Large Language Models (LLMs) are likely to play a key role in Intent-Based Networking (IBN) as they show remarkable performance in interpreting human language as well as code generation, enabling the translation of high-level intents expressed by humans into low-level network configurations. In this paper, we leverage closed-source language models (i.e., Google Gemini 1.5 pro, ChatGPT-4) and open-source models (i.e., LLama, Mistral) to investigate their capacity to generate E2E network configurations for radio access networks (RANs) and core networks in 5G/6G mobile networks. We introduce a novel performance metrics, known as FEACI, to quantitatively assess the format (F), explainability (E), accuracy (A), cost (C), and inference time (I) of the generated answer; existing general metrics are unable to capture these features. The results of our study demonstrate that open-source models can achieve comparable or even superior translation performance compared with the closed-source models requiring costly hardware setup and not accessible to all users.

The integration of Machine Learning and Artificial Intelligence (ML/AI) into fifth-generation (5G) networks has made evident the limitations of network intelligence with ever-increasing, strenuous requirements for current and next-generation devices. This transition to ubiquitous intelligence demands high connectivity, synchronicity, and end-to-end communication between users and network operators, and will pave the way towards full network automation without human intervention. Intent-based networking is a key factor in the reduction of human actions, roles, and responsibilities while shifting towards novel extraction and interpretation of automated network management. This paper presents the development of a custom Large Language Model (LLM) for 5G and next-generation intent-based networking and provides insights into future LLM developments and integrations to realize end-to-end intent-based networking for fully automated network intelligence.

To effectively express and satisfy network application requirements, intent-based network management has emerged as a promising solution. In intent-based methods, users and applications express their intent in a high-level abstract language to the network. Although this abstraction simplifies network operation, it induces many challenges to efficiently express applications' intents and map them to different network capabilities. Therefore, in this work, we propose an AI-based framework for intent profiling and translation. We consider a scenario where applications interacting with the network express their needs for network services in their domain language. The machine-to-machine communication (i.e., between applications and the network) is complex since it requires networks to learn how to understand the domain languages of each application, which is neither practical nor scalable. Instead, a framework based on emergent communication is proposed for intent profiling, in which applications express their abstract quality-of-experience (QoE) intents to the network through emergent communication messages. Subsequently, the network learns how to interpret these communication messages and map them to network capabilities (i.e., slices) to guarantee the requested Quality-of-Service (QoS). Simulation results show that the proposed method outperforms self-learning slicing and other baselines, and achieves a performance close to the perfect knowledge baseline.

Cisco today said it reached a deal to buy Exablaze, a low-latency networking device maker, for an undisclosed amount. The acquisition adds Exablaze's field programmable gate array (FPGA) based devices to Cisco's portfolio, which the networking giant says will boost its intent-based networking (IBN) strategy. Cisco expects the acquisition to close by the third quarter of fiscal 2020. Some market segments in particular like high-frequency trading (HFT), financial services, high-performance computing, and emerging artificial intelligence (AI)/machine learning (ML) clusters require extreme network capacity, flexibility, and speed, wrote Rob Salvagno, VP of corporate development and Cisco Investments, in a blog post about the acquisition. "In the case of the high frequency trading sector, every sliver of time matters," he wrote.

With increasingly complex applications and services, IT professionals are looking to machine learning and artificial intelligence to help align their business needs. In a survey of over 2,000 IT leaders and network strategists, Cisco found maximizing businesses' value to be IT's top priority, which indicates a desire to drive greater innovation and closer alignment to business strategies. In addition to investing in machine learning (ML) and artificial intelligence (AI), Cisco's survey also found that IT professionals are moving their networks towards intent-based networking. Cisco introduced intent-based networking with the launch of its DNA Center in 2017, and has since integrated it across the company's portfolio. "IT teams today are running vast, complex networks that are providing massive amounts of data. But using that data to improve the network and accelerate the business requires new tools. That's why IT teams are embracing intent-based networking, AI and machine learning --because the business demands it," said Scott Harrell, SVP and GM, Cisco Enterprise Networking, in a statement.

SAN JOSE, Calif., October 24, 2019 – The network is vital to today's digital business. Whether maximizing employee productivity, optimizing customer experience or keeping data protected and secure, the network is foundational to business success. At the same time, the network is in the midst of one of its biggest evolutions since the introduction of the Internet, creating an opportunity for IT leaders and their teams to innovate. Cisco asked over 2000 IT leaders and network strategists how they plan to prioritize investment and the current state of their networks. "IT teams today are running complex mission critical networks that are increasingly capable of providing rich data. But using that data to improve the operations, security, or business impact of the network requires new tools. That's why IT teams are embracing intent-based networking, AI and machine learning -- because the business demands it," said Scott Harrell, SVP and GM, Cisco Enterprise Networking.

We, and our customers, have long understood that networks are most valuable when they do more than support their own weight. They become assets only when they enable the growth of business strategies. To get networks to work at this level, we have to get a higher level of performance and agility from them. That's why we launched a series of products and services based on intent-based networking two years ago. Our goal was to reinvent access networking to serve business needs.

One of the most attractive benefits to intent-based networking (IBN) is its ability to relieve the IT department of updating all networking devices to complete an over-arching business objective. IBN is defined as a self-driving network that automatically applies business intent to various network devices across a network without having to rely on command-line interface (CLI). Instead, the network administrator writes intent in plain language or via a graphical interface. In simple terms, IBN is about telling the software what the business intent is with the software automatically applying the intent across the network. IBN is achievable through orchestration and machine learning (ML).

In my opening blog post this year I wrote about the many issues the market seemed to indicate would be important in 2018. Obviously, I mentioned SD-WAN technology, 5G and Big Data, and I also talked about Artificial Intelligence (AI) and a few others. However, there is one area that I did not mention, which is intent-based networking (IBN). Perhaps it may be early to call 2018 the year of intent-based networking, but some important analysts have said that this is one of the next big things to come! So, what exactly is intent-based networking or IBN?

We have seen the revolution in IT infrastructure networking with SDN and NFV in last 5 years. We are witnessing tremendous upgrade in digitization which resulted in agile and highly available network connectivity with the help of both of these technologies. Be it telecom or data center, SDN and NFV played huge role to reduce costs with adding benefit of faster resource allocation with its software defined nature. After revolutionizing network with functions virtualization and software defined operations, thought process is moved towards providing'intent' to network to perform tasks. Intent comes from business and IT services in form of demands from network to keep up with growing requirements, while reducing operational and maintenance costs and keep security in place.