Is AI the missing piece of the virtualization puzzle?

Artificial Intelligence is the latest piece of the jigsaw telecom operators must put together as they evolve their networks from physical to cloud-based, virtualized infrastructures (NFV/SDN).

Guest author

May 17, 2017

5 Min Read
Is AI the missing piece of the virtualization puzzle? periodically invites expert third parties to share their views on the industry’s most pressing issues. In this piece Angela Logothetis, VP and CTO of Amdocs Open Network, argues that AI and automation are critical factors in the successful move to telco virtualization.

Artificial Intelligence (AI) is the latest piece of the jigsaw telecom operators must put together as they evolve their networks from physical to cloud-based, virtualized infrastructures (NFV/SDN).

It’s easy to get carried away by the possibilities AI and technologies such as Machine Learning offer. Customer analytics, revenue assurance and network optimization are all ripe for this new technology.

But the area where AI can make the biggest impact is operational automation: letting networks run themselves. Instead of spending substantial amounts on managing, maintaining and fixing them, networks could become “self-healing.”

But carriers have been slow to commit to automating their networks.

Letting the network run itself

To date, network operations have been semi-automated at best. Though more and more functions have moved onto software platforms, it has made little sense for many operators to automate these functions, when there was still a large physical network that required manual configuration and servicing.

However, NFV/SDN virtualization converts much of the physical infrastructure into software. And within a virtualized network, there is little need for so-called truck rolls – sending out engineers to configure, repair or service equipment. This opens an unprecedented opportunity to automate networks ‘end-to-end’, enabling them to take care of themselves.

The costs savings and the flexibility NFV/SDN brings to creating and provisioning services (‘service agility’) are well documented. But the technology can only come truly into its own once network and service operations are automated. Not only that, but operational automation is so vital that the business case for NFV/SDN will crumble without it.

And yet, neither vendors, nor service providers or the standards community have paid much attention to operational automation so far, preferring to focus on service creation instead.

Artificial Intelligence could play an important part in changing this.

How AI fits into the picture

A telecoms network generates billions of data points daily. Level 3, the global carrier, registers more than 50 billion network events every day, including faults, security threats and performance alerts on issues such as latency, loss and jitter. Some events require action but much of the data is noise.

So far, it has fallen to rules engines within network assurance systems to sift through all this data before it is reviewed by network operations staff to figure out what to do. With the bulk of analysis being done by a person, these systems did not have to be very accurate; they just had to provide a first layer of analytics.

This is different in an automated NFV/SDN network, where the network orchestration platform – for example ONAP – needs a highly accurate feed of data and instructions to execute.

This is where Artificial Intelligence and Machine Learning come in.  AI algorithms monitor the behaviour of the network and detect events that require action. Machine Learning then steps in to analyse the events and come up with solutions, using a database of past events. Those decisions are then implemented automatically by the orchestration platform.  For example, when the AI engine detects latency or a security threat on the network, ONAP can trigger an automated ‘fix’ to the problem.

A process which typically take hours – for instance allocating extra bandwidth to reduce latency – can instead be completed in a matter of minutes. And because AI also picks up issues which a human analyst might have missed, it enables carriers to provide more consistent customer service levels.

While the benefits of AI-driven automation are evident, the question is how service providers can evolve from manual to automated operations?

The route ahead

The use of AI in NFV/SDN networks is in its infancy – as is virtualization itself. How AI engines will interact with automation platforms such as ONAP is still a work in progress.

What is clear is that the industry is looking to machine learning and AI to define automation ‘on the fly’. The goal is for the automation platform to learn from past events, improve continuously and respond to triggers ever more independently.

Right now, this is easier said than done. Existing machine learning systems do not meet carriers’ requirements in areas such as reliability (‘six nines’, 99.9999%), real-time processing and scalability.

That said, no matter how much machines can learn and how quickly they can do so, there will always be situations where even the best AI engine cannot replace a ‘war room’ of human expertise. This is especially true for rare or first time scenarios. So, while we can automate network operations to a considerable extent, they will never run entirely without human intervention.

However, even when the last resort is a person, AI and Machine Learning can assist with troubleshooting, for example by and picking out what ‘war roomers’ should investigate first.

Automation, automation, automation

The biggest threat to realizing the potential of virtualization and automation is inertia.

Modern telecoms has a history of jumping on the bandwagon of new technologies and then only committing to them half-heartedly. Take the move to all-IP networks, which dominated the headlines in the early 2000s: it was never fully realised because service providers did not want to lose control of network operations, especially routing and prioritizing traffic.

Unless carriers are willing to ‘let go’ and commit to automation, the same could happen with NFV/SDN. Because without operational automation, virtualization is simply a sideways step. Instead of hardware the network is software – but not much else changes.

AI and machine learning can play a major part in the transition towards operational automation and help ensure that operators reap the rewards of their investment in NFV/SDN.


Angela-Logothetis-150x150.jpgAngela Logothetis is the Vice President and Head of CTO for Amdocs Open Network. She leads Network Strategy, Technology and Service Innovation and Incubation for Amdocs. She is the trusted advisor to Amdocs customers as they embark on major network and IT transformations. Over the past 20 years Angela has led strategic network and IT transformations including the creation of NOCs and SOCs, fiber rollout, number portability, unbundled local loop, fixed mobile convergence, circuit to packet switching, ethernet and small cells deployment, lights out fulfilment, automation of network rollout, BSS and OSS transformation. She is currently engaged in NFV, the journey to 5G, and operational automation. She brings a truly global perspective having worked with more than 15 service providers across APAC, North America and Europe.

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