NeoTec L. Dunbar, Ed.
Internet-Draft Futurewei
Intended status: Informational Q. Sun
Expires: 13 November 2025 China Telecom
B. Wu, Ed.
Huawei
L. CONTRERASMURILLO, Ed.
Telefornica
12 May 2025
Applying Attachmet Circuit and PE2PE YANG Data Model to dynamic policies
Use Case
draft-dunbar-neotec-ac-pe2pe-ucmp-applicability-00
Abstract
This document explores how existing IETF YANG data models can be
applied to support a use case involving dynamic, time-scoped UCMP
(Unequal Cost Multipath) policy enforcement across multiple network
segments interconnecting Edge Cloud sites. The use case is motivated
by periodic, high-volume data exchanges between distributed AI
inference modules placed at geographically dispersed edge data
centers. By mapping network requirements such as bandwidth, latency,
and availability to relevant YANG models, including AC, TE Topology,
SR Policy, and QoS models, this document serves as a practical
exercise to evaluate the applicability and limitations of current
IETF specifications. It highlights the need for cloud-initiated,
time-bounded network policy activation and identifies potential gaps
in model expressiveness, policy lifecycle handling, and API-level
abstraction required for real-time cloud-network coordination.
Status of This Memo
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This Internet-Draft will expire on 13 November 2025.
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Copyright Notice
Copyright (c) 2025 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents (https://trustee.ietf.org/
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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 . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Conventions used in this document . . . . . . . . . . . . . . 5
3. Dynamic UCMP Load Balancing for Periodic Inter Site AI
Traffic . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.1. UCMP Enforcement for Access Segment . . . . . . . . . . . 6
3.2. UCMP Enforcement for SRv6 based PE to PE segment . . . . 11
3.3. UCMP Enforcement for PE to PE segment in Non-SRv6
networks . . . . . . . . . . . . . . . . . . . . . . . . 12
3.3.1. UCMP over MPLS-TE . . . . . . . . . . . . . . . . . . 12
3.3.2. UCMP Over Plain IP (ECMP) . . . . . . . . . . . . . . 13
4. Cloud-Initiated UCMP Activation API . . . . . . . . . . . . . 16
5. Gaps Analysis . . . . . . . . . . . . . . . . . . . . . . . . 16
5.1. Access Segment Gaps Analysis . . . . . . . . . . . . . . 17
5.2. PE to PE Segment Gaps Analysis . . . . . . . . . . . . . 17
5.3. Complexity of Hop-by-Hop UCMP Configuration in IP
Networks . . . . . . . . . . . . . . . . . . . . . . . . 18
5.4. Inter-Domain Coordination Gaps Analysis . . . . . . . . . 18
6. Security Considerations . . . . . . . . . . . . . . . . . . . 19
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 19
8. References . . . . . . . . . . . . . . . . . . . . . . . . . 19
8.1. Normative References . . . . . . . . . . . . . . . . . . 19
8.2. Informative References . . . . . . . . . . . . . . . . . 19
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . 20
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 20
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1. Introduction
Edge computing enables latency-sensitive applications such as AI
inference to be deployed closer to end users and data sources. In
many deployments, AI workloads are dynamically instantiated across
multiple Edge Cloud sites based on compute availability, proximity to
data sources, and overall service objectives. These distributed AI
inference modules often need to exchange large volumes of data
periodically, for example, during model synchronization, aggregated
result sharing, or collaborative analytics.
These inter-site data exchanges typically require high bandwidth and
low latency for short, well-defined time windows. However, most
transport networks are optimized for long-lived, static flows and do
not natively support time-scoped policy enforcement. Furthermore,
existing routing mechanisms like Equal Cost Multipath (ECMP) are
insufficient for granular traffic distribution when link costs and
available bandwidth are unequal. As a result, Unequal Cost Multipath
(UCMP) techniques have emerged to enable more efficient load
balancing across heterogeneous paths.
This document focuses on the application of dynamic, cloud-initiated
UCMP policy updates across multiple segments of the network
interconnecting Edge Cloud sites. It assumes the network supports
multiple paths between sites and that these paths can be selected or
weighted based on real-time performance metrics (e.g., latency,
available bandwidth, load).
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+---------------------+ +---------------------+
| Edge Cloud Site A | | Edge Cloud Site B |
+---------------------+ +---------------------+
| |
+-------------+-------------+ +------------+-------------+
| Access PE / Gateway | | Access PE / Gateway |
+-------------+-------------+ +------------+-------------+
\ /
\ /
\ /
\ /
\ /
Multiple Provider Transport Segments
(with dynamic UCMP policies)
/ \
/ \
/ \
/ \
+-------------+-------------+ +------------+-------------+
| Access PE / Gateway | | Access PE / Gateway |
+-------------+-------------+ +------------+-------------+
| |
+---------------------+ +---------------------+
| Edge Cloud Site C | | Edge Cloud Site D |
+---------------------+ +---------------------+
Periodic AI Traffic Exchange
requires time scoped dynamic UCMP adjustments
Figure 1: Network Segments for AI Data Exchange
The primary goal of this work is to:
- Demonstrate how IETF defined YANG data models, such as ietf-
routing, ietf-ac, ietf-te-topology, ietf-qos-policy, and ietf-sr-
policy, can be used to realize dynamic UCMP enforcemen.
- Highlight the requirements for time-scoped policy activation and
cloud-initiated triggers, which are not natively supported in
existing models.
- Propose augmentations or extensions needed to support temporary
policy enforcement tied to cloud workload events.
- Identify architectural and modeling gaps that must be addressed to
enable closed-loop coordination between cloud orchestration systems
(e.g., Kubernetes) and network controllers.
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By framing this scenario as a concrete use case, the document serves
both as an applicability exercise and as input for future
standardization efforts within the IETF aimed at cloud-network
integration and SLA-driven policy control.
Disclaimer
The use of specific YANG data models (e.g., Attachment Circuit and TE
topology) in this section is intended as a provisional exercise to
explore how existing IETF models might address aspects of such a use
case. These examples are not exclusive or exhaustive. Other data
models, such as Network Slicing Service Model (NSSM) or service
function chaining models, could also be relevant depending on the
network architecture and service requirements. The intent is to
assess the applicability and identify gaps (if any), not to pre-
define the final solution set.
2. Conventions used in this document
Cloud Manager: An entity that is primarily responsible for placing
workloads, managing compute resources across Edge Cloud sites,
Monitoring the health and scaling status of VMs, containers, or
services, Making application-level decisions (e.g., where to place
a function based on latency, CPU, GPU availability), etc..
Network Orchestrator (or Orchestrator): A logical entity that
interfaces with the Cloud Manager to receive service requests or
queries and coordinates the end-to-end connectivity across
multiple network domains. It abstracts underlying domain-specific
technologies (e.g., L2/L3 VPNs, SR paths, TE tunnels) and
disseminates policies to individual Network Controllers, enabling
seamless stitching of diverse network segments to meet service-
level requirements.
Network Controller: A domain specific control entity responsible for
managing and configuring network resources within a single
administrative or technological domain (e.g., IP/MPLS core, access
network). It receives high-level intent or service instructions
from a Network Orchestrator and translates them into device-level
configurations using protocols such as NETCONF, BGP, or PCEP.
UE: User Equipment
UPF: User Plane Function [TS.23.501-3GPP]
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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.
3. Dynamic UCMP Load Balancing for Periodic Inter Site AI Traffic
In the Neotec use case described in Section 1, AI inference modules
are deployed across four Edge Cloud sites to support distributed city
surveillance. These modules periodically exchange large volumes of
data, for instance, during result aggregation or synchronized event
analysis. These data exchanges are not continuous but are periodic
and event driven, requiring guaranteed bandwidth and low latency for
short time windows.
The underlying network connecting these Edge Cloud sites typically
includes multiple paths between nodes and across multiple network
segments.An end-to-end path between Edge Cloud Site A and B spans at
least three segments:
- The first is the access segment from the Edge Cloud A to its
closest PE. There could be multipe PEs to Edge Cloud A for multi-
homing.
- The second segment traverses the provider transport network between
the PE serving Edge Cloud A and the PE serving the Edge Cloud B.
- The third segment connects that Edge PE to the Edge Cloud B's
Gateway
3.1. UCMP Enforcement for Access Segment
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+-----------------------------+
| Edge Cloud Site |
| +------------------------+ |
| | AI Inference Module | |
| +------------------------+ |
| | |
| +----------------+ |
| | Edge Cloud GW | |
| +----------------+ |
+-------------|---------------+
|
Multiple Attachment Circuits
|
+----------------+ +----------------+ +----------------+
| PE1 | | PE2 | | PE3 |
+----------------+ +----------------+ +----------------+
| | |
Provider Network Provider Network Provider Network
Figure 2: Edge Cloud Access Segment
The Edge Cloud Gateway has multiple logical links (attachment
circuits) to a set of PEs (e.g., PE1, PE2, PE3). These links may
vary in latency, bandwidth, or current load. During periodic AI data
bursts, the Edge Cloud GW must push all other traffic away from PE1,
PE2, and PE3, reserving their full available bandwidth for AI flows.
Or it could push all other traffic to one of the PEs that has the
lowest bandwidth and highest latency. This can be implemented by
dynamically updating forwarding policies or QoS profiles to
deprioritize or reroute non-AI traffic, ensuring that the AI
inference module has uncontested access to network capacity during
critical synchronization windows. Depending on the request from the
Cloud Manager, the Network Orchestrator can determine what exact
policies to push to the PEs and the Edge GW..
YANG Data Models for Policy Enforcement:
To support dynamic UCMP-based traffic steering across PE1, PE2, and
PE3, the Network Orchestrator can utilize the following IETF YANG
models:
- ietf-routing [RFC8349] enables configuration of routing instances
and static routes. The data model allows per-prefix route entries
with multiple weighted next hops, supporting unequal cost paths. It
can be used to define static next-hop policies from the Edge GW
toward PE1, PE2, and PE3.
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- ietf-qos-policy [I-D.claise-opsawg-qos-packet-marking] defines
traffic classification, marking, and treatment policies. It can
enforce rate limits or scheduling priority for non-AI traffic routed
to PE3.
- ietf-ac [I-D.ietf-opsawg-teas-attachment-circuit] can provides
performance characteristics (bandwidth, latency, packet loss), which
inform the decision to assign traffic classes.
- ietf-traffic-classifier / ietf-packet-policy (or OpenConfig
equivalents) can be used to match traffic classes (e.g., AI vs non-AI
flows), enables forwarding decisions at the Edge GW and ingress of
PEs.
Policy Example: Prioritized AI Flow Assignment and Non-AI Rerouting:
Assuming PE1 has the highest available capacity and best latency
toward the remote site:
The orchestrator uses ietf-routing [RFC8349] to define weighted
static next hops at the Edge GW:
- PE1: 2/3 of AI traffic.
- PE2: 1/3 of AI traffic.
- PE3: no AI traffic.
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{
"routing:routing": {
"routing-instance": [
{
"name": "ai-routing-instance",
"routing-protocols": {
"routing-protocol": [
{
"type": "static",
"name": "ai-policy",
"static-routes": {
"ipv4": {
"route": [
{
"destination-prefix": "198.51.100.0/24",
"next-hop": [
{ "outgoing-interface": "to-PE1", "weight": 66 },
{ "outgoing-interface": "to-PE2", "weight": 34 }
]
}
]
}
}
}
]
}
}
]
}
}
Figure 3: UCMP policy example
Non-AI traffic is matched by classifiers and redirected entirely to
PE3:
- Apply QoS policy using ietf-qos-policy [I-D.claise-opsawg-qos-
packet-marking] to limit available bandwidth on PE3 to non-AI
traffic.
- PE3 may be tagged "degraded" in the ietf-ac model to signal its
backup nature.
This ensures that the AI traffic is prioritized through the most
capable paths (PE1 and PE2) with precise weight. All non-critical
traffic is offloaded to the least-desired path (PE3), reducing
congestion. Orchestrator needs to dynamically adapt this logic per
service request.
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Fallback Logic: If available capacity on PE1 and PE2 is not
sufficient, AI traffic still receives best possible routing via PE1
and PE2, and Non-AI traffic may be allowed limited access to PE2 with
reduced weight (e.g., 10%) while still primarily routed via PE3.
This tiered policy enforcement ensures strict adherence to SLA goals
for AI inference while maintaining service continuity for other
traffic classes using standard YANG-based configuration interfaces.
One critical limitation in existing IETF YANG models is the lack of
native support for time-scoped UCMP policy activation. To support
the bursty nature of AI traffic, the following strategies can be
applied:
- Define policy activation via external triggers from the Cloud
Manager using an API call to the orchestrator.
- Orchestrator maintains a mapping between the requested activation
time window and the temporary configuration to be applied.
- Extend existing YANG models (e.g., ietf-routing or ietf-sr-policy)
with optional augmentation for: start-time, duration, and expiration-
action
Example augmentation (conceptual):
augment "/routing:routing/routing-instance/static-routes/route" {
leaf burst-policy-start-time {
type yang:date-and-time;
}
leaf burst-policy-duration-sec {
type uint32;
}
leaf expiration-action {
type enumeration {
enum revert-to-default;
enum retain;
}
}
}
Figure 4: Example Augmentation
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In the absence of standard YANG support, this behavior need to be
implemented in the orchestrator logic by maintaining policy lifecycle
state and timers, pushing temporary configuration via NETCONF/
RESTCONF at start-time, and reverting after duration-sec.
3.2. UCMP Enforcement for SRv6 based PE to PE segment
Assuming an SRv6 underlay among the PEs, the network controller can
use the ietf-sr-policy YANG model to update the traffic distribution
weights across pre-established paths. For example, if three SRv6
paths exist between EdgeSite-A and EdgeSite-C, the controller can
push the following configuration to the ingress node:
sr-policy {
color 4001;
endpoint "2001:db8:100::1";
candidate-paths {
preference 100;
path {
weight 70;
sid-list [2001:db8:10::1, 2001:db8:11::2];
}
path {
weight 20;
sid-list [2001:db8:20::1, 2001:db8:21::2];
}
path {
weight 10;
sid-list [2001:db8:30::1, 2001:db8:31::2];
}
}
}
Figure 5: Using SR Policy
This UCMP configuration tells the network to distribute traffic
unequally across the three paths based on their capability. The
underlying topology and metrics are derived from ietf-TE-topology and
ietf-TE models, which expose bandwidth, latency, and available
resources for each link.
Similar UCMP behavior can also be implemented over SR-MPLS, MPLS-TE,
or enhanced IP networks, using the corresponding IETF YANG models
(ietf-TE, ietf-routing, etc.). The key point is that the network
paths are preexisting, and the only dynamic action is adjusting how
traffic is forwarded among them in response to a cloud service
request.
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3.3. UCMP Enforcement for PE to PE segment in Non-SRv6 networks
In many operator networks that do not support SRv6, the PE-to-PE
segment is realized as a logical transport tunnel (e.g., MPLS-TE LSP,
IPsec tunnel, or GRE) that may traverse multiple intermediate
routers. Each of these routers may have multiple outgoing links to
their respective next hops, making end-to-end UCMP behavior dependent
on both tunnel-level path selection and interior node forwarding
decisions.
Policy Application for UCMP in Non-SRv6 Networks:
To enable UCMP behavior between PEs in non-SRv6 networks, the network
controller can provision multiple TE tunnels or IP-layer paths with
distinct performance characteristics. The UCMP forwarding behavior
is realized by adjusting traffic distribution weights among these
logical tunnels at the ingress PE (or head-end router). This setup
also requires the orchestrator to be topology-aware and TE-capable.
3.3.1. UCMP over MPLS-TE
In MPLS-TE-enabled networks, PE-to-PE traffic is carried over logical
LSPs that are pre-established and maintained by the network
controller. These LSPs can be engineered with specific bandwidth,
latency, or disjointness constraints and serve as the building blocks
for UCMP.
Key Characteristics:
- UCMP logic is enforced at the ingress PE (head-end LSR)
- Intermediate routers simply perform label switching and do not
require knowledge of traffic distribution policies.
- The network controller selects multiple LSPs and configures the PE
to assign weighted traffic across them.
YANG Models Used
- ietf-te-topology [RFC8795]: Describes available TE links and nodes.
- ietf-routing [RFC8349]: Configures static routes with weighted
next-hops over the tunnels.
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{
"routing:routing": {
"routing-instance": [{
"name": "ucmp-te",
"routing-protocols": {
"routing-protocol": [{
"type": "static",
"name": "te-policy",
"static-routes": {
"ipv4": {
"route": [{
"destination-prefix": "198.51.100.0/24",
"next-hop": [
{ "outgoing-interface": "mpls-te-tunnel-A", "weight": 60 },
{ "outgoing-interface": "mpls-te-tunnel-B", "weight": 40 }
]
}]
}
}
}]
}
}]
}
}
Figure 6: UCMP over MPLS-TE example
In this model, the network controller dynamically adjusts the route
weights during AI synchronization periods based on the request from
the service orchestrator, favoring higher-performing tunnels.
3.3.2. UCMP Over Plain IP (ECMP)
In networks without MPLS, traffic between PEs may traverse IP-based
ECMP paths. Each router independently forwards traffic using its own
hash-based load balancing over equal-cost links. Enabling UCMP over
such paths is more complex because intermediate routers may not
natively support weighted forwarding.
Options for UCMP Enforcement:
- Head-End-Based WCMP (Weighted ECMP): If routers support WCMP
extensions, the ingress PE can apply traffic weights across next-
hops. The rest of the network performs standard ECMP; however,
overall path selection remains coarse.
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- Hop-by-Hop Configuration: If consistent UCMP behavior is required,
each router along the path must be configured to support either:
Weighted static routes using ietf-routing, or Traffic classifiers and
filters using ietf-traffic-classifier or OpenConfig equivalents.
This approach increases operational complexity and may require
additional non-trival control logic.
YANG Models Used:
- ietf-routing [RFC8349]: Configures static or policy-based routes
with weighted next-hops.
- ietf-qos-policy [I-D. claise-opsawg-qos-packet-marking] and ietf-
traffic-classifier [I-D. opsawg-traffic-classifier]: Used for traffic
tagging and class-based forwarding.
Limitations: many routers do not support WCMP or allow fine-grained
weight control for ECMP paths. Lack of coordination across routers
can lead to inconsistent forwarding behavior. Telemetry feedback
from mid-path nodes is often unavailable or vendor-specific.
Here is a JSON-based example using IETF YANG models to enforce UCMP
behavior via hop-by-hop configuration, assuming each router along the
path supports: Weighted static routes (ietf-routing), Traffic
classification (ietf-traffic-classifier), and QoS forwarding rules
(ietf-qos-policy).
This example configures each router to: Match AI-related traffic
using a classifier, Apply weighted next-hops via ietf-routing, and
Enforce treatment policies using QoS configuration.
1. Traffic Classifier (per router)
{
"traffic-classifier:classifiers": {
"classifier": [
{
"name": "ai-traffic",
"description": "Classify traffic for AI application",
"match": {
"ipv4": {
"destination-address": "198.51.100.0/24"
},
"dscp": 34 // Assumes AI flows are marked with DSCP AF41
}
}
]
}
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}
2. QoS Policy for Forwarding Behavior:
{
"qos-policy:policies": {
"policy": [
{
"name": "ucmp-ai-policy",
"classifier": "ai-traffic",
"forwarding-actions": {
"outgoing-interfaces": [
{ "interface": "eth1", "weight": 70 },
{ "interface": "eth2", "weight": 30 }
]
}
}
]
}
}
3. Weighted Static Route for UCMP (applied per-hop)
{
"routing:routing": {
"routing-instance": [
{
"name": "ucmp-instance",
"routing-protocols": {
"routing-protocol": [
{
"type": "static",
"name": "static-ucmp",
"static-routes": {
"ipv4": {
"route": [
{
"destination-prefix": "198.51.100.0/24",
"next-hop": [
{ "outgoing-interface": "eth1", "weight": 70 },
{ "outgoing-interface": "eth2", "weight": 30 }
]
}
]
}
}
}
]
}
}
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]
}
}
Figure 7: Hop by Hop UCMP example
This configuration must be replicated (with appropriate interface
changes) on each router along the PE-to-PE path.Traffic
classification ensures only AI flows are subject to UCMP
behavior.Each router's forwarding engine must support policy-based
routing or weighted ECMP for this to work.Orchestration systems must
maintain synchronized state and rollback mechanisms across devices.
4. Cloud-Initiated UCMP Activation API
A simplified example of a cloud-initiated API call to the network
controller might look like:
POST /network-policy/ucmp-activation
{
"source-sites": ["EdgeSite-A", "EdgeSite-B"],
"dest-sites": ["EdgeSite-C", "EdgeSite-D"],
"start-time": "2025-05-01T10:00:00Z",
"duration-sec": 300,
"min-bandwidth-mbps": 5000,
"max-latency-ms": 10
}
Figure 8: Burst Network Request
This request informs the network controller that a high-volume, low-
latency data exchange will occur and that UCMP forwarding policies
should be applied to optimize transport between the specified sites
for the specified duration.
5. Gaps Analysis
This section evaluates the limitations and shortcomings of current
IETF YANG models and network orchestration mechanisms in supporting
dynamic, time-scoped UCMP policy enforcement across both Access and
PE-to-PE segments in multi-segment networks interconnecting Edge
Cloud sites.
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5.1. Access Segment Gaps Analysis
In the access segment between Edge Cloud gateways and PEs (e.g., PE1,
PE2, PE3), traffic distribution policies can be enforced using
weighted static routing (ietf-routing) and QoS classifiers (ietf-qos-
policy). However, the following issues remain:
- Lack of Time-Bound Policy Support: Existing YANG models do not
support time-bound routing or QoS policies natively. Implementing
time-scoped UCMP (e.g., apply for 5 minutes during AI sync) requires
custom orchestrator logic, including timers, state tracking, and
reversion mechanisms.
- No Native Lifecycle Hooks: YANG models like ietf-ac and ietf-
routing lack fields for lifecycle hooks such as start-time, duration,
or expiration-behavior, making transient policy management
cumbersome.
- Granular Traffic Steering Limitations: While attachment circuit
metrics can inform traffic placement decisions, there is no
standardized way to associate UCMP weights directly with circuit
performance in an event-driven manner.
- Policy Interference Risk: There is no clear conflict resolution
mechanism when temporary UCMP policies override existing long-term
configurations. Operators may hesitate to automate access path
reconfigurations due to unpredictable side effects.
5.2. PE to PE Segment Gaps Analysis
For the transport segment between PEs (especially non-SRv6 networks),
dynamic UCMP faces additional modeling and orchestration challenges:
- Inconsistent UCMP Support Across Technologies: While SRv6 supports
weighted traffic steering via ietf-sr-policy, non-SRv6 networks
(e.g., MPLS-TE, SR-MPLS, or IP) lack consistent models for applying
and adjusting UCMP behavior dynamically. The UCMP logic often has to
be emulated via unequal TE tunnel provisioning and static route
weight tuning.
- Topology Model Limitations: ietf-te-topology and ietf-te expose
rich metrics but are not inherently reactive to cloud events. There
is no trigger mechanism to automatically instantiate or adjust TE
tunnels based on time-scoped cloud service requests.
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- Limited Feedback Loops: Current YANG models are largely
configuration-driven. There is no standardized feedback channel from
devices to orchestrators to confirm UCMP policy status, enforcement
success, or SLA compliance during active windows.
5.3. Complexity of Hop-by-Hop UCMP Configuration in IP Networks
In IP-based networks without MPLS-TE or SRv6, traffic distribution
across multiple paths typically relies on Equal Cost Multipath
(ECMP), where each router independently forwards traffic based on
local hash-based algorithms. Applying Unequal Cost Multipath (UCMP)
in such environments requires hop-by-hop configuration to ensure
consistent behavior, introducing substantial complexity.
Identified Gaps
- Lack of Unified UCMP Capability Discovery: Current IETF YANG models
(ietf-routing, ietf-interfaces) do not expose whether a router
supports Weighted ECMP (WCMP), the granularity of supported weights,
or the hashing algorithm used for multipath forwarding. Network
controller cannot dynamically determine whether a node can
participate in UCMP enforcement.
- No Model for Coordinated Hop-by-Hop Weight Distribution:There is no
standardized method to propagate UCMP weights across multiple nodes
consistently. ietf-routing supports static routes with weighted next-
hops, but each router must be configured independently with no
assurance that downstream nodes share the same view.
- Limited Telemetry for Flow Distribution Verification: Existing
telemetry models (e.g., ietf-interfaces, ietf-te) offer counters per
interface, but do not expose real-time per-class or per-prefix
distribution across ECMP paths. Operators lack visibility into
whether UCMP objectives are being met without custom probes or
vendor-specific tools.
- Policy Lifecycle and Rollback Limitations: Time-scoped UCMP
policies require precise activation and rollback behavior. Current
models lack lifecycle attributes (e.g., start time, expiration
behavior) to automate the temporal aspect of UCMP enforcement.
5.4. Inter-Domain Coordination Gaps Analysis
- Cross-Domain Policy Stitching: When the PE-to-PE path traverses
multiple administrative domains, coordinating UCMP policy enforcement
becomes challenging. There is no standard way for an orchestrator to
request and verify consistent path weights across AS boundaries.
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- Cloud-to-Network Intent Translation: Cloud controllers (e.g.,
Kubernetes) cannot directly express intent such as "min 5Gbps to
EdgeSite-C from 10:00-10:05 UTC" in a format consumable by TE or
routing models. This necessitates the creation of a new intent or
policy model aligned with both cloud-native and IETF constructs.
6. Security Considerations
To be added
7. IANA Considerations
None
8. References
8.1. Normative References
8.2. Informative References
[Neotec-Zerotrust-Access]
L. Dunbar and H. Chihi, "Neotec-Zerotrust-Access",
December 2024, .
[opsawg-teas-attachment-circuit]
M. Boucadair, et al, "opsawg-teas-attachment-circuit",
January 2025, .
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, .
[RFC8345] Clemm, A., Medved, J., Varga, R., Bahadur, N.,
Ananthakrishnan, H., and X. Liu, "A YANG Data Model for
Network Topologies", RFC 8345, DOI 10.17487/RFC8345, March
2018, .
[RFC8639] Voit, E., Clemm, A., Gonzalez Prieto, A., Nilsen-Nygaard,
E., and A. Tripathy, "Subscription to YANG Notifications",
RFC 8639, DOI 10.17487/RFC8639, September 2019,
.
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[RFC8641] Clemm, A. and E. Voit, "Subscription to YANG Notifications
for Datastore Updates", RFC 8641, DOI 10.17487/RFC8641,
September 2019, .
[RFC8776] Saad, T., Gandhi, R., Liu, X., Beeram, V., and I. Bryskin,
"Common YANG Data Types for Traffic Engineering",
RFC 8776, DOI 10.17487/RFC8776, June 2020,
.
[RFC8795] Liu, X., Bryskin, I., Beeram, V., Saad, T., Shah, H., and
O. Gonzalez de Dios, "YANG Data Model for Traffic
Engineering (TE) Topologies", RFC 8795,
DOI 10.17487/RFC8795, August 2020,
.
[TS.23.501-3GPP]
3rd Generation Partnership Project (3GPP), "System
Architecture for 5G System; Stage 2, 3GPP TS 23.501
v2.0.1", December 2017.
Acknowledgements
The authors would like to thank for following for discussions and
providing input to this document: xxx.
Contributors
Authors' Addresses
Linda Dunbar (editor)
Futurewei
United States of America
Email: ldunbar@futurewei.com
Qiong
China Telecom
China
Email: sunqiong@chinatelecom.cn
Wu Bo (editor)
Huawei
China
Email: lana.wubo@huawei.com
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LUIS MIGUEL CONTRERAS MURILLO (editor)
Telefornica
Spain
Email: luismiguel.contrerasmurillo@telefonica.com
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