Switching Efficiency: A Metric Framework for AI Data Center Networks
draft-ye-ippm-switching-efficiency-02
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| Document | Type | Active Internet-Draft (individual) | |
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| Authors | YNG2020 , Weiqiang Sun , Dong Wang , Jiang Sun | ||
| Last updated | 2026-04-19 | ||
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draft-ye-ippm-switching-efficiency-02
IP Performance Measurement N. Ye
Internet-Draft W. Sun
Intended status: Informational Shanghai Jiao Tong University
Expires: 21 October 2026 D. Wang
J. Sun
China Mobile Research Institute
19 April 2026
Switching Efficiency: A Metric Framework for AI Data Center Networks
draft-ye-ippm-switching-efficiency-02
Abstract
This document specifies the Switching Efficiency Framework, a
measurement methodology designed to evaluate network efficiency in AI
Data Centers (AIDCs). Conventional network metrics, such as
bandwidth utilization or network throughput, fail to directly link
network activity to computational progress, as they cannot
distinguish computationally effective data that directly advances
neural network computing from the redundant traffic induced by both
multi-hop forwarding and the algorithmic overhead of collective
operations.
To address this, this document defines the Switching Efficiency
Framework, a measurement methodology for evaluating AIDC network
efficiency. The core metric, Switching Efficiency, quantifies the
computationally effective data throughput delivered per unit of
provisioned switching capacity. To facilitate precise diagnostic
analysis, the framework further decomposes this core metric into
three fine-grained factors: Data Efficiency, Routing Efficiency, and
Port Utilization.
This framework provides metrics that can help operators identify
communication bottlenecks and evaluate topology-traffic alignment.
About This Document
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Conventions and Definitions . . . . . . . . . . . . . . . . . 4
3. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 4
4. The Switching Efficiency Framework . . . . . . . . . . . . . 5
4.1. Core Variables . . . . . . . . . . . . . . . . . . . . . 5
4.2. Scope and Accounting Rules . . . . . . . . . . . . . . . 6
4.3. Core Metric: Switching Efficiency (eta) . . . . . . . . . 7
4.4. Fine-Grained Efficiency Factors . . . . . . . . . . . . . 7
4.4.1. Data Efficiency (gamma) . . . . . . . . . . . . . . . 7
4.4.2. Routing Efficiency (delta) . . . . . . . . . . . . . 8
4.4.3. Port Utilization (theta) . . . . . . . . . . . . . . 8
5. Measurement Methodology . . . . . . . . . . . . . . . . . . . 9
5.1. Reporting Requirements . . . . . . . . . . . . . . . . . 10
5.2. Uncertainty and Bias . . . . . . . . . . . . . . . . . . 11
6. Security Considerations . . . . . . . . . . . . . . . . . . . 11
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7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 11
8. References . . . . . . . . . . . . . . . . . . . . . . . . . 11
8.1. Normative References . . . . . . . . . . . . . . . . . . 12
8.2. Informative References . . . . . . . . . . . . . . . . . 12
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . 12
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 12
1. Introduction
In hyperscale AI Data Centers (AIDCs), network communication is often
a performance bottleneck for training Large Language Models (LLMs).
While diverse network topologies and communication algorithms (e.g.,
In-Network Computing) are being deployed, operators lack a common
quantitative methodology to evaluate how effectively raw physical
switching resources are converted into actual training progress.
Conventional performance metrics, such as bandwidth utilization or
network throughput, are inadequate for this environment because they
measure overall network activity rather than useful work.
Specifically, they treat all transferred bytes equally, failing to
isolate "computationally effective data" - the net data that directly
advances neural network computing. For example, during an All-Reduce
operation, large volumes of data are transferred across the fabric
only to be discarded after mathematical reduction (algorithmic
overhead). Similarly, when the physical topology fails to match the
spatial distribution of the workload - such as forcing logically
localized, high-volume traffic to cross the broader scale-out fabric
- data must traverse an excessive number of forwarding hops (multi-
hop overhead). Because traditional metrics conflate these
redundancies with effective data delivery, operators cannot
accurately quantify how well a specific network architecture aligns
with its intended AI traffic patterns.
To bridge this gap, this document defines the Switching Efficiency
Framework [SwitchingEfficiencyPaper], which relates the throughput of
effective data to the aggregate switching capacity of the network
through its core metric, Switching Efficiency (eta). This top-level
metric is further decomposed into three diagnostic factors: Data
Efficiency (gamma) evaluates the communication algorithm by
indicating whether it delivers computationally effective data or
generates redundant bytes; Routing Efficiency (delta) evaluates
topology-traffic alignment by indicating whether the physical network
provides direct paths or forces traffic into excessive multi-hop
detours; and Port Utilization (theta) evaluates hardware resource
allocation by indicating whether the provisioned switching capacity
is actively utilized.
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By defining these metrics, this document provides operators and
telemetry systems with a common basis for evaluating AIDC network
performance and diagnosing communication bottlenecks.
2. Conventions and Definitions
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. Terminology
* *Computationally Effective Data (CED):* The aggregate application-
payload volume yielded by a communication primitive and retained
by one or more endpoints for subsequent neural network
computation. CED excludes transport, network, and link-layer
headers; padding; control traffic; unreduced intermediate data;
and any bytes that are delivered but not retained as semantic
input to the next computation phase.
- For non-reduction operations (e.g., All-Gather or All-to-All
dispatch), CED equals the aggregate newly received application-
payload volume retained at the endpoints.
- For reduction operations (e.g., All-Reduce, Reduce-Scatter, or
All-to-All combine), CED equals only the final reduced output
volume retained at the endpoints.
* *Switching Capacity:* The aggregate theoretical egress data
forwarding rate of all packet-switching ports within the evaluated
measurement domain. To reflect the heterogeneous hardware of
modern AI Data Centers, this capacity includes all functional
transit components within that domain, specifically:
1. Standalone network switches (e.g., standard Ethernet or
InfiniBand switches acting as Top-of-Rack, Leaf, or Spine).
2. Embedded switching elements within a single compute chassis
(e.g., NVSwitch interconnecting GPUs within a server).
3. Forwarding ports residing natively on the compute accelerators
(e.g., Google TPUs).
* *In-Network Computing (INC):* A network architecture paradigm
where mathematical or logical operations (such as data reduction
in collective communications) are executed within the network data
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plane (e.g., by programmable switches) while data is in transit.
In the context of AI Data Centers, INC is commonly deployed to
offload collective communication reductions (e.g., performing
arithmetic operations for All-Reduce directly on the switch),
thereby eliminating the transmission of unreduced data and
delivering only the reduced results to the endpoints.
* *Observation Window (T):* The common half-open time interval [t0,
t1) over which all variables in this document are accumulated.
The duration T equals t1 minus t0. All variables used to compute
a reported metric instance MUST use the same observation window.
* *Measurement Domain:* The explicitly identified set of compute
endpoints, forwarding elements, and forwarding ports included in a
reported metric instance.
* *Measured Traffic Set:* The subset of packets, messages, or
communication primitives attributed to the workload, job, tenant,
or collective class under evaluation. The same traffic-selection
rule MUST be applied consistently to V_CED, V_RECV, and V_FWD.
* *Byte Counting Rule:* The declared rule that specifies which bytes
are counted and which are not counted when computing V_RECV and
V_FWD. A report MUST state this rule explicitly and MUST apply
the same rule consistently to V_RECV and V_FWD. V_CED is always
counted using only the retained application data because it
represents computation input that remains semantically useful to
the application.
4. The Switching Efficiency Framework
This section defines the Switching Efficiency Framework. The
detailed mathematical derivations supporting this framework are
provided in [SwitchingEfficiencyPaper]. For operational measurement,
all variables and derived metrics are defined relative to a single
measurement domain, a single measured traffic set, a single byte
counting rule, and a single observation window T. Two reported
results are comparable only if these contextual parameters are the
same, or if any differences are explicitly disclosed and normalized.
4.1. Core Variables
The framework relies on four primary operational metrics collected
over the measurement window T:
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* *V_CED (Total CED Volume):* The aggregate CED yielded by all
measured communication primitives whose retained outputs are
attributable to the observation window T according to the declared
boundary-handling rule.
* *V_RECV (Total Received Volume):* The aggregate byte volume of the
measured traffic set successfully accepted at the ingress of all
measured compute endpoints during T. Each successful receipt
counts once per endpoint receipt. If a payload is received
multiple times because of retransmission or duplication, each
actual receipt is included in V_RECV.
* *V_FWD (Total Forwarded Volume):* The aggregate byte volume of the
measured traffic set emitted on the egress side of all measured
forwarding ports during T. Each forwarding event counts once per
egress transmission. Therefore, replicated copies, multicast fan-
out, load-balancing replicas, retransmissions, and forwarding
loops each increase V_FWD according to the number of observed
egress transmissions.
* *C_TOTAL (Aggregate Switching Capacity):* The aggregate
theoretical egress data forwarding rate of all packet-switching
ports within the measurement domain. C_TOTAL equals the sum of
the theoretical maximum unidirectional egress data rates of those
ports.
4.2. Scope and Accounting Rules
To promote comparable results across implementations and experiments,
the following accounting rules apply:
* All volumes defined in this document MUST be reported in bytes.
All rates MUST be reported in bytes per second.
* A reported metric instance MUST identify its measurement domain,
measured traffic set, byte counting rule, observation window, and
boundary-handling rule for communication primitives that overlap
the edges of T.
* Only traffic attributable to the measured traffic set MUST be
included in V_CED, V_RECV, and V_FWD. Management traffic, storage
traffic, unrelated tenant traffic, and background traffic outside
the measured traffic set MUST be excluded unless the report
explicitly declares that a mixed-traffic domain is being measured.
* The same traffic-selection rule MUST be used for V_CED, V_RECV,
and V_FWD.
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* The same byte counting rule MUST be used for V_RECV and V_FWD. A
report MUST state whether additional non-payload bytes, such as
encapsulation or framing overhead, are included. For maximum
comparability, experiments that are compared against each other
SHOULD use the same byte counting rule across all runs.
* For direct comparison, an implementation SHOULD use operation-
aligned observation windows so that measured communication
primitives are wholly contained within T. If communication
primitives overlap T, the report MUST state whether overlapping
primitives are excluded or attributed by a declared attribution
point. The attribution point is the completion time of the
communication primitive for V_CED, the endpoint receipt time for
V_RECV, and the egress transmission time for V_FWD.
4.3. Core Metric: Switching Efficiency (eta)
Switching Efficiency (eta) is the top-level metric quantifying how
effectively a network translates its raw physical capacity into
computational progress. It is defined as the ratio of the CED
throughput over observation window T to the aggregate switching
capacity of the network.
V_CED / T
eta = -----------
C_TOTAL
A high eta indicates that a large proportion of the network's
provisioned hardware capacity is successfully contributing to the
delivery of computationally effective data.
4.4. Fine-Grained Efficiency Factors
To enable diagnostic analysis and isolate specific performance
bottlenecks, eta is mathematically decomposed into three diagnostic
efficiency factors (eta = gamma * delta * theta):
4.4.1. Data Efficiency (gamma)
Data Efficiency evaluates the effectiveness of implementing the
communication primitives. It specifies the ratio of Computationally
Effective Data (V_CED) to the total received volume (V_RECV).
V_CED
gamma = --------
V_RECV
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* *Diagnostic Focus:* Identifies redundant data delivered to
endpoints. A value of gamma less than 1 indicates that endpoint
ingress traffic contains bytes that do not survive as retained
computation input, such as unreduced data, duplicated deliveries,
or additional overhead included by the declared byte counting
rule. Executing mathematical reductions within the network data
plane via INC can improve gamma by reducing non-retained traffic
delivered to the endpoints.
4.4.2. Routing Efficiency (delta)
Routing Efficiency quantifies the topological alignment between the
physical network architecture and the AI workload traffic patterns.
V_RECV
delta = -------
V_FWD
* *Diagnostic Focus:* Identifies forwarding overhead. In a lossless
network with no duplicated in-network copies, delta equals the
inverse of the volume-weighted average number of forwarding events
incurred per received byte. A value of delta less than 1
indicates that traffic either traverses multiple forwarding stages
or experiences extra forwarding caused by retransmission,
replication, or looping behavior.
4.4.3. Port Utilization (theta)
Port Utilization measures the spatial and temporal engagement of the
provisioned switching capacity.
V_FWD
theta = -------------
C_TOTAL * T
* *Diagnostic Focus:* Identifies underutilized switching capacity.
A low theta indicates that the provisioned hardware (C_TOTAL)
operates below its theoretical maximum data rate over the
observation window T, due to either spatial traffic imbalance or
temporal idleness.
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5. Measurement Methodology
This section specifies the operational procedures for collecting the
variables required to compute the efficiency metrics. Accurate
measurement requires tight time synchronization (e.g., via the
Precision Time Protocol (PTP) [IEEE1588]) across all network and
compute endpoints, as well as an observation window T sufficiently
large to dilute telemetry polling variance. A report claiming
compliance with this specification MUST record the measurement
domain, the measured traffic set, the byte counting rule, the
observation window, the boundary-handling rule, and the estimated
synchronization accuracy of the participating measurement points.
The four core variables span the network, endpoint, and application
planes, and are collected as follows:
* *C_TOTAL (Aggregate Switching Capacity):* Derived from the
topology inventory. It is computed by summing the theoretical
maximum unidirectional egress data rates of all packet-switching
ports within the declared measurement domain.
* *V_FWD (Total Forwarded Volume):* Collected from the network
plane. Operators MUST extract aggregate egress byte counters from
the measured forwarding ports, typically from switch ASIC counters
or equivalent forwarding-plane telemetry. Only traffic matching
the declared measured traffic set is included. If the same
payload is transmitted multiple times on different egress ports,
or retransmitted on the same port, each egress transmission counts
separately in V_FWD. If communication primitives overlap T, the
implementation MUST apply the declared attribution point for V_FWD
consistently. Counter wrap, reset, discontinuity, or sampling
loss MUST be corrected if possible; otherwise, the affected
observation window MUST be reported as invalid or qualified
accordingly.
* *V_RECV (Total Received Volume):* Collected from the endpoint
plane. Operators MUST extract aggregate ingress byte counters
from the host interfaces or accelerators attached to the measured
endpoints. Only traffic matching the declared measured traffic
set is included. V_RECV counts successful endpoint receipts;
bytes dropped before endpoint ingress are excluded. Duplicate
deliveries and retransmissions that are actually received at the
endpoint each contribute to V_RECV. If communication primitives
overlap T, the implementation MUST apply the declared attribution
point for V_RECV consistently.
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* *V_CED (Total CED Volume):* Collected from the application plane.
The implementation MUST count only the retained semantic outputs
of measured communication primitives. To avoid the high overhead
of parsing verbose logs, operators SHOULD utilize lightweight
collection mechanisms such as host-side telemetry agents, eBPF
hooks dynamically attached to collective communication APIs, or
native metrics endpoints exposed by standard communication
libraries (e.g., MPI or vendor-specific equivalents such as NCCL/
RCCL). If communication primitives overlap T, the implementation
MUST apply the declared attribution point for V_CED consistently,
especially when a primitive starts before T or completes after T.
5.1. Reporting Requirements
A comparable measurement report produced using this framework MUST
include at least the following items:
* The start time t0, end time t1, and duration T of the observation
window.
* A description of the measurement domain, including the set of
measured endpoints and forwarding elements.
* A description of the measured traffic set, including any job
identifiers, tenant filters, flow selectors, or collective-
operation selectors used to isolate the traffic.
* The byte counting rule used for V_RECV and V_FWD, including
whether additional non-payload bytes are included.
* The boundary-handling rule used when communication primitives
overlap the boundaries of T, including whether overlapping
primitives are excluded or attributed by completion time, receipt
time, and egress transmission time, respectively.
* The time-synchronization method and the maximum estimated clock
error across measurement points.
* The polling or export interval for counters, together with the
treatment of counter reset, wrap, or missing samples.
* The set of ports included in C_TOTAL and the theoretical maximum
unidirectional egress data rate used for each port or port class.
* The final reported values of V_CED, V_RECV, V_FWD, C_TOTAL, eta,
gamma, delta, and theta.
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5.2. Uncertainty and Bias
The following effects can materially change the measured values and
therefore MUST be disclosed whenever they are present:
* Imperfect isolation of the measured traffic set from unrelated
background traffic.
* Incomplete visibility into replicated traffic, dropped packets, or
endpoint duplicates.
* Clock error that is large relative to the duration of the
communication primitives being measured.
* Counter sampling intervals that are too coarse relative to burst
duration, or counter discontinuities caused by reset, wrap, or
telemetry loss.
* Application-level instrumentation that cannot unambiguously
determine whether partially completed primitives contribute to
V_CED.
Two reported results MUST NOT be treated as directly comparable
unless the reporting items above are either the same or are
normalized to an equivalent basis by the experimenter.
6. Security Considerations
The operational deployment of this measurement framework raises the
following security and privacy considerations:
* *Data Confidentiality:* Collecting V_CED and V_RECV can
inadvertently expose proprietary AI workload characteristics
(e.g., model architecture or training strategies). Telemetry data
MUST be transported over encrypted channels, such as Transport
Layer Security (TLS) [RFC8446] or Internet Protocol Security
(IPsec) [RFC4301], and securely stored.
* *Measurement Integrity:* Falsifying the underlying counters
(V_FWD, V_RECV, V_CED) will manipulate the calculated efficiency
metrics. Authentication and authorization MUST be enforced for
all telemetry endpoints to prevent data poisoning.
7. IANA Considerations
This document has no IANA actions.
8. References
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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/rfc/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/rfc/rfc8174>.
8.2. Informative References
[IEEE1588] "IEEE Standard for a Precision Clock Synchronization
Protocol for Networked Measurement and Control Systems",
IEEE Std 1588-2019, November 2019.
[RFC4301] Kent, S. and K. Seo, "Security Architecture for the
Internet Protocol", RFC 4301, DOI 10.17487/RFC4301,
December 2005, <https://www.rfc-editor.org/rfc/rfc4301>.
[RFC8446] Rescorla, E., "The Transport Layer Security (TLS) Protocol
Version 1.3", RFC 8446, DOI 10.17487/RFC8446, August 2018,
<https://www.rfc-editor.org/rfc/rfc8446>.
[SwitchingEfficiencyPaper]
Ye, N., Zhu, J., Chen, B., Wang, D., Sun, J., Sun, W., and
W. Hu, "Switching Efficiency: A Novel Framework for
Dissecting AI Data Center Network Efficiency",
arXiv 2604.14690, DOI 10.48550/arXiv.2604.14690, April
2026, <https://doi.org/10.48550/arXiv.2604.14690>.
Acknowledgments
We are grateful for the valuable discussions and input from the
community. We also acknowledge support from NSFC.
Authors' Addresses
Niangen Ye
Shanghai Jiao Tong University
China
Email: yng2020@sjtu.edu.cn
Weiqiang Sun
Shanghai Jiao Tong University
China
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Email: sunwq@sjtu.edu.cn
Dong Wang
China Mobile Research Institute
Department of Fundamental Network Technology
Beijing
China
Email: wangdongyjy@chinamobile.com
Jiang Sun
China Mobile Research Institute
Department of Fundamental Network Technology
Beijing
China
Email: sunjiang@chinamobile.com
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