When thinking about an operator’s network operation center (NOC), imagine the control room at NASA or the situation room at the White House. The network operation center is the nerve center for operator activities. Whether it monitors system health, service delivery, alarm notifications, or other activities, keeping the NOC running efficiently is critical. Traditionally, this requires many human resources. This also means that OPEX is high, and there is a higher risk of human error. How can operators insulate themselves against rising costs, increase demand for lower time to resolve problems, and increase the need to predict and proactively avoid issues impacting user experiences?

NOC challenges

Running a NOC is a challenging feat. There are several hurdles to contend with:

• The operator environment is becoming increasingly complex: with multiple types of technologies and many different services, some provided by the operator and others being over-the-top services and applications

• Customer expectations are incredibly high, and subscribers can quickly churn

• Data volume continues to grow exponentially: with no manual analysis able to handle this efficiently.

• Dealing with alert storms causing an increase in manual workload

• Using low efficiency and multiple tools for day-to-day issues

• Long alert response times and delayed resolutions

The answer to these is virtualization and automation. Moving the NOC to a virtual solution powered by AI/ML analytics and automation has several key advantages. Automation creates the ability to handle increased network complexity and fix degradations in real time. Rather than having an engineer check each specific alert and then spend time working on how to fix it, an automated solution can pick up on trends and initiate alerts and resolutions much more quickly. Once programmed, the engineer must only oversee the system and actively get involved when there is a need for a physical intervention that a software-based solution cannot activate.

When automating the NOC, operators can move from a reactive to a proactive approach to managing the network. Reactive is when an alert is triggered, and the platform or a human acts if such intervention is needed. A proactive approach allows operators to monitor and optimize their networks before a problem impact customers. Operators need to make use of both methods. A cloud-native virtualized system can predict and prevent trends and anomalies before they impact many end users. This helps reduce customer churn and is an excellent way to increase revenues with customer loyalty and expansion.

How do you automate a NOC?

As we have said, going from reactive to proactive care enables operators to avoid possible issues. Another step to aid in automating is facilitating data consolidation and automating the root cause analysis process. If there is a fractured network infrastructure with unconsolidated data, this makes the network harder to monitor, which can create visibility gaps and increase costs. Once data has been consolidated into one platform, operators can quickly run a root cause analysis to find anomalies and problematic behaviors that could cause events that disturb end users.

Using AI/ML helps optimize network operations by learning the typical network trends and detecting when network anomalies occur. Once a problem has been detected, and the cause or causes identified, having an actionable solution is critical. This can be accomplished with a virtual solution (to trigger the corrective action automatically) or human intervention as required. By using AI/ML, the operational performance of the NOC can be dramatically enhanced, and network engineering made more efficient. Transitioning to a more closed-loop approach to network operations.

Solution benefits             

Any NOC solution requires the ability to implement some key features. For example, a virtual NOC solution needs to reduce the time taken to detect network faults, reduce the MTTR (mean time to resolution), and allow NOC and network personnel to focus more efficiently focusing on root cause analysis tasks. In addition, allowing alert information to flow and be shared inside the organization (operations, engineering, customer care) means that organization knowledge can be better maintained, and teams can focus on improving service quality. A vNOC solution helps operators:

Reduce costs

Operators can see what is working and what needs improvement by performing anomaly detection on various KPIs and counters. Knowing which KPIs to focus on allows for cost savings in the future. By creating predictive analysis for early anomaly detection, operators can stay one step ahead of any potential issues impacting their costs.

Improve service quality

Using automation and AI/ML to create best-in-class alerts for various offerings such as network, technology, service, device types, regions, NFs, and slices will improve the service quality for end-users. In addition, managing the alerts using an alert manager utility to help alert sending, notification delivery options, aggregations, timing, activation/deactivation, and muting helps the operator keep track of what is happening in their networks. By implementing support alert annotations and feedback, operators will be kept in the loop regarding what is happening internally and, with open APIs, share all the analytics with other users.

Increase engineering efficiency

A vNOC solution allows operators to perform impact analysis, create network function anomaly propagation chains, and automate periodic NOC flows, such as connectivity, access, and consistency checks. In conjunction with improving its capacity to identify and handle network faults and automate repetitive tasks and flows, this can significantly increase operational engineering efficiencies across networks.

If you want to learn more about how RADCOM can help you automate your NOC, become more efficient, and save costs, contact us at marketing@radcom.com.