The Shift of Service Assurance in Telecommunications

Anritsu has been leading innovation in communications equipment since 1895. It’s seen the global evolution of telecoms and has accordingly widened its business to include the latest instruments, in addition to updating its quality assurance inspection.

Brian Murray, Head of Product Marketing for Anritsu Service Assurance, shares with us how mobile network operators (MNOs) can resolve modern problems in the telecommunications industry and how edge assurance can transform service assurance.

Challenges mobile network operators face with the growth of telecom data

“The exponential growth of telecom data continues to pose intensifying challenges for MNOs,” says Brian. “The increase in data speeds has led to a surge in User Plane traffic, which has become a significant data storage challenge from a service assurance and post-hoc analysis perspective. MNOs are grappling with not having the budget or inclination to store these vast amounts of data for troubleshooting and analysis purposes.” 

Numerous factors, from simple storage capacity to complex data protection requirements, have necessitated a new approach to service assurance.

“Instead of proactively hunting for prey, orb-web spiders build a web, retreat and wait for prey to become tangled in the web,” Brian illustrates. “The prey's struggle in the web can be likened to an alarm, which prompts the spider to go to the centre of the web and wait for further vibrations to tell it the exact location of its prey. Once the spider knows the location, it heads in that direction and consumes the insect. The spider web analogy is strikingly similar to the reactive approach mobile network operators use to resolve issues.”

Consisting of four steps, service assurance in telecommunications relies on:

• Waiting for an alarm
• Investigating the alarm’s source by analysing data stored in a central location
• Dive deeper into the issue by examining data on the edge (on probes)
• Resolve the issue.

In another example, octopuses have both a central brain and a network of nerves within and between their arms, which serve as a second brain. 

“While the arms can operate independently, they also collaborate with the central brain. In certain situations, like encountering a potential food source, the octopus’ arm makes an independent decision – a decision on the edge,” Brian describes. “The concept of edge assurance requires service assurance or automation systems to be capable of making resolutions or automation decisions that are closer to the problem (on the edge).” 

However, doing this requires complex pattern recognition and a new form of communication between the central and edge assurance. 

“Edge assurance requires edge agents to be closer to the root problem – not at the core but closer to the network boundaries. For example, an edge agent could quickly track the call success ratio (CSR) to where calls fail in the Radio Access Network (RAN). If the CSR in the access network falls below a predefined threshold, the edge agent can trigger an alarm or independently attempt an automated resolution.”

The power to make independent edge decisions can only be fueled by machine learning (ML) and AI. Leveraging a combination of technologies (advanced AI, ML algorithms, real-time analytics, and edge computing) and innovative processes will speed issue detection, root cause analysis and automated issue resolution. 

How edge assurance will transform service assurance in telecommunications

“As MNOs embrace varying levels of autonomous networks, evidential data will become increasingly critical,” he continues. “Evidential data falls within two categories - generic fixes and automations for generic issues and operator-specific fixes and automations.”

The evidence itself, however, is incident-specific and should, at a minimum, include:

• A fingerprint (description) of the incident that can be cross-referenced against an ongoing incident
• A template for the fix such as reboot, redirect, update configuration
• A reference that allows the action system to understand if the fix has resolved the issue. For example ‘X minutes after the fix was applied, run test Y to verify resolution, if problem was not resolved, raise alarm Z’.

Edge agents use near real-time data to look for issues and anomalies on the network’s edge and validate their findings using rules or references. 

“Once validated, edge agents share their findings with other edge agents and crowdsource from them, ensuring each edge agent has the same level of intelligence. This essential data set shared between edge agents is evidential data,” explains Brian. 

Edge assurance represents a significant shift in how MNOs approach today’s telecommunications network challenges. By leveraging decentralised intelligence and harnessing the power of ML and AI, MNOs can create more efficient, responsive, and effective service assurance, automation, and orchestration systems. 

“However,” Brian warns, “This evolution will require rethinking current practices, a commitment to innovation and a willingness to embrace new technologies and methodologies.”

Make sure you check out the latest edition of Mobile Magazine and also sign up to our global conference series - Tech & AI LIVE 2024

******

Mobile Magazine is a BizClik brand