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Executive Summary - Overview and Key Takeaways
0.25 page – Beth Cohen will fill in when the paper is mostly complete.
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The future of Intelligent Networking and AI is in the hands of the individuals and organizations who are willing and able to contribute to new and existing projects and initiatives. If you are involved in building and operating networks, developing network technology or consuming network services, consider getting involved. Engaging with the LFN projects and communities can be an educational and rewarding way to shape the future of Intelligent Networking.
Key Takeaways
- Intelligent networking is rapidly moving out of the lab and being deployed directly into production
- Operational maintenance, and service assurance are still a priority, but there is increasing interest in using AI/ML to drive network optimization and efficiency
- More research and development is needed to establish industry wide best practices and a shared understanding of intelligent networking to support interoperability.
- There has been some work on developing common or shared data sources and standards, but it remains a challenge
- LFN and the Open Source community are key contributors to furthering the development of intelligent networking now and in the future
Background and History
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At their core, telecoms are technology companies driven by the need to scale their networks to service millions of users, reliably, transparently and efficiently. To achieve these ambitious goals, they need to optimize their networks by incorporating the latest technologies to feed the connected world's insatiable appetite for ever more bandwidth. To do this efficiently, the networks themselves need to become more intelligent. At the end of 2021, a bit over 2.5 years ago, LFN published its first white paper on the state of intelligent networking in the telecom industry. Based on a survey of over 70 of its telecom community members, the findings pointed to a still nascent field made up of mostly research projects and lab experiments, with a few operational deployments related to automation and faster ticket resolutions. The survey did highlight the keen interest its respondents had in intelligent networking, machine learning and its promise for the future of the telecom industry in general.
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- How to create and maintain public Networking data sets for research and development of AI applications? (Ranked #1 in GB member survey)
- What are some feasible goals (short term) in creation of AI powered Network Operations technologies
- Evaluate the existing Networking AI assets coming from member company contributions
- Analyze generic base AI models and recommend the creation of Network specific base models (Ranked high in GB member survey)
- Recommended approaches and the potential for open source projects to contribute to the next generation of intelligent networking tools.
Challenges and Opportunities
1 page - Focus on Telco pain points only
Ironically, as generative AI and LLM adoption becomes more widespread in many industries, telecom has lagged somewhat due to a number of valid factors. As was covered in the previous white paper, the overall industry challenges remain the same, that is the constant pressure to increase the efficiency and capacity of operators’ infrastructures to deliver more services to customers for lower operational costs. The complexity and lack of a common standard understanding of network traffic data remains a barrier for the industry to speed the adoption of AI/ML to optimize network service delivery. Some of the challenges that are motivating continued research and adoption of intelligent networking in the industry include:
Common Telecom Pain Points
- Operational Efficiency: The continuing need to reduce costs and errors, potentially increase margins
- Network Automation: Right-sizing network hardware and software, optimizing location placement
- Availability: Identifying single points of failure in systems to improve equipment maintenance efficiency
- Capacity Planning: Avoid unnecessary upgrades or poor network performance from overloaded nodes.
Challenges to Achieving Full Autonomy
0.5 page Lingli Deng Andrei Agapi
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- Community Unity and Standards: Although equipment manufacturers can provide many domain AI solutions for professional networks/single-point equipment, these solutions are limited in "field of view" and cannot solve problems that require a "global view" such as end-to-end service quality assurance and rapid response to faults. . Operators need to aggregate management and maintenance data in various network domains by building a unified data sharing platform, and based on this, further provide a unified computing resource pool, basic AI algorithms and inference platform (i.e. cross-domain AI platform) for scenario-specific AI, end-to-end scenarios, and intra-domain scenarios using an applied reasoning platform.
Emerging Opportunities
The Telecoms have been working on converged infrastructure for a while. Voice over IP has long been an industry standard, but there is far more than can be done to drive even more efficiencies with network and infrastructure convergence.
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Data Model Simplification: Large language models can be used to understand large amounts of unstructured operation and maintenance data (for example, system logs, operation and maintenance work orders, operation guides, company documents, etc., which are traditionally used in human-computer interaction or human-to-human collaboration scenarios), from which effective knowledge is extracted to guide further automatic/intelligent operation and maintenance, thereby effectively expanding the scope of the application of autonomous mechanism.
Brief Overview of AI Research in Context
Lingli DengAndrei Agapi Sandeep Panesar
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Beyond typical chatbot-style applications, LLMs have been extended to generate code, solve math problems (stated either formally or informally), pass science exams, or act as incipient agents for different tasks, including advising on investment strategies, or setting up a small business. Recent advancements to the basic LLM text generation model include instruction finetuning, retrieval augmented generation using external vector stores, using external tools such as web search, external knowledge databases or other APIs for grounding models, code interpreters, calculators and formal reasoning tools. Beyond LLMs and NLP, transformers have also been used to handle non-textual data, such as images, sound, and arbitrary sequence data.
Intelligent Networking Differences
A natural question arises on how the power of LLMs can be harnessed for problems and applications related to Intelligent Networking, network automation and for operating and optimizing telecommunication networks in general, at any level of the network stack. Datasets in telco-related applications have a few particularities unique to the industry. For one, the data one might encounter ranges from fully structured (e.g. code, scripts, configuration, or time series KPIs), to semi-structured (syslogs, design templates etc.), to unstructured data (design documents and specifications, Wikis, Github issues, emails, chatbot conversations).
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In conclusion, while foundation models and transfer learning have been shown to work very well on general human language when pre-training is done on large corpuses of human text (such as Wikipedia, or the Pile [29]), it remains an open question to be answered whether domain adaptation and downstream task adaptation work equally well on the kinds of domain-specific, semi-structured, mixed modality datasets we find in the telecom industry. To enable this, telecoms should focus on standardization and data governance efforts, such as standardized and unified data collection policies and developing high quality structured data across a common definition and understanding.
Projects and Research
1.5 page
3GPP Intelligent Radio Access Network (RAN)
The intelligent evolution of wireless access networks is in a phase of rapid evolution and continuous innovation. In June 2022, 3GPP announced the freezing of R17 and described the process diagram of an intelligent RAN in TR37.817, including data collection, model training, model inference, and execution modules, which together form the infrastructure of an intelligent RAN. This promotes the rapid implementation and deployment of 5G RAN intelligence and provides support for intelligent scenarios such as energy saving, load balancing, and mobility optimization.
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However, despite the remarkable progress made in the 5G wireless access network intelligence industry, some challenges and issues remain to be addressed. For example, network security and data privacy protection are pressing issues that require effective measures to be implemented. The energy consumption issue of 5G networks also needs attention, necessitating technological innovations and energy-saving measures to reduce energy consumption. In the future, continuous efforts should be made in technological innovation, market application, and other aspects to promote the sustainable and healthy development of the 5G wireless access network intelligence industry.
Mobile Core Network Transformation
Mobile core networks can be thought of as the brains of mobile communication. In recent years, these networks have experienced a huge transformation from legacy proprietary hardware to telecom cloud native systems. Today, the majority of mobile core networks are deployed based on telco cloud architectures supported by NFV technologies. Intelligent networking most benefits the packet core networks such as 5GC and UPF which are responsible for packet forwarding, IMS which support delivery of multimedia communications such as voice, message and video, and operational functions that manage the core network itself including telco cloud infrastructure and 5G network applications. There have been three areas where intelligent networking have shown benefits:
Network Intelligence Enables Experience Monetization and Differentiated Operations
For a long time, operators have strived to realize traffic monetization on MBB(Mobile Broadband) networks. However, there are three technical gaps: non-assessable user experience, limited or no dynamic optimization, and no-closed-loop operations. To bridge these gaps, there is a need for an Intelligent Personalized Experience solution, designed to help operators add experience privileges to service packages and better monetize differentiated experiences. Typically in the industry, the user plane on the mobile core network processes and forwards one service flow using one vCPU. As heavy-traffic services increase, such as 2K or 4K HD video and live streaming, microbursts and extremely large network flows become the norm, it becomes more likely that a vCPU will become overloaded, causing packet loss. To address this issue, Intelligent AI supported 5G core networks need to be able to deliver ubiquitous 10 Gbps superior experiences.
Service Intelligence Expands the Profitability of Calling Services
In 2023, New Calling was put into commercial use, based on a 3GPP specification, it can enhance intelligence and data channel (3GPP specification)-based interaction capabilities; it enables users to use multi-modal communications, and helps operators construct more efficient service layouts. In addition, the 3GPP architecture allows users to control digital avatars through voice during calls, delivering a more personalized calling experience. One example where this can be seen as a business opportunity might be an enterprise using the framework to customize their own enterprise ambassador avatar to promote their brand.
O&M Intelligence Achieves High Network Stability and Efficiency
Empowered by the multi-modal large model, the Digital Assistant & Digital Expert (DAE) based AI technology could reduce O&M workload and improve O&M efficiency. It can reshape cloud-based O&M from "experts+tools" to intelligence-centric "DAE+manual assistance". Using the DAE, it is possible that up to 80% of telecommunication operator trouble tickets can be automatically processed, which is far more efficient than the manual processing it is for the most part today. DAE also enables intent-driven O&M, avoiding manual decision-making. Before, it commonly took over five years to train experts in a single domain, with the multi-modal large model it is now possible for it to be trained and updated in mere weeks.
Thoth project - Telco Data Anonymizer
The Thoth project, which is a sub project under the Anuket infrastructure project, has recently focused on a major challenge to the adoption of intelligent networks, the lack of a common data set or an agreement on a common understanding of the data set that is needed. AI has the potential for creating value in terms of enhanced workload availability and improved performance and efficiency for NFV use cases. Thoth's work aims to build machine-Learning models and Tools that can be used by Telecom operators (typically by the operations team). Each of these models is designed to solve a single problem within a particular category. For example, the first category we have chosen is Failure prediction, and the project plans to create 6 models - failure prediction of VMs. Containers, Nodes, Network-Links, Applications, and middleware services. This project also will work on defining a set of data models for each of the decision-making problems, that will help both providers and consumers of the data to collaborate.
How could Open Source Help?
2-3 pages
It is important to understand the current landscape of Open Source Software (OSS) projects and initiatives and how they came into existence to see how OSS is well positioned to address the challenges of Intelligent Networking. Several such initiatives have already laid down the groundwork for building Network AI solutions, or are actively in the process of creating them. Building on these foundations, it is easy to envision the critical role open source software can play in unleashing the power of AI for the future generations of networking. Some of the required technologies are unique to the Networking industry, and will have to be addressed by existing OSS projects on the landscape, or by the creation of additional ones. Some pieces of the technologies are more generic, and will need to come from the broader community of AI OSS. Here is a rough outline of the different layers of Networking AI and the sources for the required technology:
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In addition, successful development of AI models for use in Networking relies on the availability of data that could be shared under a common license. The Linux Foundation created the Community Data License Agreement (CDLA) for this purpose. Using this license, end users can share data and make it available for researchers, who in turn can further develop the necessary models and applications that benefit the telecom ecosystems and ultimately the end users.
Related Open Source Landscape
1-2 pages
The legal and technical framework for sharing research is important, but what is really essential for driving innovations is the ability for Open Source to provide a forum for creating communities with shared purpose. These communities by allowing people from various companies, diverse skill levels and different cultural perspectives to collaborate, have the potential to spark real innovation in ways that are not really possible in any other context.
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Experience with OSS in other domains shows that whenever there is an OSS technology that powers commercial products or offerings, there is a need to validate the products to make sure they are properly using the OSS technology and are ready to use in a predictable manner. Such validation/verification programs have existed as part of OSS ecosystems for a while. The Cloud Native Computing Foundation (CNCF) has a successful "Certified Kubernetes" program that helps vendors and end users ensure that Kubernetes distributions provide all the necessary APIs and functionality. A similar approach needs to be applied to any OSS Networking AI projects. Users should have a certain level of confidence, knowing that the OSS based AI Networking solution they use will behave as expected.
Common Vision: Intelligence plane for XG Networks
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Drawing from the IMT-2030 vision, the integration of AI into mobile networks unlocks transformative capabilities. Advanced human-machine interfaces, such as extended reality (XR) displays and haptic sensors, offer immersive experiences, blurring the boundaries between physical and virtual realms. Intelligent machines, equipped with machine perception and autonomous capabilities, facilitate seamless interactions and drive digital economic growth and societal changes.
Call for Action
0.25 page
The Linux Foundation's role in joining the AI revolution underscores the importance of open-source collaboration in advancing network capabilities. By harnessing open-source technologies, network operators can leverage the collective expertise of the community to accelerate innovation and adoption of AI-driven solutions. This collaborative approach not only democratizes access to advanced AI capabilities but also fosters interoperability and scalability across diverse companies and network environments.
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- Collaborate on Network Super Blueprints or develop new ones: https://wiki.lfnetworking.org/x/ArAZB
- Work on the Thoth project - Telco Data Anonymizer Project
- Join LFN AI mailing list: https://lists.lfnetworking.org/g/lfn-ai-taskforce
Final Thoughts
In conclusion, the future of networks in the era of 6G and beyond hinges on the transformative power of AI, fueled by open-source collaboration. By embracing AI-driven intelligence, networks can enhance situational awareness, performance, and capacity management, while enabling quick responses to address undesired states. As we navigate this AI-powered future, the convergence of technological innovation and open collaboration holds the key to unlocking boundless opportunities for progress and prosperity in the telecommunications landscape.
References
[1] Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems 30 (2017).
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