Fast Notification Problem Statement

Modern network applications, ranging from AI training to large-scale cloud services, require lossless and adaptive networks to ensure reliable, congestion-free data transfer within a single data center or across multiple sites. These workloads demand high throughput, low latency, and minimal packet loss across dynamically shifting traffic patterns. To meet these requirements, networks employ mechanisms such as traffic engineering (TE), load balancing, flow control, and protection. However, existing solutions often face limitations in responsiveness, coverage, and operational complexity, particularly in high-speed, large-scale environments. This document summarizes the limitations of existing mechanisms that prevent rapid notification and action to critical network events, including link or node failures and congestion. This document describes why the IETF may need a new set of fast notification related solutions to support these use cases. provides a gap analysis of existing solutions and where they are deficient in supporting high demand services. This document primarily focuses on describing the problem space.

The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in RFC 2119.

FaNTEL: Fast Notification for Traffic Engineering and Load Balancing FRR: Fast Re-Route ECN: Explicit Congestion Notification BFD: Bidirectional Forwarding Detection IOAM: In-situ Operations, Administration, and Maintenance

Current network traffic manipulation mechanisms such as TE, load balancing, flow control, and protection, has deficiencies in providing the low-latency, high-granularity responsiveness needed in modern, dynamic networks, at least in part due to the lack of dynamic network state information. This results in suboptimal performance, low reliability and delayed recovery. FaNTEL is proposed as a set of solutions to address this by enabling fast, real-time, lightweight notifications that enhance the responsiveness for traffic engineering, congestion mitigation, and rapid failure protection. There is a demonstrable need for a standardized framework in IETF to define these fast notification mechanisms, requirements and integration strategies. The following describes a summary of limitations of existing notification solutions: Slow Reaction: Existing control protocols (e.g., routing protocol, etc.) may be used for dissemination of dynamic network state information, while they usually rely on control plane based hop-by-hop distribution, which causes delay when the recipient is multiple hops away. With modern high-throughput environments (AI/ML clusters, multi-DC WANs), this delay is often prohibitive. Explicit Congestion Notification (ECN) needs congestion signals to be sent back to the sender, which can be slow if the source node is far away, and it relies on the source node to react in the transport layer. What is needed is a lightweight signaling method that can provide real-time alerts (e.g., at the level of sub-10 ms) on failures, congestion, or threshold breaches, enabling immediate actions (e.g., in ms to 10s ms ranges) in the network layer. Coarse-Grained Signals: ECN and similar mechanisms only provide binary or threshold-based feedback, without granularity for rapid, fine-tuned adjustments. This leads to either overreaction or underutilization of available capacity. What would be useful is a set of notifications that aren't just "on-off" state reports but can also convey information like congestion level/utilization information, latency spikes, queue buildup or flow characteristics, so that it can trigger immediate and precise responses like rerouting, rate adjustment, or protection switching for specific flows. Overhead and Churn: IOAM and similar tools provide detailed telemetry information, but the collection and feedback loops are controller-centric. They cannot be used to deliver lightweight, real-time alerts for immediate action on specific network nodes. And carrying dynamic network state information in control protocols (e.g. routing protocols) also increases the overhead and churn of the control plane, which may have negative impact to the core functionality of the protocol. It would be useful to have solutions designed to avoid the overhead and churn introduced by telemetry flooding or route distribution, so it can adapt to large-scale networks and dynamic traffic patterns (e.g. AI workloads, cloud WAN bursts). Local-Only Decision Making: Current load-balancing, flow-control and fast reroute (FRR) techniques often act on local information and fail to capture downstream or cross- domain network conditions, limiting their effectiveness. The Point of Local Repair (PLR) makes its decision based on its local view of the topology and network status. It does not know about the state of the entire path of the backup route (e.g., if the backup path itself is congested). It would be helpful to send fast notifications to upstream nodes which can perform the action based on the view of regional or global network conditions. Scalability Challenges: High-volume information or frequent signaling introduces bandwidth and processing overhead. At scale, this becomes a bottleneck rather than a solution.

Consider a large-scale AI/ML training job distributed across multiple data centers. These clusters exchange terabits per second of data between GPU nodes, requiring ultra-low latency and high throughput to maintain synchronization. In such environments, a single fiber link failure or severe congestion event can disrupt the entire training run, leading to: Delays in job completion (hours to days for large models) Massive energy and compute cost waste due to resynchronization Degraded convergence accuracy if synchronization windows are missed

Today's mechanisms provide partial solutions but are not fast or precise enough for these scenarios: BFD : Provides fast forwarding path failure detection. It can be used for both link and path failure detection, while it cannot be used to detect link or path congestion, nor can it notify the failure or congestion to other nodes in the network. BFD is preconfigured with periodic message exchange, while fast notifications needs to be event-driven. When the transmit interval is set to a small value (e.g., at the level of ms), frequent BFD message exchange may become a burden to some systems. FRR /Route convergence: Without fast notification, the failure detection can take tens of milliseconds, followed by either local repair (FRR) or route convergence. The former lacks of global network situation thus may cause congestion on the backup paths, while the latter may breach strict synchronization deadlines. ECN: Provides binary congestion feedback to the endpoints, which is insufficient for granular congestion spikes on high-speed links, and the action can be slow. Telemetry (e.g., IOAM): Offers detailed information, but relies on collection and RTT-based feedback, which delays action. Receiver/Sender Flow Control: Tied to RTT or packet loss, unsuitable for the bursty nature of AI traffic patterns. In practice, this means that by the time a fiber link failure is detected and recovery mechanisms are invoked, critical GPU synchronization barriers may already be missed, forcing rollbacks or restarts of the training process.

Fast notification mechanisms could improve the response to fiber link failures and congestion in AI/ML clusters: Real-Time Alerts: Nodes adjacent to the failure or congestion could immediately (e.g., at 10 ms level) send lightweight notifications to nodes whose fowarding paths can be affected. Action-Oriented Response: Upon receiving the notification, routing and load balancing mechanisms could instantly shift traffic to backup paths or alternative DC interconnects. Granularity: Notifications could carry more detailed information than "link failure/congestion," e.g., indicating specific link utilization, queue buildup or microburst congestion, allowing differentiated responses to different traffic flows. Complementary: The fast notification solutions are complementary to BFD or IOAM, it would bridge the time gap between event onset and slower control plane or telemetry-driven responses, and enable network-wide optimization. By deploying fast notifications, large AI/ML workloads can maintain synchronization across data centers even during transient failures or congestion, protecting job completion time and resource utilization.

Existing Approach: BFD detects failure after tens of ms FRR causes congestion on backup paths Reroute/convergence delays impact GPU sync Result: Training stalls, job wastes compute Fast Notifications Approach: BFD detects failure after tens of ms Fast notification alerts upstream nodes of failure or congestion in real time Regional or global TE steers traffic quickly to link without causing new congestion Result: Training continues with minimal disruption

The information carried in the fast notifications, by the originating node, can be one or multiple of the following: Failure information: This can include the location of failure, and the type of failure. Fine-grained Congestion information: This can include link utilization, queue length, or the level of congestion, together with the location where the congestion happens. Fine-grained Performance information: This can include link or node delay, jitter, packet loss information etc., together with the location where the performance degradation happens. Path identification information: This can be used to indicate the path along which one service flow is being forwarded. Flow identification information: This can include either the identification or the 5-tuple of a flow. Other information related to the network status and need to be timely actioned may also be carried in the fast notifications. Thus there is a need to work on the information model of Fast Notifications to better understand what needs to be carried in the notifications.

Fast notifications may be consumed by two broad forms of recipients: (1) recipient nodes that participate directly in forwarding or signaling, and (2) functions and applications that consume notifications in order to optimize, monitor, or adapt behaviors. Separating these categories clarifies which entities are physical/ logical nodes versus which are higher-level functional consumers.

As illustrated above, the latency sensitivity of recipients decreases as one moves from the data plane to the application plane. Recipient nodes (e.g., adjacent forwarding elements, ingress routers,etc.) often require near-instantaneous notification, while functions and applications (e.g., routing protocols, analytics, NMS, etc.) may tolerate slightly longer timescales but still benefit from rapid awareness compared to existing mechanisms. The range of recipients of the notification depends on the type of recipients, it also depends on what type of action is required. The mechanism to determine the type and range of the recipients is something needs further consideration.

Depending on the position and number of the recipient nodes, fast notifications may be sent via one of the following delivery modes: Unicast directly to the recipient node Multicast to a group of recipient nodes Hop-by-hop to a series of receipt nodes along a specified path Flooding in a specified range of the network Additionally, recipient nodes or functions may subscribe to specific types of notifications based on their roles or interests. A subscription-based approach enables selective delivery, reduces unnecessary signaling overhead, and ensures that each recipient receives only the information relevant to its function. Mechanisms supporting both delivery and subscription must guarantee timely, reliable, and secure propagation of notifications. Examples: Adjacent routers subscribing to all local failure notifications Centralized controllers subscribing only to congestion alerts exceeding defined thresholds Applications or analytics systems subscribing to performance degradation events affecting specific flows or services The mechanisms to support the above delivery mode needs to make sure the notification is always sent to the targeted recipient noded in a timely manner. It could be based on existing messaging and transport mechanisms, or a new protocol may be introduced.

Current network mechanisms were not designed for the responsiveness and scale required by todays' dynamic environments. Techniques such as load balancing, protection switching, and flow control rely on telemetry and feedback loops that are often too slow, too coarse, or too resource-intensive. This results in performance bottlenecks, delayed recovery, and inefficiencies in large-scale AI, cloud, and WAN deployments. A fast notification mechanism could help to address these gaps by providing lightweight, real-time, actionable alerts that complement existing tools and enable faster, more accurate network management decisions.

This document has no IANA actions.

Fast notifications, if not properly authenticated and rate-limited, could be exploited as a vector for Denial-of-Service (DoS) attacks. An attacker able to inject or flood spurious notifications may trigger unnecessary re-convergence, path changes or repeated state updates, overwhelming both recipient nodes and higher-level applications. Implementations must therefore ensure integrity protection, origin authentication, and appropriate rate controls on notification messages.

The authors would like to thank XXX for the valuable comments and discussion.

The following people contributed substantially to the content of this document.