Consideration for Computing-Power Collaboration in Computing-Aware Traffic Steering (CATS)
draft-wang-cats-computing-power-collaboration-00

cats                                                             J. Wang
Internet-Draft                                              China Mobile
Intended status: Informational                                    J. Guo
Expires: 3 September 2026           Inspur Computer Technology Co., Ltd.
                                                            2 March 2026

   Consideration for Computing-Power Collaboration in Computing-Aware
                        Traffic Steering (CATS)
            draft-wang-cats-computing-power-collaboration-00

Abstract

   This document outlines a series of challenges and considerations to
   explore computing-power collaboration in CATS.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Definition of Terms . . . . . . . . . . . . . . . . . . . . .   3
   3.  Challenges  . . . . . . . . . . . . . . . . . . . . . . . . .   3
     3.1.  Architectural Heterogeneity . . . . . . . . . . . . . . .   3
     3.2.  Differences in Network Protocols  . . . . . . . . . . . .   4
     3.3.  Challenging Cross-domain Collaboration  . . . . . . . . .   5
   4.  Consideration . . . . . . . . . . . . . . . . . . . . . . . .   5
     4.1.  Flexible Conversion of Agreements . . . . . . . . . . . .   6
     4.2.  Long-distance Low-latency Routing . . . . . . . . . . . .   6
     4.3.  Exposure of Energy Status in Computing Resource Supply  .   7
   5.  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .   8
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .   8
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   8
   8.  Informative References  . . . . . . . . . . . . . . . . . . .   8
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   8

1.  Introduction

   With the continuous development and progress of the Internet, a large
   amount of computing resources is required to complete data
   processing.  In order to disperse the pressure of cloud data centers,
   computing power gradually moves from the center to the edge, forming
   scattered computing resources in mobile networks.  In order to make
   full use of scattered computing resources and provide better
   services, Computing-Aware Traffic Steering (CATS) is proposed to
   support steering the traffic among different edge sites according to
   both the real-time network and computing resource status as mentioned
   in [I-D.ietf-cats-usecases-requirements].  It requires the network to
   be aware of computing resource information and select a service
   instance based on the joint metric of computing and networking.

   As artificial intelligence technology advances into the era of large
   models, the demand for computational power for AI training has grown
   exponentially.  This has resulted in a continuous increase in
   electricity consumption.  From pre-training to fine-tuning and
   ongoing iterative optimization, massive computing clusters operate
   under high loads for extended periods of time.  As a result,
   electricity costs, power supply stability, and energy provision
   capacity directly impact the training efficiency, deployment scale,
   and iteration speed of AI models.  Today, power supply is no longer
   just a cost issue, but it has become a critical bottleneck that
   hinders the further scaling and industrialization of AI technology.

   Whether it's the construction of hyperscale data centers, the
   deployment of intelligent computing centers, or the development and
   application of large-scale models, all rely heavily on stable, green,

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   and low-cost power supply.  Tight power resources, regional
   disparities in supply capacity, and energy consumption control
   policies are profoundly shaping the development pace and spatial
   layout of the AI industry.  Today, power has become a core constraint
   on AI advancement—a conclusion no longer confined to individual
   companies' observations but a widely recognized industry consensus
   spanning the entire industrial chain from research and development to
   application.

   Green requirements for AI training scenarios have now been formally
   incorporated into [I-D.ietf-cats-usecases-requirements].  This
   document outlines a series of challenges and considerations to
   explore computing-power collaboration in CATS.

2.  Definition of Terms

   Computing-Aware Traffic Steering (CATS):  Aiming at computing and
     network resource optimization by steering traffic to appropriate
     computing resources considering not only routing metric but also
     computing resource metric.

   Service:  A monolithic functionality that is provided by an endpoint
     according to the specification for said service.  A composite
     service can be built by orchestrating monolithic services.

   Service instance:  Running environment (e.g., a node) that makes the
     functionality of a service available.  One service can have several
     instances running at different network locations.

3.  Challenges

   Computing-Power Collaboration faces a series of challenges.

3.1.  Architectural Heterogeneity

   The network architectures of Power Network and Computing-Aware
   Network originate from distinct design philosophies and application
   scenarios, exhibiting significant heterogeneity.  This makes it
   difficult to coordinate computing and power management..

   Power system networks use a "layered, partitioned, closed-loop
   control" architecture and primarily support grid dispatch, equipment
   monitoring, and fault isolation.  Nodes are dispersed and mainly
   located at industrial sites, with network topologies dominated by
   trees and rings, prioritizing stability and controllability.

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   In contrast, Computing-Aware Network are centered around data centers
   and intelligent computing clusters, utilizing a "flat, highly
   aggregated" architecture.  The focus is on supporting large-scale
   data transmission, computing power scheduling, and distributed
   computing.  The nodes are highly concentrated and the topologies are
   primarily based on spine-leaf architectures, prioritizing bandwidth
   and transmission efficiency.

   However, the significant differences in design objectives, topology
   structures, and node characteristics between these two architectures
   present challenges for efficient interconnection during computing
   power coordination.  The exchange of data and scheduling of resources
   must overcome architectural barriers, resulting in increased network
   deployment and modification costs, as well as issues such as data
   transmission delays and resource scheduling disconnects.  These
   factors greatly hinder the overall efficiency of computing-power
   coordination.

3.2.  Differences in Network Protocols

   The Power Network and Computing-Aware Network have historically
   developed independently, each with its own closed network protocol
   system.  However, this has resulted in protocol inconsistencies that
   pose challenges for data exchange and command transmission during
   power-computing coordination.  This has become a major technical
   bottleneck.

   The Power Network primarily uses specialized industrial protocols
   such as IEC 61850 and DL/T 860, which are designed for real-time
   control and equipment monitoring in the grid.  These protocols
   prioritize low-latency and high-reliability transmission of small
   data packets, meeting the communication needs of power equipment.
   However, they struggle to seamlessly integrate with general-purpose
   network protocols, leading to compatibility issues.

   On the other hand, the Computing-Aware Network mainly relies on the
   TCP/IP protocol suite, which includes general-purpose protocols like
   HTTP, FTP, and RDMA.  These protocols prioritize the transmission of
   large data volumes and high bandwidth, catering to scenarios such as
   computing resource scheduling and data exchange.  However, they lack
   the ability to adapt to the real-time control commands of power
   systems.

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   As a result, direct interaction between power system operational
   data/control commands and computing system workload/scheduling
   demands is hindered, requiring the deployment of additional protocol
   conversion devices.  This increases system complexity and operational
   costs, while also introducing extra transmission delays and the risk
   of data packet loss.  Ultimately, this compromises the real-time
   performance and reliability of computing-power collaboration.

3.3.  Challenging Cross-domain Collaboration

   The core requirement for cross-domain computing-power coordination is
   to achieve dynamic matching and real-time scheduling of computing
   resources and power resources.  This process imposes extremely high
   demands on network latency, presenting a critical challenge that
   constrains coordination effectiveness.  In cross-regional computing-
   power coordination scenarios, such as ultra-long-distance
   interconnection, cross-regional virtual power plant coordination, and
   intelligent computing cluster scheduling, real-time collection of
   power system load data, renewable energy output data, and computing
   system workload/energy consumption data is essential.

   This data must be transmitted via networks to the coordination
   dispatch center, where it undergoes analysis and decision-making
   before dispatch instructions are relayed back to terminal nodes,
   forming a closed-loop “collection-analysis-decision-execution”
   process.  This closed-loop process imposes extremely stringent
   latency requirements.  End-to-end latency for power control commands
   must be kept below 10ms, with certain critical scenarios demanding
   ≤5ms, while jitter must be ≤1ms to ensure synchronized execution of
   dispatch commands.

   However, current wide-area networks suffer from complex transmission
   links, multiple cross-domain routing hops, and network congestion,
   making it difficult to consistently meet these latency requirements.
   Exceeding latency thresholds may cause computational power scheduling
   delays, untimely power load transfers, and even risks such as grid
   frequency fluctuations or computational cluster overloads, severely
   compromising the safety and effectiveness of computing-power
   coordination.

4.  Consideration

   To better achieve computing-power coordination, it is necessary to
   enable flexible protocol conversion; long-distance low-latency
   routing; and exposure of energy status for computing resource supply.

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4.1.  Flexible Conversion of Agreements

   Flexible protocol conversion serves as the core enabler for resolving
   protocol incompatibilities between power and computing systems,
   achieving power-computing synergy, and overcoming the challenges of
   data isolation and command transmission difficulties.

   To address compatibility challenges between specialized industrial
   protocols for power systems (e.g., IEC 61850, DL/T 860) and general-
   purpose protocols for computing systems (e.g., TCP/IP protocol suite,
   RDMA), a flexible and efficient protocol conversion framework must be
   established—not merely deploying single conversion devices.  A
   modular, scalable conversion architecture should be adopted to
   support real-time parsing, adaptation, and conversion of multiple
   protocols.  This enables both the transformation of power system
   control commands and operational data into computing system protocols
   for efficient transmission to the collaborative dispatch center, and
   the conversion of computing system dispatch commands into specialized
   protocols recognizable by power equipment to ensure precise
   execution.

   Simultaneously, the conversion process must balance low latency with
   high reliability, avoiding additional transmission delays and data
   packet loss.  By optimizing conversion algorithms and streamlining
   conversion workflows, millisecond-level response times for protocol
   conversion can be achieved.  This ensures seamless data interaction
   and command transmission during computing-power coordination, laying
   the foundation for cross-system collaborative dispatch.

4.2.  Long-distance Low-latency Routing

   Long-distance low-latency routing design is a critical measure for
   meeting the high-latency requirements of cross-domain computing-power
   coordination and enabling dynamic cross-regional matching of
   computing and power resources.  To address current challenges such as
   complex wide-area network links, multiple cross-domain routing hops,
   and frequent congestion, a dedicated wide-area routing system for
   computing-power coordination must be established, balancing long-
   distance transmission with low-latency requirements.

   On one hand, optimized routing planning algorithms should dynamically
   select optimal transmission paths based on computing-power
   coordination service priorities, minimizing routing hops and avoiding
   congested network segments to ensure the shortest paths and lowest
   latency for cross-domain data transmission and command delivery.

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   On the other hand, implementing deterministic networking technologies
   such as TSN+SRv6 through techniques like time slot scheduling and
   path reservation ensures stability and predictable latency for long-
   distance transmission.  This results in a cross-domain end-to-end
   latency of less than 10ms, reduces core scenarios to under 5ms, and
   maintains jitter within 1ms.

   Simultaneously, deploying multi-path redundant routing addresses link
   failures, enabling sub-second fault self-healing.  This ensures
   uninterrupted, low-latency transmission over long distances,
   supporting the efficient implementation of cross-domain computing-
   power coordination scenarios like “East Data, West Computing” and
   “West Power, East Transmission.”

4.3.  Exposure of Energy Status in Computing Resource Supply

   Exposing the energy status of computing resources is a crucial
   prerequisite for achieving dynamic coordination between computing and
   power systems, enhancing resource utilization efficiency, and
   breaking down barriers to matching computing and power resources.
   Currently, computing systems and power systems operate independently.
   The energy consumption, energy efficiency, and power supply
   requirements of computing clusters are not effectively exposed to
   power dispatch systems.

   Similarly, information such as the power supply capacity, green power
   generation output, and load fluctuations of the power system is not
   synchronized to computing dispatch systems, leading to a disconnect
   in resource matching between the two.  Therefore, it is necessary to
   establish a unified computing-power status perception system to
   promote the comprehensive exposure and sharing of energy status
   information for computing resource supply.  Specifically, deploy
   energy monitoring devices within computing clusters to collect real-
   time data on energy consumption, power demands, and energy efficiency
   from computing nodes.  This information should be transmitted via
   standardized interfaces to the computing-power coordination dispatch
   center.

   Simultaneously, synchronize real-time power system data—including
   supply load, green power generation output, and electricity price
   fluctuations—to the computing dispatch system.  This achieves
   bidirectional transparency between computing energy status and power
   supply conditions.  Through this state exposure, the coordination
   center can precisely grasp the resource status of both parties,
   enabling deep synergy between computing power scheduling and power
   dispatch.  This optimizes resource allocation, enhances green
   electricity consumption rates, and improves computing power
   operational efficiency.

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5.  Conclusion

   This document highlights the challenges and considerations for
   Computing-Power Collaboration in CATS.

6.  Security Considerations

   TBD.

7.  IANA Considerations

   TBD.

8.  Informative References

   [I-D.ietf-cats-usecases-requirements]
              Yao, K., Contreras, L. M., Shi, H., Zhang, S., and Q. An,
              "Computing-Aware Traffic Steering (CATS) Problem
              Statement, Use Cases, and Requirements", Work in Progress,
              Internet-Draft, draft-ietf-cats-usecases-requirements-14,
              2 February 2026, <https://datatracker.ietf.org/doc/html/
              draft-ietf-cats-usecases-requirements-14>.

Authors' Addresses

   Jing Wang
   China Mobile
   No.32 XuanWuMen West Street
   Beijing
   100053
   China
   Email: wangjingjc@chinamobile.com

   Jianchao Guo
   Inspur Computer Technology Co., Ltd.
   Beijing
   China
   Email: guojianchao01@inspur.com

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