



NeoTec                                                  Jiayuan. Hu, Ed.
Internet-Draft                                             F. Zhang, Ed.
Intended status: Informational                               Y. Zhu, Ed.
Expires: 7 January 2026                                           C. Xie
                                                           China Telecom
                                                             6 July 2025


             Use cases in network operations in telco cloud
                    draft-hu-neotec-usecases-notc-00

Abstract

   This document presents two network operations in telco cloud
   orchestration use case for AI-based video recognition in smart city
   management and dynamic high-bandwidth transport.  Key innovations
   include dynamic resource scheduling across heterogeneous computing
   (GPU/NPU) and network domains, centralized training with distributed
   inference, and low-latency data transmission compliant with data
   sovereignty requirements.  Additionally, the use case demonstrates
   elastic bandwidth provisioning and failover mechanisms to ensure
   reliability.  The framework highlights the need for standardized
   interfaces between cloud and network controllers to optimize
   performance, resource utilization, and QoS in telecom cloud
   environments.

Status of This Memo

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Copyright Notice

   Copyright (c) 2025 IETF Trust and the persons identified as the
   document authors.  All rights reserved.




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   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents (https://trustee.ietf.org/
   license-info) in effect on the date of publication of this document.
   Please review these documents carefully, as they describe your rights
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   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Conventions Used in This Document . . . . . . . . . . . . . .   3
     2.1.  Requirements Language . . . . . . . . . . . . . . . . . .   3
     2.2.  Abbreviations . . . . . . . . . . . . . . . . . . . . . .   3
   3.  Problem Statement . . . . . . . . . . . . . . . . . . . . . .   3
   4.  Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . .   4
     4.1.  Example 1: AI-based Video Recognition for City
           Management  . . . . . . . . . . . . . . . . . . . . . . .   4
     4.2.  Example 2: dynamic high-bandwidth transport . . . . . . .   6
   5.  Requirements  . . . . . . . . . . . . . . . . . . . . . . . .   9
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  10
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .  10
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  10
     8.1.  Normative References  . . . . . . . . . . . . . . . . . .  10
   Contributors  . . . . . . . . . . . . . . . . . . . . . . . . . .  10
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  10

1.  Introduction

   This document presents two network operations in telco cloud
   scheduling use case including AI-based video recognition in smart
   city management and dynamic high-bandwidth transport.  The AI-based
   video use case addresses critical urban governance challenges
   including illegal street vending, unauthorized parking, garbage
   disposal, and waste classification through intelligent video
   analysis.

   dynamic high-bandwidth transport is an innovative network solution to
   address the challenges of large-scale, cross-regional data migration
   for high-performance computing (HPC), AI training, scientific
   research, and enterprise applications.  It provides on-demand,
   elastic, and secure high-bandwidth connectivity tailored for
   temporary or periodic bulk data transfers, significantly reducing
   costs and improving efficiency compared to traditional methods like
   physical hard disk shipping or fixed-bandwidth dedicated lines.  It
   enables instant setup and teardown of connections, allowing users to
   request bandwidth (1G-100G) only when needed (e.g., for scheduled



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   nighttime transfers).  Supports multi-dimensional billing (bandwidth,
   duration, distance, traffic volume, or usage frequency).  Moreover,
   dynamic high-bandwidth transport can dynamically adjust bandwidth
   (e.g., from 30M to 10G in seconds) to match data transfer demands and
   implements network slicing (FlexE/IPv6+), SRv6 tunneling, and
   encryption to ensure data isolation and integrity.

   One of the example of dynamic high-bandwidth transport is LHAASO
   cosmic ray observatory, it transfers 11PB data from Sichuan to
   Beijing for processing per year.  Reduced a 1.6TB transfer over 2,000
   km to 40 minutes (vs. days via hard disks).  In AI/ML training area,
   dynamic high-bandwidth transport supports large-scale dataset
   migration to GPU clusters for distributed training.  Moreover, PB-
   scale video rendering can be uploaded to cloud-based post-production
   studios in hours (e.g., 2TB/day via 10Gbps) in media production
   scenario.

2.  Conventions Used in This Document

2.1.  Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
   "OPTIONAL" in this document are to be interpreted as described in BCP
   14 [RFC2119] [RFC8174] when, and only when, they appear in all
   capitals, as shown here.

2.2.  Abbreviations

   LHAASO: Large High Altitude Air Shower Observatory

   UCMP: Unequal-Cost Multi-Path routing

3.  Problem Statement

   Telecom Clouds integrate compute, storage, and networking resources
   to deliver low-latency, high-bandwidth services such as 5G, AI/ML
   workloads, and real-time media processing.  Unlike public clouds that
   depend on third-party networks, Telecom Clouds are operated under a
   single administrative domain, enabling tight coupling between cloud
   infrastructure and network operations.  However, existing network
   management systems lack real-time visibility into dynamic cloud
   resource states, resulting in suboptimal performance, inefficient
   resource utilization, and SLA violations.  Key challenges include:







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   1.  Network controllers remain unaware of cloud-side scaling events
   (e.g., VM/container orchestration, GPU resource allocation),
   preventing dynamic adjustments to load balancing, UCMP routing, or
   QoS policies.

   2.  While cloud platforms (e.g., AWS CloudWatch, Azure Monitor)
   expose resource metrics, no standardized APIs or data models exist
   for network controllers to ingest and act on this telemetry in real
   time.

   3.  AI/ML pipelines, 5G network slicing, and inter-cloud traffic
   exhibit highly variable patterns.  Without real-time coordination
   between cloud resource availability and network state, traffic
   engineering becomes reactive, leading to congestion, unbalanced
   resource usage, and degraded QoE.

   4. standardized interface for informing routing decisions like UCMP
   weight adjustments, flow steering, or bandwidth
   allocation.[draft-li-unco-framework]

   5.  Traditional network orchestrators often pre-allocate resources
   statically or based on historical models, but modern applications
   demand rapid provisioning and adjustment of both compute and network
   resources.  Real-Time and dynamic resource scheduling ability is
   needed.[draft-li-unco-framework]

   To solving the problem is critical to achieving true cloud-network
   convergence, where dynamic cloud workloads and network resources are
   orchestrated as a unified system.

4.  Use Cases

4.1.  Example 1: AI-based Video Recognition for City Management

   This use cases leverages cloud-network-computing integration to
   enable intelligent urban governance through real-time video
   analytics.  Key Applications include

   1.  Illegal Street Vending Detection: Identifies static objects
   (e.g., tables, chairs) left in restricted zones for prolonged
   periods, indicating unauthorized vending activities.

   2.  Unauthorized Parking Monitoring: Detects vehicles parked in no-
   parking areas by analyzing predefined zones in video feeds.

   3.  Litter and Waste Management: Flags scattered waste (bottles,
   paper, bags) on streets and overflowing/uncovered trash bins.




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   4.  Public Space Compliance: Monitors violations like disorderly
   wiring and shopfront obstructions.

   For the cameras used in urban management, the main network structure
   is shown in the following figure:

                              +---------------------+
                              |     Street Cameras  |
                              +---------------------+
                                  /             \
                                 /               \
                 (Cellular Access)           (WiFi Access)
                               /                 \
                         +--------+         +-------------+
                         |  eNB   |         |  WiFi AP   |
                         +--------+         +-------------+
                               |                   |
                               |                   |
                 +----------------------+    +----------------------+
                 |        UPF           |    |    Access PE Router  |
                 +----------------------+    +----------------------+
                         |                             |
                         |                             |
                         |                             |
                        +-------------------------------+
                        |        Provider Transport     |
                        |            Network (TE)       |
                        +-------------------------------+
                                       |
                                       |
                                       v
                         +---------------------------+
                         |  Edge PE Router (per site)|
                         +---------------------------+
                                       |
                                       |
                                       v
                             +--------------------+
                             |  Edge Cloud Gateway|
                             |  + AI Model Module |
                             +--------------------+

       Figure 1: The framework of AI-based video recognition for city
                                 management

   In the cloud-network convergence architecture, AI cameras transmit
   data via cellular networks (e.g., through eNBs/gNBs and User Plane
   Functions (UPFs)) or WiFi Access Points.For cellular access, data is



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   forwarded via GTP-U tunnels from eNBs to UPFs, which are often co-
   located with Edge Cloud sites.  Data traverses the provider's
   transport network between the access point (PE router) and the Edge
   Cloud PE router.  The Edge PE router connects to the Edge Cloud
   Gateway or compute node hosting the AI workload (e.g., real-time
   inference modules).  A Cloud Manager evaluates end-to-end paths
   (bandwidth, latency, topology) between cameras and Edge Cloud sites
   to select optimal deployment locations for AI models.Network
   controllers dynamically adjust UCMP (Unequal Cost Multipath) load-
   balancing algorithms to meet performance constraints (e.g., XX Gbps
   bandwidth, YY ms delay) for inter-site data exchange.
   [draft-dunbar-neotec-ac-te-applicability]

   This architecture ensures low-latency, high-throughput data
   transmission for real-time AI processing while enabling dynamic
   resource allocation based on network-aware metrics.  The solution
   leverages edge computing infrastructure to deploy AI inference models
   closer to data sources, enabling real-time processing of high-
   resolution video streams with millisecond-level response times.  Key
   technical components include:

   1.Centralized training at the group data center with distributed edge
   inference

   2.Dynamic resource orchestration across heterogeneous computing
   facilities (GPU/NPU-enabled edge nodes)

   3.Cloud-aware network optimization ensuring low-latency data
   transmission

   4.Data sovereignty compliance through localized processing

4.2.  Example 2: dynamic high-bandwidth transport

   The dynamic high-bandwidth transport is an innovative network
   solution designed to address the challenges of large-scale, high-
   efficiency data migration for scenarios such as scientific computing,
   AI training, and cross-regional data transfers.  Typical Use Cases
   like East-to-West Data Storage: Low-cost cold/backup data transfers
   to western data centers.  Scientific Computing: Supports projects
   like the LHAASO cosmic ray observatory (11PB/year data) with high-
   speed links to supercomputing centers.  AI/Media Production:
   Accelerates raw footage (e.g., 2TB/day) or AI model training data
   transfers.

   The network architecture of thedynamic high-bandwidth transport line
   service comprises three layers: the service enabling layer, the
   service core layer, and the business carrying layer.  It can provide



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   services for various data transmission businesses such as gene
   sequencing, scientific computing, cloud-to-cloud storage, film and
   television production, artificial intelligence, and more.  The
   service enabling layer, through user-oriented unified APIs, SDKs, or
   service platforms, invokes network capabilities and allocates network
   resources on demand based on the business requirements transmitted by
   various applications, generating a combination of network
   capabilities and business capabilities.  The service provides users
   with network capabilities such as elastic bandwidth, security
   isolation, flexible networking, deterministic resource assurance, and
   flexible billing based on usage, according to the business requests
   transmitted by the service enabling layer.  The business carrying
   layer, as the physical carrier providing data transportation, builds
   on-demand, deterministic, secure, and reliable network channels
   between communicating parties, including network functional entities
   such as access terminals, super business gateways, and routers.  The
   specific network architecture is shown in Figure 2.


































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                            +---------------------+
                            |     Applications    |
                            +---------------------+
                                      ^
                                      |
                         API/service-oriented interfaces
                                      |
                                      v
                +--------------------------------------------+
                |  service enabling layer                    |
                |   +---------------------+   +----------+   |
                |   | resources on demand |   | security |   |
                |   +---------------------+   +----------+   |
                +--------------------------------------------+
                                      ^
                                      |
         combination of network capabilities and business capabilities
                                      |
                                      v
                +--------------------------------------------+
                |  service core layer                       |
                | +-------------------+ +-----------------+  |
                | | elastic bandwidth | |flexible billing |  |
                | +-------------------+ +-----------------+  |
                | +----------------------------------+       |
                | | deterministic resource assurance |       |
                | +----------------------------------+       |
                +--------------------------------------------+
                       |                             ^
                       |                             |
               control signal        business information collection
                       |                             |
                       v                             |
          +--------------------------------------------------------+
          |  business carrying layer                               |
          |  +---------------------+ +---------------------------+ |
          |  | data transportation | | reliable network channels | |
          |  +---------------------+ +---------------------------+ |
          |  +-------------------------+                           |
          |  | super business gateways |                           |
          |  +-------------------------+                           |
          +--------------------------------------------------------+

        Figure 2: The framework of dynamic high-bandwidth transport







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   Overall, dynamic high-bandwidth transport can significantly reducing
   costs and improving efficiency compared to traditional methods like
   physical hard disk shipping or fixed-bandwidth dedicated lines.
   Here's an overview of its key aspects:

   1.  Task-Based On-Demand Service: Supports instant setup and teardown
   of connections (1G-100G bandwidth) for temporary or scheduled data
   transfers (e.g., nighttime off-peak usage).

   2.  Elastic Bandwidth: Allows dynamic adjustment of bandwidth (e.g.,
   from 100M to 10G) to meet burst demands while maintaining cost
   efficiency.

   3.High-Bandwidth and Low-Latency: Optimizes protocols (e.g., TCP/
   UDP), leverages wide-area RDMA for lossless transmission, and uses
   load balancing (e.g., SRv6 UCMP) to maximize throughput.

   4.  Security and Reliability: Ensures end-to-end isolation via FlexE
   slicing and VPNs, with built-in encryption (IPSec) and route
   authentication (RPKI).

   5.  Cross-Domain Coordination: Enables multi-domain/operator
   collaboration through centralized or distributed control planes for
   seamless resource scheduling.

5.  Requirements

   To enable seamless cloud-network integration across edge, core, and
   transport environments, cloud-network integration framework
   establishes a set of functional requirements that drive its
   architecture and interface design.  These requirements prioritize:

   1.  To achieve dynamic resource scheduling, the system MUST support
   real-time elastic scaling of computing resources (e.g., GPU
   containers) and network bandwidth based on AI workload fluctuations.

   2.  Upon detecting GPU node failures or BGP route oscillations, the
   system SHOULD automatically migrate services to back up nodes and
   activate OTN protection rings within 60 seconds.

   These requirements emphasize responsiveness, reliability, and
   compatibility in multi-domain environments ensures cloud-native
   applications (e.g., AI/ML, XR) achieve deterministic performance
   while maintaining operational efficiency in cloud-network fused
   environments.






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6.  IANA Considerations

   TBC

7.  Security Considerations

   TBC

8.  References

8.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <https://www.rfc-editor.org/info/rfc8174>.

   [draft-li-unco-framework]
              "Unified Network and Cloud Orchestration Framework".

   [draft-dunbar-neotec-ac-te-applicability]
              "Applying Attachmet Circuit and Traffic Engineering YANG
              Data Model to Edge AI Use Case".

Contributors

   Thanks to all of the contributors.

Authors' Addresses

   Jiayuan Hu (editor)
   China Telecom
   109, West Zhongshan Road, Tianhe District
   Guangzhou
   Guangdong, 510000
   China
   Email: hujy5@chinatelecom.cn










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   Fan Zhang (editor)
   China Telecom
   109, West Zhongshan Road, Tianhe District
   Guangzhou
   Guangdong, 510000
   China
   Email: zhangf52@chinatelecom.cn


   Yongqing Zhu (editor)
   China Telecom
   109, West Zhongshan Road, Tianhe District
   Guangzhou
   Guangdong, 510000
   China
   Email: zhuyq8@chinatelecom.cn


   Chongfeng Xie
   China Telecom
   Beiqijia Town, Changping District
   Beijing
   102209
   China
   Email: xiechf@chinatelecom.cn


























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