Energy-aware Routing Using Flex-Algo in Segment Routing
draft-ralc-lsr-energy-aware-routing-00
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| Authors | Raul Arco , Luis M. Contreras | ||
| Last updated | 2025-07-07 | ||
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draft-ralc-lsr-energy-aware-routing-00
spring R. Arco
Internet-Draft Nokia
Intended status: Informational L. Contreras
Expires: 8 January 2026 Telefonica
7 July 2025
Energy-aware Routing Using Flex-Algo in Segment Routing
draft-ralc-lsr-energy-aware-routing-00
Abstract
This document proposes enhancements to the Segment Routing (SR)
Flexible Algorithm (Flex-Algo) framework by integrating power
consumption metrics into routing decisions. It introduces metrics
encompassing both node-level and link-level energy consumption
attributes and proposes dynamic energy-aware path computation. By
incorporating these metrics alongside traditional routing parameters,
the enhanced Flex-Algo framework can enable networks to optimize
routing paths for energy efficiency, and then leverage on advances
router capabilities to further reduce operational costs as well as
supporting sustainability objectives.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
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This Internet-Draft will expire on 8 January 2026.
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/
license-info) in effect on the date of publication of this document.
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Please review these documents carefully, as they describe your rights
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1. Related work . . . . . . . . . . . . . . . . . . . . . . 3
1.2. Terminology . . . . . . . . . . . . . . . . . . . . . . . 4
2. Proposed Modifications for the reporting of metrics to be used
by Flex-Algo . . . . . . . . . . . . . . . . . . . . . . 5
2.1. Additional information to be reported . . . . . . . . . . 6
2.1.1. Adjustment factors . . . . . . . . . . . . . . . . . 6
2.1.2. Configurable default value . . . . . . . . . . . . . 7
2.1.3. Power modes . . . . . . . . . . . . . . . . . . . . . 7
3. Proposed TLV formats . . . . . . . . . . . . . . . . . . . . 7
3.1. Value field for reporting measured metrics . . . . . . . 8
3.2. Value field for reporting average metrics . . . . . . . . 9
4. Integration into Path Computation . . . . . . . . . . . . . . 10
5. Open points for further discussion . . . . . . . . . . . . . 10
6. Security Considerations . . . . . . . . . . . . . . . . . . . 11
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 11
8. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 11
9. References . . . . . . . . . . . . . . . . . . . . . . . . . 11
9.1. Normative References . . . . . . . . . . . . . . . . . . 11
9.2. Informative References . . . . . . . . . . . . . . . . . 12
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 13
1. Introduction
The increasing significance of energy efficiency in networks, driven
by both environmental sustainability goals and the need to reduce
operational costs, highlights the necessity to develop routing
mechanisms that enable path selection based on energy consumption.
This includes the ability to define routing policies that leverage
emerging capabilities in routers, such as power state management,to
progressively reduce energy usage over time. But at the same time is
also important to support legacy devices on the overall management of
the network in which refers to energy-aware routing.
This document proposes enhancements to Flexible Algorithm (Flex-Algo)
and its applicability to Segment Routing (SR) [RFC9350] by
integrating power consumption metrics into routing decisions
[I-D.ietf-idr-sr-policy-metric][RFC9256], to be populated by IGP
protocols [RFC8491][RFC8665][RFC8667].
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The motivation of this document is to address the growing importance
of energy efficiency in networks, driven by environmental concerns
and operational cost savings. The inherent flexibility and
scalability of Segment Routing make it suitable for implementing
energy-aware routing by leveraging energy consumption metrics to
guide path selection. This document details extensions to previous
works for energy data collection, path computation policies, and path
issuance, aiming to reduce network carbon footprint and support
sustainable network growth amid increasing traffic demands. The
approach supports both MPLS SR and SRv6 data planes and includes
mechanisms to avoid traffic oscillation by setting thresholds for
path switching.
1.1. Related work
The relevance of energy-aware routing in general, but also in
relation with Segment Routing, is reflected on some existing
documents.
[I-D.li-lsr-flex-algo-energy-efficiency] proposes extensions to IGP
protocols, specifically IS-IS and OSPF, to advertise energy
consumption information within a network. It defines new TLVs and
sub-TLVs for conveying various energy consumption metrics such as
node maximum energy, real-time energy, unit energy consumption, and
interface energy consumption values. These metrics enable energy-
aware routing by allowing network controllers to compute paths that
optimize for energy efficiency, balancing performance with
operational costs. The document also introduces Flex-Algorithm
constraints to exclude links or nodes from routing computations if
their energy consumption exceeds specified thresholds, thereby
facilitating the selection of low-power paths. These extensions aim
to improve network sustainability by integrating energy consumption
into routing decisions.
[I-D.liu-spring-sr-policy-energy-efficiency] proposes a method for
computing energy consumption paths in Segment Routing (SR) networks
to optimize traffic routing for improved energy efficiency. It
outlines a framework where a network controller centrally collects
energy consumption data from nodes and links within an SR domain
using IGP and BGP-LS extensions or direct reporting mechanisms like
NETCONF. Based on such information, the controller computes energy-
efficient paths considering metrics such as maximum and real-time
energy consumption at node and interface levels, and distributes
optimized SR policies to head-end devices for forwarding. The
approach includes mechanisms to avoid traffic oscillation by setting
thresholds for path switching.
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Furthermore, some other docuemnts address aspects such as energy-
related metrics or data models.
Regarding metrics, [I-D.cx-green-green-metrics] define metrics
focusing on attributes like power consumption, energy efficiency, and
carbon footprint. It categorizes green metrics at different levels,
such as equipment, flow, path, and network-wide. The metrics can be
used for network optimization and benchmarking.
In terms of models, [I-D.cwbgp-green-common-energy-management]
defines a common YANG data model module intended for reuse across
various energy efficiency-related network management modules. It
introduces identities, types, and groupings that represent energy-
saving modes, power states, and energy consumption parameters. The
module supports standardized monitoring and control of power and
energy consumption at device and component levels. On the other
hand, [I-D.cwbgp-green-common-energy-management] specifies a YANG
network topology data model for energy efficiency management,
augmenting existing network topology models to include energy
consumption and energy-saving information at both the device and
component levels. Finally,
[I-D.cwbgp-green-inventory-energy-management] defines a YANG network
inventory data model for energy efficiency management that captures
static capability information related to energy saving modes and
methods in network elements and components. It extends the network
inventory base model to include energy management capabilities and
power parameters.
Importantly, [I-D.belmq-green-framework] proposes a structured
approach for energy efficiency management in network devices and
their components. The framework describes physical power topologies,
relationships among devices and components, and the semantics of
power states. It can be expected that such framework could allow the
energy-aware routing described by this document.
(Note. Additional documents will be considered in this section, as
previous work is flourishing).
1.2. Terminology
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
[RFC2119].
In addition, this document uses the terms defined in
[I-D.bclp-green-terminology].
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2. Proposed Modifications for the reporting of metrics to be used by
Flex-Algo
To enable energy-aware routing using Segment Routing Flex-Algo it is
necessary to consider metrics reporting real-time data about energy
consumption at the node and link level, allowing routing decisions to
optimize paths based on energy efficiency and/ or overall energy
consumption.
[I-D.li-lsr-flex-algo-energy-efficiency] and
[I-D.cx-green-green-metrics] propose different set of metrics.
[I-D.li-lsr-flex-algo-energy-efficiency] defines the following energy
consumption metrics:
* Node Maximum Energy Consumption: The power consumption of a node
at full load, measured in watts.
* Node Real-Time Energy Consumption: The real-time power consumption
of a node, measured in watts.
* Node Maximum Unit Energy Consumption: The power consumption of a
node at full load divided by traffic, measured in watts per
gigabyte (W/GB).
* Node Real-Time Unit Energy Consumption: The real-time power
consumption of a node divided by real-time traffic, measured in
watts per gigabyte (W/GB).
* Node Average Unit Energy Consumption: The change in power
consumption of a node over a measurement period divided by the
change in traffic, measured in watts per gigabyte (W/GB).
* Interface Maximum Unit Energy Consumption: The power consumption
of an interface at full load divided by traffic, measured in watts
per gigabyte (W/GB).
* Interface Real-Time Unit Energy Consumption: The real-time power
consumption of an interface divided by real-time traffic, measured
in watts per gigabyte (W/GB).
* Interface Average Unit Energy Consumption: The change in power
consumption of an interface over a measurement period divided by
the change in traffic, measured in watts per gigabyte (W/GB).
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From the set of metrics defined for node and interface (or port), the
ones reporting maximum values can be considered static and then no
necessary to be reported periodically. It can be expected that such
information could be retrieved from the inventory, for instance by
means of [I-D.cwbgp-green-inventory-energy-management].
However, the real-time and the average values are dynamic and require
periodic advertisement for a timely characterization of the energy
consumed by the network. This document builds on top of such
metrics, proposing new structures for the TLV fields in IGP protocols
so to extend FlexAlgo capabilities to provide energy-aware
topologies.
The following section proposes additional information ot be
considered as part of the reported metrics to build energy-aware
routing topologies with FlexAlgo and to allow path computation based
on that information.
2.1. Additional information to be reported
Additional information for the energy-related metrics is described
next.
2.1.1. Adjustment factors
To ensure flexibility, network providers can apply configurable
weighting factors to adjust power values during path computation.
These values can account for external environmental factors, such as
temperature that may impact energy consumption, or for any other
administrative consideration of interest for the network provider.
Moreover, that factor could reflect aspects such as the ratio of
renewable enrgy sources of applicable to the network element (e.g.,
by extrapolating the ratio of renewable energy feeding the Point of
Presence hosting the network element) at the time of reporting the
metric. This can be useful in situations where nodes operate in
diverse physical conditions.
The following configurable parameters
* Node-Specific Adjustment Factor: a node-level factor allows
operators to increase or decrease the advertised power value for
specific nodes.
* Link-Specific Adjustment Factor: Similarly, a per-link adjustment
factor modifies the reported power values for individual links.
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The adjustment factor is defined in the range of 0 to 1, with the aim
of multiplying the reported metric by such factor. The adjustment
factor can be assumed to be populated time to time by an external
network management entity (e.g., the network controller) based on
policies or external administrative information (that could be the
case of updating the percentage of renewable energy). The
calculation of these adjustment factors will be detailed in next
versions of the document.
2.1.2. Configurable default value
Energy-aware routing must address cases where not all network
elements provide power metrics, as could be teh case of legacy nodes.
These factors help balance energy-aware routing decisions when data
is incomplete or inconsistent.
For network elements that do not advertise energy metrics a default
static value is configured.
* Node-Specific static default value: A node-level static value.
* Link-Specific static default value: Similarly, a per-link static
value is defined.
Nodes reporting static node or per-link values can be taking into
account as a criterion for the path calculation providing the option
to avoid use of paths with static default values, or prefer paths not
containing static default values .
2.1.3. Power modes
The metrics being reported can be collected under different power
state modes, thus being important to provide such context to the
measurement for optimization purposes.
IANA [IANA-power] maintains different registries for enumeration of
power states. Thus indication of the registry used plus the power
state in place is an important information.
3. Proposed TLV formats
Note. This version describes only TLVs for IS-IS. Next versions
will nclude TLVs for OSPF.
The proposed TLV fields follow a generic structure as shown in the
next figure.
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0 1 2 3
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
| Type | Length |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
| Value |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
Figure 1: Generic format for the TLV
The Type octect serves for the purpose of identifying the type of the
TLV for its proper interpretation. The Lenght octect serves for
indicating the length of the entire TLV structure. The Value field
follows the encoding described in the next sections.
3.1. Value field for reporting measured metrics
When reporting an absolute power consumption metric, the Value field
has the following structure:
0 1 2 3
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
|A| AF Value | Rsv |D|P| Reg.| Power Mode |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
| Power Value (entire part) | Power Value (fraction) |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
Figure 2: Value field for absolute power consumption metrics
The meaning of the fields is as follows:
* A (1 bit): this bit declares the presence of the adjustment
factor. The value 0 means that no adjustment factor applies (then
implying that the field AF Value is ignored), while the value 1
means that an adjustment factor must be applied.
* AF VAlue (7 bits): this field reports the value of the adjustment
factor to be applied to the reported metric. It represents a
decimal value between 0 and 100, that multiplies the power metric
(as expressed by the field Power Value).
* Rsv (3 bits): this bits are reserved for future use.
* D (1 bit): this bit indicates if the power metric (as expressed by
the field Power Value) is a default value for backward
compatibility with nodes not capable of reporting power metrics
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directly. A value of 1 means that the reported metric is a
default metric, while the value 0 means that the metric reported
is result of a mesurement by sensors in the network element.
* P (1 bit): when set to 1 this bit means that a power state is
associated to the reported metric.
* Registry (3 bit): this field indicates the IANA registry used
[IANA-power] for interpreting the identifyer of the power state
reported in the field Power Mode.
* Power Mode (8 bits): this field identify the power state which
applies to the reported metric.
* Power Value (32 bits) provide the actual power measurement which
is divided in an entire part (the first 16 bits) and a decimal
part (the last 16 bits) expressed in watts.
3.2. Value field for reporting average metrics
When reporting a relative power consumption metric (e.g., watts per
unit of traffic), the Value field has the following structure:
0 1 2 3
0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
| Time Interval |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
| Power value (entire part) | Power value (fraction) |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
Figure 3: Value field for relative power consumption metrics
The meaning of the fields is as follows:
* TIme Interval (16 bits): this fileds indicates the interval in
which the calculation of the average for the relative power
consumption metric, expressed in seconds.
* Power Value (32 bits) provide the average metric of power per unit
of traffic which is divided in an entire part (the first 16 bits)
and a decimal part (the last 16 bits), being expressed in watts
per transmitted Gb.
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4. Integration into Path Computation
The enhanced power metric framework introduces an energy-aware
approach to path computation by incorporating both node-level and
link-level power attributes. This framework enables more efficient
and sustainable network operations by quantifying energy usage across
all elements contributing to a forwarding path.
At the node level, a Node Score is calculated by aggregating power
efficiency ratings from all hardware components within a network
device (e.g., forwarding engines, line cards, and control plane
modules). At the link level, a Link Score is determined by summing
the power consumption values of each link participating in the
candidate path. These two components are combined by the Path
Selection Algorithm, which computes the total Path Score as the sum
of the Node Score and the Link Scores along the path. This score
represents the cumulative energy cost of routing traffic through that
path.
During path computation, Segment Routing (SR) controllers leverage
this framework to prioritize paths with the lowest cumulative power
scores, effectively favoring energy-efficient routes. The algorithm
may also operate in hybrid mode, where power metrics are considered
alongside traditional routing metrics such as latency, bandwidth, or
reliability, enabling flexible, policy-driven optimization strategies
tailored to the network’s operational goals.
5. Open points for further discussion
A number of open points require further discussion.
* Check whether the adjustment factor and power mode should be
applied to the relative W/Gb measurement.
* If power mode is applied to the relative measurement, determine
whether to indicate if the usage was total or partial during the
measurement period.
* Define how frequently to report the measurement (in the case of
relative metrics, it may make sense to report at the same cadence
as traffic monitoring).
* Analyze how to report the power consumption of line cards.
* Determine how to anticipate the impact of adding a new flow on the
node or link power consumption.
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* Assess how to handle scenarios where computation leads to a change
in power mode.
* Consider defining custom power modes.
6. Security Considerations
To be completed.
7. IANA Considerations
To be provided.
8. Acknowledgements
TBC
9. References
9.1. Normative References
[RFC8491] Tantsura, J., Chunduri, U., Aldrin, S., and L. Ginsberg,
"Signaling Maximum SID Depth (MSD) Using IS-IS", RFC 8491,
DOI 10.17487/RFC8491, November 2018,
<https://www.rfc-editor.org/info/rfc8491>.
[RFC8665] Psenak, P., Ed., Previdi, S., Ed., Filsfils, C., Gredler,
H., Shakir, R., Henderickx, W., and J. Tantsura, "OSPF
Extensions for Segment Routing", RFC 8665,
DOI 10.17487/RFC8665, December 2019,
<https://www.rfc-editor.org/info/rfc8665>.
[RFC8667] Previdi, S., Ed., Ginsberg, L., Ed., Filsfils, C.,
Bashandy, A., Gredler, H., and B. Decraene, "IS-IS
Extensions for Segment Routing", RFC 8667,
DOI 10.17487/RFC8667, December 2019,
<https://www.rfc-editor.org/info/rfc8667>.
[RFC9256] Filsfils, C., Talaulikar, K., Ed., Voyer, D., Bogdanov,
A., and P. Mattes, "Segment Routing Policy Architecture",
RFC 9256, DOI 10.17487/RFC9256, July 2022,
<https://www.rfc-editor.org/info/rfc9256>.
[RFC9350] Psenak, P., Ed., Hegde, S., Filsfils, C., Talaulikar, K.,
and A. Gulko, "IGP Flexible Algorithm", RFC 9350,
DOI 10.17487/RFC9350, February 2023,
<https://www.rfc-editor.org/info/rfc9350>.
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9.2. Informative References
[I-D.bclp-green-terminology]
Liu, P. C., Boucadair, M., Wu, Q., Contreras, L. M., and
M. P. Palmero, "Terminology for Energy Efficiency Network
Management", Work in Progress, Internet-Draft, draft-bclp-
green-terminology-02, 14 June 2025,
<https://datatracker.ietf.org/doc/html/draft-bclp-green-
terminology-02>.
[I-D.belmq-green-framework]
Claise, B., Contreras, L. M., Lindblad, J., Palmero, M.
P., Stephan, E., and Q. Wu, "Framework for Energy
Efficiency Management", Work in Progress, Internet-Draft,
draft-belmq-green-framework-03, 13 June 2025,
<https://datatracker.ietf.org/doc/html/draft-belmq-green-
framework-03>.
[I-D.cwbgp-green-common-energy-management]
Ma, Q., Wu, Q., Stephan, E., de Dios, O. G., Pignataro,
C., and S. Han, "A Common YANG Data Model for Energy
Efficiency Network Management", Work in Progress,
Internet-Draft, draft-cwbgp-green-common-energy-
management-00, 2 March 2025,
<https://datatracker.ietf.org/doc/html/draft-cwbgp-green-
common-energy-management-00>.
[I-D.cwbgp-green-inventory-energy-management]
Chen, G., Wu, Q., Stephan, E., de Dios, O. G., Pignataro,
C., and S. Han, "A Network Inventory Data Model for Energy
Efficiency Management", Work in Progress, Internet-Draft,
draft-cwbgp-green-inventory-energy-management-00, 2 March
2025, <https://datatracker.ietf.org/doc/html/draft-cwbgp-
green-inventory-energy-management-00>.
[I-D.cx-green-green-metrics]
Clemm, A., Pignataro, C., Schooler, E., Ciavaglia, L.,
Rezaki, A., Mirsky, G., and J. Tantsura, "Green Networking
Metrics for Environmentally Sustainable Networking", Work
in Progress, Internet-Draft, draft-cx-green-green-metrics-
00, 21 October 2024,
<https://datatracker.ietf.org/doc/html/draft-cx-green-
green-metrics-00>.
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[I-D.ietf-idr-sr-policy-metric]
KaZhang, Dong, J., and K. Talaulikar, "BGP SR Policy
Extensions for metric", Work in Progress, Internet-Draft,
draft-ietf-idr-sr-policy-metric-02, 29 December 2024,
<https://datatracker.ietf.org/doc/html/draft-ietf-idr-sr-
policy-metric-02>.
[I-D.li-lsr-flex-algo-energy-efficiency]
Li, J. and C. Lin, "Flexible Algorithms for Energy
Efficiency", Work in Progress, Internet-Draft, draft-li-
lsr-flex-algo-energy-efficiency-00, 3 March 2025,
<https://datatracker.ietf.org/doc/html/draft-li-lsr-flex-
algo-energy-efficiency-00>.
[I-D.liu-spring-sr-policy-energy-efficiency]
Liu, Y. and C. Lin, "Computing Energy Consumption Path in
Segment Routing Networks", Work in Progress, Internet-
Draft, draft-liu-spring-sr-policy-energy-efficiency-00, 3
March 2025, <https://datatracker.ietf.org/doc/html/draft-
liu-spring-sr-policy-energy-efficiency-00>.
[IANA-power]
"IANA Power State Sets (https://www.iana.org/assignments/
power-state-sets/power-state-sets.xhtml)", n.d..
[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>.
Authors' Addresses
Raul Arco
Nokia
Email: raul.arco@nokia.com
Luis M. Contreras
Telefonica
Email: luismiguel.contrerasmurillo@telefonica.com
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