Orange points to privacy benefits through MEC

Mobile Edge Computing (MEC) is back on the buzzword agenda after spending a few years in the wilderness and Orange has pointed to an interesting privacy benefit to the technology.

After getting a technology tour at Roland Garros this week, one of the quick demos offered some insight into the world of video analytics and edge computing. Using several different wireless cameras scattered around the venue and various AI applications, Orange is able to keep track on the number of individuals who are in one particular area. This could be one of the entertainment areas or the courts themselves, but the algorithm is able to give an accurate estimate of how populated these areas are, which can help for crowd control or security purposes.

The idea of using facial recognition through video surveillance has started to create some privacy concerns in recent months, as there is little awareness from the general public who have not consented to being monitored, but this is where it gets interesting. Orange pointed out that the images are not detailed to identify specific individuals, just the number of individuals in an area, but even if it was, it doesn’t matter because of edge computing.

With processing power stored on the edge of the network the data can be processed, insight captured, before being deleted. Useless information can be sifted out on the edge, with only relevant data or the insight sent back to the core. By empowering the edge, privacy concerns are negated as personal information is not actually being stored by Orange, simply the insight which would not be considered sensitive.

This is not a revelation which is going to change the technology world, but it is an interesting little benefit which addresses a growing concern in the wider society.

Unlocking the true potential of Mobile Edge Computing in Mobile Networks

Despite the extensive interest in MEC, widespread deployment faces major hurdles as previously available MEC solutions implement proprietary non-3GPP standard solutions in order to maintain compatibility with security, legal intercept, charging, paging. These solutions require deep changes in operator network architecture, which is challenging for operators and are yet unable to provide highly secure and reliable low latency services (e.g. Industry4.0) that must operate independently of backhaul availability or operator network failures. This has had a blocking impact on deployment cost, complexity, security and scalability.

In response, Athonet has created a novel MEC Gateway (incorporating the innovative SGW-LBO) that addresses all of these needs and finally unlocks the true potential of MEC for mobile operators. The full details can be obtained from this Whitepaper. Unlike other approaches, the MEC Gateway natively supports all the above functionality on standard 3GPP interfaces that allows the gateway to be deployed with no other impacts to mobile operator networks. Uses extend from simple video offload to MEC cases requiring mission or business critical needs.

Athonet’s approach was hosted at MWC 2018 by Orange, Italtel and BT:–and-a-stepping-stone-to-5g!/v/d-id/741379?_mc=RSS_LR_EDT

Please fill in the short form below to receive a copy of this whitepaper.

The potential of Machine Learning to optimize content distribution periodically invites expert commentators to share their insights into the most pressing industry issues. In this piece network AI specialists B.Yond explains why we need intelligence at the network edge.

We live in a connected world that is constantly streaming—video, games, music—the demand for content is always on. And, with emerging technologies presenting massive potential, including virtual reality, augmented reality, autonomous transportation, and mobile infotainment, there will be an unprecedented level of demand on the networks. Internet traffic on content delivery networks (CDNs) will more than double from 73 exabytes to 166 exabytes (Cisco VNI 2017) in the next three years.

In addition to the data demand, these applications will require lower latency, higher reliability and better fidelity than current networks deliver. This will require a significant change in network infrastructure from a centralized to a massively distributed architecture. To truly manage the volume and demand on the new network infrastructure and provide an optimized consumer experience with content at the edge, we need software-driven solutions that are focused on significantly reducing operational cost. We need intelligence at the edge.

Bringing Content to the Edge

To meet increasing content volumes, networks must effectively and intelligently manage massive and divergent amounts of data, be available anywhere with the capability to respond instantaneously, and have extensive security capabilities to support privacy concerns. However, the current industry-standard, based on centralized network infrastructure, cannot meet these requirements.

A network with a massively distributed architecture leveraging cloud, Network Function Virtualization (NFV), and Software-Defined Networking (SDN) technologies to employ edge computing, improves operational efficiency, reduces CAPEX, and creates opportunities for new revenue streams. By significantly reducing the distance between the mobile user and content, edge computing enhances network security, improves scalability and responsiveness, and supports low-latency applications.

With enhanced opportunity for content delivery through edge computing, there are further opportunities for growth and revenue. In CDNs, we are seeing a trend as companies build their own private servers on the edge and move away from distributing content through a shared CDN provider. For content providers, this shift to privatization is lowering the cost of handling increasingly high-definition videos, improving the user experience, and enhancing security.

There is a prime opportunity for operators and cable provides to capitalize on this by creating private or shared CDN servers. This can be achieved by repurposing central offices and adding nodes to cell sites and virtual Customer Premise Equipment (vCPE). Operators enable new revenue streams by building private CDNs using their wireless and wireline networks. With 5G and network slicing the costs can be further reduced.

An Intelligent Approach to Managing and Optimizing Content Delivery

As content is pushed to the edge, the automated, intelligent management and optimization of the network becomes essential. By applying Machine Learning (ML) and Artificial Intelligence (AI) to a distributed infrastructure, operators can proactively identify network traffic patterns and proactively respond appropriately to communications traffic demand with focus on improved customer experience. This process works by operators gathering real-time performance data from the software-defined core and access networks, then using ML and AI algorithms to provide guidance instantaneously. By applying this to video applications, service providers can optimize the end-to-end Quality-of-Experience (QoE) to reduce start-up delay, eliminate freezes and improve video quality.

Imagine a customer who is watching the latest episode of “Stranger Things” deployed through the closest local server in “central office one”. However, as traffic on the network begins to increase, the ML platform would proactively identify the potential impact to content delivery and automatically respond. In this case, by making a copy of “Stranger Things” in another central office. It may not be as close physically, but with more availability for transport. For customers, it means never again having their Netflix binging disrupted.

Because of the scale and reach of their networks and their ability to access full end-to-end infrastructure data, operators have an advantage over content providers distributing over the top today. To leverage the opportunity, operators need to build a virtualized CDN infrastructure with a next-generation ML- and AI-based management solution.  Though necessary to effectively and dynamically manage an increasingly complex network, an intelligent management solution will also deliver enhanced quality of experience, and new revenue streams.

Intelligence is Necessary for Progress

With the explosion of content, there is no question that a move to the edge is required to support a new wave of increasingly demanding content-based applications. But, the move to a distributed infrastructure is not enough. Without the use of proactive intelligence—the complexity of a massive edge network and the demands of the content become unmanageable and turn into an operational nightmare.

The optimal customer QoE requires the application of ML and AI to network performance data in order to guide the CDN infrastructure and video applications. Operators and content providers must work together to bring intelligence to the edge to progress the capabilities of content delivery.

ETSI give TLC to MEC – aging buzzword to get a facelift

Multi-access Edge Computing (MEC) might have been given a bit of attention in months gone, but with the 5G dawn about to break a resurgence for MEC could be on the cards.

While it does not sound like the sexiest part of the mobile industry, MEC is crucially important. If we are to live the 5G dream of 8K videos or instant access to insight, the ability to store and cache data on the edge of the network is critical. This is an old story for the industry, but it is a narrative which has been neglected in recent months. ESTI is one organization which seems to be trying to gather some extra steam for the forgotten buzzword.

“As the first Standards Developing Organization to address the challenges of MEC, ETSI brings the world’s leading experts on MEC to the table,” said Alex Reznik, Chair of ETSI MEC Industry Specification Group. “The ETSI ISG MEC can make a significant impact in the effort to make 5G a reality and we invite the industry to take advantage of everything we have to offer.”

MEC is of course only one piece of the 5G puzzle and a step in the complicated journey of virtualization, but one which is very important. Will virtual assistants be able to perform adequately without it, or will latency be low enough for autonomous vehicles or remote surgery? Not only will we not be able to realise some of these glorious usecases, ignoring MEC could potentially undermine the whole premise of the 5G system architecture, which is supposed to be a distributed network. With the 5G light breaking over the horizon ETSI is shifting the focus back to MEC.

As part of the push, ETSI has released two white papers while also creating a Hackathon framework to accelerate multi-access edge computing adoption and interoperability, and encourage all stakeholders to use the group’s specifications to develop edge applications. Collaboration between the various different parties will be critical here, and considering some of the parties involved there is risk of a few disagreements.

“While MEC is central to enabling the world of 5G applications over both 4G and 5G networks, it is only part of a solution to a bigger puzzle,” Reznik had previously said. “Increasingly, the industry is looking for guidance on how to put the overall solution together. By providing end-to-end solution guidance, encouraging and promoting the market through events like Hackathons and other related activities, our group is stepping up to this challenge.”

ETSI is kicking starting the refocus onto MEC, but we expect this to be a much more prominent talking point (once again) over the next couple of months.