Automated Anomaly Detection: The next step for CSPs

Today’s telecom engineers are expected to handle, manage, optimize, monitor and troubleshoot multi-technology and multi-vendor networks, in a competitive and unforgiving market with minimal time to resolution and high costs for errors. With the ongoing growth in operational complexities, effectively managing radio networks, current and legacy core networks, services, and transport and IT operations is becoming a radical challenge. On the other hand, customers expect flawless service and availability, and are quick to seek other options. Operators, therefore, need to reduce manual monitoring and increase network automation. That’s why leading CSPs are already relying on advanced anomaly detection solutions as the brain on top of the OSS, giving them  a holistic view across domains for real time detection of service-impacting incidents.

Advanced anomaly detection

In the network operations context, every network generates millions of time series data, measuring all aspects of the network. Anomalies can cause service degradations and system-wide outages/incidents. Discovering these anomalies and identifying the technical root cause to fix incidents is a key objective of network operations. 

Advanced anomaly detection is all about finding and fixing incidents as they are starting to happen and before they become an incident. These advanced monitoring solutions use a Machine Learning approach to monitor 100% of data, learn every metrics’ behavior, and provide spot-on alerts on critical failures. By identifying anomalies within network big data, these solutions enable telecoms to resolve issues before they generate impacts or outages, to deliver a better customer experience, and to decrease revenue leaks and customer churn. 

ML-based advanced anomaly detection solutions help operations and NOC teams become proactive in their ability to identify service degradations and outages by providing: 

Dynamic Monitoring. AI-powered anomaly detection is more accurate. Rather than setting manual thresholds, you can use machine learning algorithms to autonomously create a dynamic baseline. They continuously analyze 100% of your network data to understand its normal behavior under different conditions and seasons, or what constitutes “business as usual”.

Real-Time Analysis. With dynamic baselines for network data, advanced anomaly detection can correlate incidents to root causes faster than traditional monitoring tools. Correlations are crucial for understanding metrics in context. Events are correlated across metrics, dimensions and other concurrent processes. The correlations established by the automated pattern discovery helps to define causality chains linking cause and effect. And once root causes are identified, real-time analysis gives you a prioritized set of opportunities to cut time to remediation.

Holistic Visibility. Advanced anomaly detection sees beyond traditional data silos. By correlating between metrics across network layers, applications, databases, storage, CRMs, monitoring and analytics tools, advanced monitoring solutions enable the fastest time to resolution, leading to improved network availability and customer experience.  

Supporting demanding applications

To keep the telecom network on track, AI-based early detection of service degradation, outages and system failures is essential. This is especially true with the industry’s massive shift to new service offerings enabled by 5G and edge computing technologies. The biggest challenge for managing 5G at scale will be its ability to support demanding applications (eMBB, mMTC, URLLC) and deliver guaranteed latency for mission-critical applications. In modern mobile networks, engineers are already overwhelmed by data, and this is only going to worsen as 5G is deployed. Bridging the gap between 4G and 5G will require incremental deployments and advanced monitoring capabilities.

Managing 4G RAN with 5G components and NFV-based networks creates another layer of complexity for network monitoring. OSS/monitoring systems are vendor- and technology-oriented in many cases. KPIs and alerts for 4G and 5G are different, requiring a steep learning curve from NOC/SOC teams. 5G adds many challenges, including the volume of data, the real-time requirement with URLLC, and the added complexity of the infrastructure and the virtualized environment. For that reason, 5G services monitoring must be based on real-time data, a more accurate view of the dynamic network and service topology, powerful diagnostics, and AI-driven predictive capabilities.

Improve performance and QoS

Advanced anomaly detection enables traditional network and service ops to integrate AI-driven automation and intelligent operations into their workflows. AI effectively augments and automates early detection, predictions and decision-making in operations and in business processes where humans can’t deal with the volume or velocity of data. Improving overall time to detect invariably leads to quicker resolution of incidents and thus results in reduced costs associated with outages, and aids in the prevention of lost revenue and brand impact. 

Since network data is so complex and dynamic, AI/ML-based autonomous solutions are critical for achieving business outcomes and avoiding blind spots. Static monitoring approaches based on dashboards and manual thresholds aren’t sensitive, robust or agile enough to withstand this challenge. AI-based anomaly detection solutions are capable of analyzing multiple dimensions of data sources, looking at cell, subscriber and device level KPIs, monitoring for faults in network equipment, and correlating alerts for noise reduction and root cause analysis. This gives engineers a transparent view of both network & Service performance and subscriber experience — at any given time.