Mobile network operators, cable companies and hyperscalers are all investing in edge infrastructure, with market drivers including use cases from private cellular networking to edge robotics in industry—as well as disaggregated open networks, said Bejoy Pankajakshan, EVP and chief technology and strategy officer for Mavenir.
During the Mobile Edge Forum virtual event, Pankajakshan said that a major shift in processing is coming. “Today, if we look at how the applications are being processed, less than 10% of the traffic is actually processed outside of a traditional data center, which comes from the operator,” he said. “If we look at 10 years out, the projections that I’ve seen, it talks about up to 75% of the processing would actually happen outside of the traditional data centers.” Expected verticals with anticipated growth in edge use include remote healthcare diagnostics and telehealth as well as industrial edge computing.
Pankajakshan sees Open RAN development intersecting with, and being an additional driver for, edge computing. In particular, he highlighted the role of the RAN Intelligent Controller, or RIC—which he compared to an app store for the RAN, where innovation can play out with more agility and speed than in traditional, single-vendor implementations.
The RIC, he noted, comes in two forms: The near-real-time RIC and the non-real-time RIC. Both host applications: In the near-real-time RIC, those are xApps, while the non-real-time RIC has rApps. The difference between then is the closeness of the control loops, Pankajakshan explains. “If you’re implementing an application where the control loop needs to be executed in less than a second, then you would run it on a near-real-time RIC. … And when you have longer control cycles, then you would run it in the rApps,” he says.
That application-hosting ability makes the RIC in itself a “flavor” of edge compute, he says, bringing intelligence deeper into the network—and potentially hosting additional third-party apps that make use of the available Open RAN network APIs.
“Why this is critical for edge computing is because, by the fact that you’re moving some of the radio resource management and control functionality out into the centralized edge infrastructure or radio intelligent controller, you could now have third-party applications leverage the APIs that come out of these boxes to build applications that could be used for enterprise or consumer use cases,” Pankajakshan said. “You could now host applications that are much closer and have a per-user or per-device control mechanism in place.
He offered up use cases such as traffic steering or quality-of-experience optimization, in which edge computing could be applied to steer a particular user to get better throughput or latency. “If a user is in a congested area, you could optimize the experience of the user even though the rest of the users in that locality may not get the best experience. … You enhance the end experience that the enterprise or consumer could leverage.”
That means more opportunities for monetization as well as a variety of human or machine user experiences that could be enabled via edge intelligence—and an opportunity for operators in 5G that was missed in 4G, in which third-party players ultimately developed APIs that were more consumable and simpler than operator-exposed APIs, and drove adoption by developers. What does it take to actually implement such use cases? A lot of expertise in the RAN combined with machine learning, Pankajakshan says. “You would go through detailed network modeling, you would build algorithms that can deliver better performance and then modify and retrain these models so that it gets better optimized over time,” he explains. “You build a library of network intelligent applications that can be leveraged by the edge applications, and that could be a third-party developer or the operator themselves utilizing this edge application to run their network better and deliver better experience for the edge applications.
“As you go higher up the value chain here in terms of intelligence, you use better algorithms, you use better machine learning and AI algorithms here to deliver a model which can deliver better results as opposed to a closed system,” Pankajakshan concluded. “So we see Open RAN with [the RIC] and the intelligence it brings into the network near the edge as one of the key building blocks for the operator to build intelligence at the edge of the network.”
