticketed and more efficiently resolved without relying on network inventory, topology or static rules.