What if, at the touch of a button, your network could automatically configure itself to deliver and guarantee the level of service required for your application? What if your network had 360° visibility and could automatically manage resources to guarantee service delivery? Just imagine how much efficiency and agility this would bring to your enterprise network.

Evolved intent-based networking promises all this and more – including a new generation of innovative applications. But before I explain more, let me just provide a little context around intent-based networking (IBN) itself. It’s a concept that enables networks to be automatically configured to support a specific set of business requirements.

Each intent is enabled automatically, thereby simplifying network configuration and operations to a level previously unheard of. Requirements are set out, formalized into specific intents for the network to carry out and then enacted. It sounds simple and it is – because manual network configuration and specific coding for individual tasks are not required.

Whitepaper: The pervasive intelligence of evolved intent-based networking.

The trouble is that the applications of IBN have been limited because they utilize a single point view of the network to assess and monitor performance. This is where evolution comes in. The team here at CC believes that the future of IBN will be driven by ‘evolved IBN’, a concept fueled by our work to explore the future of IBN and identify opportunities for network owners. Evolved IBN enables holistic and much more comprehensive network administration because of its ability to manage distributed intelligence from across the entire network.

The pervasive intelligence of evolved IBN

Multiple, fundamental technological capabilities are arriving and changing the potential of IBN. Artificial intelligence (AI), open radio access networks (Open RAN), private networks, software defined networks, 5G and numerous industrial IoT applications, to name just a few, are placing new demands on the network – and simultaneously making new capabilities a reality.

I believe evolved IBN will meet these demands because it is founded on the concept of pervasive intelligence. This is achieved by gathering comprehensive points of information at volume from across the network. Deep learning and machine learning methods are then applied to extract insights from the intelligence so the evolved IBN system can act upon these and set up the network to meet business requirements. I see this new capability, which proves the feasibility of the evolved IBN concept, opening up the potential to apply it to use cases from across the spectrum of industries.

To expand on that point a little further, I think it’s difficult to overstate the timely significance of evolved IBN. By expanding the boundaries of what’s currently considered possible, it can deliver new capabilities and revenues for network users and operators across a variety of industries, including agriculture, healthcare, logistics, manufacturing, ports, utilities and warehousing. What’s more, evolved IBN has an inherent flexibility that allows it to address a specific business need – and for the end customer to have complete confidence that a specific service and experience level will be delivered.

Intent Based Networks
Figure 1: A comparison between a legacy intent-based network and an evolved intent-based network

Who will benefit from evolved IBN?

Evolved IBN enables services to be created quickly and offer great flexibility in switching from one level of service to another. As network operators face requirements from a growing set of diverse use cases, it is essential to simplify and accelerate the process of configuration, deployment and monitoring of the networks to meet the requirements of the diverse use cases we are starting to see.  

Supporting these demands presents an important opportunity for communications service providers (CSPs) to create services with benefits for end-users and their individual, focused needs. The function-oriented performance needed by machines and automated guided vehicles (AGVs) in warehouses or factories illustrates how an evolved IBN can be indispensable. To create an efficient operational environment, AGVs, picking machines, weighers and conveyors all need to come together with connected systems that ensure stock is sent to the right locations and customers’ shops are handled efficiently.

How does evolved IBN enable a resilient and robust service under these conditions? The intent of optimizing stock picking across a warehouse is translated so a script is coded that can configure the hardware and control bandwidth during the picking cycle. Then the evolved IBN system runs a feasibility check before approving the configuration changes and authorizing them to be implemented. Performance is constantly audited, and the configuration is adjusted, ideally by AI in real time to assure performance. Finally, the evolved IBN system reports the network status, performance and any issues to the network administrator.

Another example in healthcare sees medical support for chronic conditions being achieved over LTE or 5G networks that monitor, record and then communicate vital signs to hospitals or medical professionals. The intent here is that the network is available to the system when it needs to upload data but does not unnecessarily connect and consume battery power when communication is unnecessary.

A third, and in this case low-value example, is soil sensors in a field of crops. These can communicate pH levels, soil moisture or the need for fertilizer. Such projects involve large numbers of typically cheap products that seldom communicate urgent data, so the network needs to be configured to reflect this. For applications of this type, maintaining low operating cost is important in order to secure high volume uptake to make the service proposition viable for its service provider.

Higher-value examples demand different intents to be enabled. In the utilities industry, power suppliers could utilize evolved IBN to support their advanced metering infrastructure (AMI), supervisory control and data acquisition (SCADA) systems, distribution automation and distributed energy resources. In AMI alone, having IBN-enabled capabilities would enable the network to be built for purpose and smooth out peaks and troughs in capacity requirements alongside the need to process customer information.

Automatic enablement of intents

The number of industries, use cases and applications evolved IBN can be applied to demonstrates its potential value. We see the evolved IBN as an opportunity for CSPs, systems integrators and IT vendors. There is significant revenue potential here and large, terrestrial US CSPs are already exploring how to build specific networks for manufacturing and warehousing organizations. These CSPs are also focusing on automating networks so they can move on from being simply network providers and become providers of services that take an intent and make it a reality.

Evolved IBN relies on understanding, customizing and applying the advanced technologies that are revolutionizing networks. But nothing about it is easy. There is much to learn and significant difficulties to overcome in handling the sheer scale of evolved IBN adjustments and demands. Nevertheless, the concept stands ready to become reality thanks to the application of AI and deep and machine learning.

In my opinion the effort will be worth the reward because evolved IBN provides vast opportunities for providers of all types to be pioneers of new capabilities. To discover how evolved IBN has been transformed by the addition of pervasive intelligence – and how you can benefit – read our whitepaper: ‘A new way forward for industry: the pervasive intelligence of evolved intent-based networking’.

Author
ラム・ナイドゥ
ケンブリッジコンサルタンツ シニア・バイスプレジデント

ラム・ナイドゥはケンブリッジコンサルタンツ(キャップジェミニ・インベントの一部門)はAI分野のシニア・バイスプレジデント  ラムは、世界トップクラスのAIを活用したイノベーションを市場に送り出すために、卓越したリーダーシップを発揮してきました。ラムは困難な問題を解決し、優れた製品とサービスを構築することに専念するチームをインスパイアして指導することに情熱を注いできました。

彼は、Questrom School of BusinessでMBAを、Boston University College of Engineeringで博士号を取得し、製品戦略や商品化、イノベーションマネジメント、AIに関する重要な専門知識を持っています。