Benchmarking Methodology for Computing-aware Traffic Steering
draft-yl-bmwg-cats-05
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| Document | Type | Active Internet-Draft (individual) | |
|---|---|---|---|
| Authors | Kehan Yao , Peng Liu , Guanming Zeng , Xinxin Yi , Quan Xiong , Trần Minh Ngọc | ||
| Last updated | 2026-07-31 | ||
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draft-yl-bmwg-cats-05
bmwg K. Yao
Internet-Draft P. Liu
Intended status: Informational China Mobile
Expires: 1 February 2027 G. Zeng
Huawei
X. Yi
China Unicom
Q. Xiong
ZTE
M.-N. Tran
ETRI
31 July 2026
Benchmarking Methodology for Computing-aware Traffic Steering
draft-yl-bmwg-cats-05
Abstract
Computing-aware traffic steering (CATS) is a traffic engineering
approach for steering service requests towards appropriate service
instances based on the awareness of both computing and network
information. This document proposes benchmarking methodologies for
CATS.
Status of This Memo
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This Internet-Draft will expire on 1 February 2027.
Copyright Notice
Copyright (c) 2026 IETF Trust and the persons identified as the
document authors. All rights reserved.
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Please review these documents carefully, as they describe your rights
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Definition of Terms . . . . . . . . . . . . . . . . . . . . . 3
3. Test Methodology . . . . . . . . . . . . . . . . . . . . . . 3
3.1. Test Setup . . . . . . . . . . . . . . . . . . . . . . . 3
3.1.1. Test Setup - Centralized Model . . . . . . . . . . . 4
3.1.2. Test Setup - Distributed Model . . . . . . . . . . . 6
3.1.3. Test Setup - Hybrid Model . . . . . . . . . . . . . . 7
3.2. Control Plane and Forwarding Plane Support . . . . . . . 10
3.3. Topology . . . . . . . . . . . . . . . . . . . . . . . . 10
3.4. Device Configuration . . . . . . . . . . . . . . . . . . 10
4. Reporting Format . . . . . . . . . . . . . . . . . . . . . . 11
5. Benchmarking Tests . . . . . . . . . . . . . . . . . . . . . 13
5.1. CATS Metrics Collection and Distribution . . . . . . . . 13
5.2. Session continuity . . . . . . . . . . . . . . . . . . . 14
5.3. End-to-end Service Latency . . . . . . . . . . . . . . . 15
5.4. System Utilization . . . . . . . . . . . . . . . . . . . 15
5.5. Load Balancing Variance . . . . . . . . . . . . . . . . . 16
6. Security Considerations . . . . . . . . . . . . . . . . . . . 17
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 17
8. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 17
9. References . . . . . . . . . . . . . . . . . . . . . . . . . 17
9.1. Normative References . . . . . . . . . . . . . . . . . . 17
9.2. Informative References . . . . . . . . . . . . . . . . . 18
Appendix A. Aggregation and Normalization Functions . . . . . . 19
A.1. Time-scale Aggregation . . . . . . . . . . . . . . . . . 19
A.2. Spatial Aggregation . . . . . . . . . . . . . . . . . . . 19
A.3. Cross-category Aggregation . . . . . . . . . . . . . . . 20
A.4. Normalization . . . . . . . . . . . . . . . . . . . . . . 20
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 20
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1. Introduction
Computing-aware traffic Steering(CATS) is a traffic engineering
approach considering both computing and network metrics, in order to
select appropriate service instances. Some of the latency-sensitive,
throughput-sensitive applications or compute-intensive applications
need CATS to guarantee effective instance selection, which are
mentioned in [I-D.ietf-cats-usecases-requirements]. Considering
there are many computing and network metrics that can be selected for
traffic steering, as proposed in [I-D.ietf-cats-metric-definition],
some benchmarking test methods are required to validate the
effectiveness of different CATS metrics. Besides, there are also
different deployment models as described in the CATS framework
[I-D.ietf-cats-framework], i.e. the distributed model, the
centralized model and the hybrid model, and there are also multiple
objectives for instance selection, for example, instance with lowest
end-to-end latency or the highest system utilization. The
benchmarking methodology proposed in this document is essential for
guiding CATS implementation.
2. Definition of Terms
This document uses the following terms defined in
[I-D.ietf-cats-framework]:
* Computing-aware Traffic Steering (CATS)
* CATS Path Selector (C-PS)
* CATS-Forwarder
* CATS Service Metric Agent (C-SMA)
* CATS Network Metric Agent (C-NMA)
3. Test Methodology
3.1. Test Setup
The test setup in general is compliant with [RFC2544]. As is
mentioned in the introduction, there are basically three models for
CATS deployment. The centralized model, the distributed model, and
the hybrid model.
The difference primarily sits in how CATS metrics are collected and
distributed into the network and accordingly, where the CATS path
selector(C-PS) is placed to make decisions, as is defined in
[I-D.ietf-cats-framework].
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3.1.1. Test Setup - Centralized Model
Figure 1 shows the test setup of the centralized model to implement
CATS. The centralized test setup is similar to the Software Defined
Networking(SDN) standalone mode test setup defined in [RFC8456]. The
DUT locates at the same place with the SDN controller. In the
centralized model, SDN controller takes the role of the decision
making for instance selection as well as traffic steering. The
application plane test emulator is connected with the forwarding
plane test emulator via interface 2 (I2). The SDN controller is
connected to Edge server manager via interface 4 (I4). The interface
(I1) of the SDN controller is connected with the forwarding plane.
Service request is sent from application to the ingress CATS-
Forwarder through I2. CATS metrics are collected from Edge server
manager via I4. The traffic steering policies are configured through
I1.
In the forwarding plane, CATS-Forwarder 1 serves as the ingress node
and is connected with the host which is an application plane
emulator. CATS-Forwarder 2 and CATS-Forwarder 3 serve as the egress
nodes and are connected with two edge servers respectively. Both of
the edge servers are connected with edge server manager via I3. I3
is an internal interface for CATS metrics collection within edge
sites.
To accommodate the CATS framework, the C-PS is placed at the SDN
controller. The C-SMA is placed at the edge server manager. C-NMAs
are placed at all CATS-Forwarders.
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+-----------------------------------------------+
| Application-Plane Test Emulator |
| |
| +-----------------+ +-------------+ |
| | Application | | Service | |
| +-----------------+ +-------------+ |
| |
+-+(I2)-----------------------------------------+
|
|
| +-------------------------------+ +-------------+
| | +----------------+ | | |
| | | SDN Controller | | | Edge Server |
| | | (C-PS) | |----| Manager |
| | +----------------+ | I4 | (C-SMA) |
| | Device Under Test (DUT) | | |
| +-------------------------------+ +--------+----+
| | |
| | |
+-+------------+(I1)--------------------------+ |
| | |
| +----------------+ | |
| |CATS-Forwarder 1| | |
| | (C-NMA) | | | I3
| +----------------+ | |
| / \ | |
| / \ | |
| l0 / \ ln | |
| / \ | |
| +----------------+ +----------------+ | |
| |CATS-Forwarder 2| |CATS-Forwarder 3| | |
| | (C-NMA) |..| (C-NMA) | | |
| +----------------+ +----------------+ | |
| | | | |
| +------------+ +------------+ | |
| | Edge | | Edge | | |
| | Server 1 | | Server 2 | | |
| | (ES1) | | (ES2) | | |
| +------------+ +------------+ | |
| | | | |
| +---------------+------------------------+
| Forwarding-Plane Test Emulator |
+---------------------------------------------+
Figure 1: Centralized Test Setup
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3.1.2. Test Setup - Distributed Model
Figure 2 shows the test setup of the distributed model to implement
CATS. In the distributed test setup, The DUT is the group of CATS-
Forwarders, since the decision maker is the CATS ingress node, namely
CATS-Forwarder 1. CATS egress nodes, CATS-Forwarder 2 and CATS-
Forwarder 3, take the role of collecting CATS metrics from edge
servers and distribute these metrics towards other CATS-Forwarders.
Service emulators from application plane is connected with the
control-plane and forwarding-plane test emulator through the
interface 1.
To accommodate the CATS framework, the C-PS is placed at the ingress
CATS-Forwarder 1. The C-NMAs and C-SMAs are placed at all egress
CATS-Forwarders (CATS-Forwarder 2 and 3).
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+---------------------------------------------+
| Application-Plane Test Emulator |
| |
| +-----------------+ +-------------+ |
| | Application | | Service | |
| +-----------------+ +-------------+ |
| |
+---------------+-----------------------------+
|
|
+---------------+(I1)---------------------------+
| |
| +----------------------------------------+ |
| | +----------------+ | |
| | |CATS-Forwarder 1| | |
| | | (C-PS) | | |
| | +----------------+ | |
| | / \ | |
| | / \ | |
| | l0 / \ ln | |
| | / \ | |
| | +----------------+ +----------------+ | |
| | |CATS-Forwarder 2| |CATS-Forwarder 3| | |
| | |(C-NMA & C-SMA) |..|(C-NMA & C-SMA) | | |
| | +----------------+ +----------------+ | |
| | Device Under Test (DUT) | |
| +----------------------------------------+ |
| | | |
| +-------------+ +-------------+ |
| |Edge Server 1| |Edge Server 2| |
| | (ES1) | | (ES2) | |
| +-------------+ +-------------+ |
| Control-Plane and |
| Forwarding-Plane Test Emulator |
+-----------------------------------------------+
Figure 2: Distributed Test Setup
3.1.3. Test Setup - Hybrid Model
Figure 3 shows the test setup of the hybrid model to implement CATS.
In hybrid model, some stable CATS metrics are distributed among
involved network devices (i.e., CATS-Forwarders), while other
frequent changing CATS metrics may be collected by a centralized SDN
controller. At the mean time, Service scheduling function can be
performed by a SDN controller and/or the ingress CATS-Forwarder. The
entire or partial C-PS function may be implemented in the centralized
control plane, depending on the specific implementation and
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deployment.
To accommodate the CATS framework, the C-PSs are placed at the SDN
controller and the ingress CATS-Forwarder 1. The C-SMAs are placed
at the edge server manager and all egress CATS-Forwarders, The C-NMAs
are placed at all egress CATS-Forwarders (CATS-Forwarder 2 and 3)
too.
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+-----------------------------------------------+
| Application-Plane Test Emulator |
| |
| +-----------------+ +-------------+ |
| | Application | | Service | |
| +-----------------+ +-------------+ |
| |
+-+(I2)-----------------------------------------+
|
|
| +-------------------------------+ +-------------+
| | +----------------+ | | |
| | | SDN Controller | | | Edge Server |
| | | (C-PS) | |----| Manager |
| | +----------------+ | I4 | (C-SMA) |
| | Device Under Test (DUT) | | |
| +-------------------------------+ +--------+----+
| | |
| | |
+-+------------+(I1)--------------------------+ |
| | |
| +----------------+ | |
| |CATS-Forwarder 1| | |
| | (C-PS) | | | I3
| +----------------+ | |
| / \ | |
| / \ | |
| l0 / \ ln | |
| / \ | |
| +----------------+ +----------------+ | |
| |CATS-Forwarder 2| |CATS-Forwarder 3| | |
| |(C-NMA & C-SMA) |..|(C-NMA & C-SMA) | | |
| +----------------+ +----------------+ | |
| | | | |
| +------------+ +------------+ | |
| | Edge | | Edge | | |
| | Server 1 | | Server 2 | | |
| | (ES1) | | (ES2) | | |
| +------------+ +------------+ | |
| | | | |
| +---------------+------------------------+
| Forwarding-Plane Test Emulator |
+---------------------------------------------+
Figure 3: Hybrid Test Setup
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3.2. Control Plane and Forwarding Plane Support
In the centralized model, Both of the control plane and forwarding
plane follow Segment Routing pattern, i.e. SRv6[RFC8986]. The SDN
controller configure SRv6 policies based on the awareness of CATS
metrics and traffic is steered through SRv6 tunnels built between
CATS ingress nodes and CATS egress nodes. The collection of CATS
metrics in control plane is through Restful API or similar signalling
protocols built between the SDN controller and the edge server
manager.
In the distributed model, in terms of the control plane,
EBGP[RFC4271] is established between CATS egress nodes and edge
servers. IBGP[RFC4271] is established between CATS egress nodes with
CATS ingress nodes. BGP is chosen to distribute CATS metrics in
network domain, from edge servers to CATS ingress node. Carrying
CATS metrics is implemented through the extension of BGP, and
[I-D.ietf-idr-5g-edge-service-metadata] provides some examples by
leveraging sub-TLVs extensions.
In the hybrid model, the metric distribution follows the control
plane settings in both centralized and distributed model, according
to the actual choices in what metrics are required to be distributed
centrally or distributedly.
In terms of the forwarding plane, SRv6 tunnels are enabled between
CATS ingress nodes with CATS egress nodes.
Service flows are routed towards service instances by following
anycast IP addresses in all of the models.
3.3. Topology
In terms of all of the approaches to test CATS performance in
laboratory environments, implementors consider only single domain
realization, that is all CATS-Forwarders are within the same AS.
There is no further special requirement for specific topologies.
3.4. Device Configuration
Before implementation, there are some pre-configurations need to be
settled.
** Application plane Setup
Application plane functionalities must be setup in edge servers
before the implementation, and hosts that send service requests must
also be setup.
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** CATS Metrics Collector Setup
In the centralized model and the hybrid model, the CATS metrics
collector need to be first setup in the edge server manager. A
typical example of the collector can be the monitoring components of
Kubernetes. It can periodically collect different levels of CATS
metrics. Then the connecton between the edge server manager and the
SDN controller must be established, one example is to set restful API
or ALTO protocol for CATS metrics publication and subscription.
In the distributed model and the hybrid model, the CATS metrics
collector need to be setup in each edge site. In this benchmark
test, the collector is setup in each edge server which is directly
connected with a CATS egress node. Implementors can use plugin
software to collect CATS metrics. Then each edge server must set BGP
peer with the CATS egress node that's directly connected. In each
edge server, a BGP speaker is setup.
** Control Plane and Fordwarding Plane functionality Setup
In the centralized model and the hybrid model, the SDN controller
need to be pre-configured and the interface between the SDN
controller and CATS-Forwarders must be tested to validate if control
plane policies can be correctly downloaded and it metrics from
network side can be correctly uploaded. In the distributed model and
the hybrid model, the control plane setup is the iBGP connections
between CATS-Forwarders. For both models. the forwarding plane
functions, SRv6 tunnels must be pre-established and tested.
4. Reporting Format
CATS benchmarking tests focus on data that can be measured and
controllable.
* Control plane configurations:
- SDN controller types and versions;
- northbound and southbound protocols.
* Forwarding plane configurations:
- forwarding plane protocols (e.g., SRv6);
- the number of CATS-Forwarders;
- the number of edge servers;
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- the number of links;
- edge server types, versions.
* Application plane configurations:
- Traffic types and configurations.
* CATS Metrics: Each test must clearly state what CATS metrics it
use for traffic steering, according to the CATS metrics definition
in [I-D.ietf-cats-metric-definition].
- For Level 0 metrics, benchmarking tests must declare metric
types, units, statistics(e.g, mean, max, min), format, and
metric sources(e.g, nominal, estimation, aggregation).
- For Level 1 metrics, benchmarking tests must declare metric
types, statistics, units, format, aggregation functions, and
metric sources.
- For the Level 2 metric, benchmarking tests must declare metric
type, normalization functions, and metric source.
For all metric levels, benchmarking tests must report the
Measurement_Window parameter used for metric collection.
Tests must report the sequence number and source timestamp carried
in each metric update. If timestamps from different devices are
used for comparison and metric validation, benchmarking tests must
report the synchronization method (e.g., NTP[RFC5905],
PTP[IEEE-1588-2008]) between the involved devices.
*Detailed normalization functions and aggregation functions are
listed in appendix A.*
The recommended metric levels are listed in Figure 4 for the
benchmark tests that are described in Section 5:
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+----------------+--------------------------+---------------------+
| Test Objective | Recommended Metric Level | Example Metric_Type |
+----------------+--------------------------+---------------------+
| CATS Metrics | | compute_CPU_util or |
| Collection and | All Metric Levels | level1_composed or |
| Distribution | | level2_global |
+----------------+--------------------------+---------------------+
| Session | Level 1 or | level1_composed or |
| Continuity | Level 2 | level2_global |
+----------------+--------------------------+---------------------+
| End-to-end | Level 1 or | level1_composed or |
| Service Latency| Level 2 | level2_global |
+----------------+--------------------------+---------------------+
| System | Level 1 | level1_computing |
| Utilization | | |
+----------------+--------------------------+---------------------+
| Load Balancing | Level 1 or | level1_computing or |
| Variance | Level 2 | level2_global |
+----------------+--------------------------+---------------------+
Figure 4: Mapping between Benchmarking Tests and Metric Levels
5. Benchmarking Tests
5.1. CATS Metrics Collection and Distribution
* Objective: To determine that CATS metrics can be correctly
collected and distributed to the DUTs which are the SDN controller
in the centralized model and the CATS ingress node in the
distributed model, as anticipated within a pre-defined time
interval for CATS metrics update.
* Procedure:
In the centralized model and the hybrid model, the edge server
manager periodically grasp CATS metrics from every edge server that
can provide CATS service. Then it passes the information to the SDN
controller through publish-subscription methods. Implementors then
should log into the SDN controller to check if it can receive the
CATS metrics from the edge server manager.
In the distributed model and the hybrid model, the collectors within
each edge server periodically grasp the CATS metrics of the edge
server. Then it distributes the metrics to the CATS egress node it
directly connected. Then Each CATS egress node further distributes
the metrics to the CATS ingress node. Implementors then log into the
CATS ingress node to check if metrics from all edge servers have been
received.
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For all of the above approaches, it is required that the edge servers
from which CATS metrics are collected are time-synchronized. Each
metric update must carry a sequence number and a source timestamp.
The receiving DUT must check that the sequence numbers are strictly
increasing to detect duplication or reordering, thereby validating
the correctness and ordering of the metrics. To test whether metrics
are received within the pre-defined time interval, implementors
should compare the timestamp of the received current metric with the
timestamp of the last arrival from the logs. If the time difference
equals the pre-defined metric update interval, then the CATS metrics
collection is considered correct.
* Expected results:
** All CATS metrics are correctly received, and for metrics from the
same collection source, the sequence numbers are strictly increasing
with no duplication, reordering, or missing updates detected.
** The interval between consecutive metric updates matches the
predefined value.
** If timestamps from multiple time-synchronized edge servers are
compared, the relative ordering of updates from multiple sources is
correctly preserved.
5.2. Session continuity
* Objective: To determine that traffic can be correctly steered to
the selected service instances and TCP sessions are maintained for
specific service flows.
* Procedure: Enable several hosts to send service requests. In
distributed model, log into the CATS ingress node to check the
forwarding table that route entries have been created for service
instances. Implementors can see that a specific packet which hits
the session table, is matched to a target service intance. Then
manually increasing the load of the target edge server. From the
host side, one can see that service is going normally, while in
the interface of the CATS-Forwarder, one can see that the previous
session table aging successfully which means CATS has steer the
service traffic to another service instance.
In the centralized model and the hybrid model, implementors log into
the management interface of the SDN controller and can check routes
and sessions.
* Expected results:
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** Traffic is correctly forwarded to the selected edge server without
blackholing.
** Session entries are dynamically created and properly aged.
** When the target server is overloaded, traffic is smoothly switched
to another instance.
** No session drop or service interruption occurs during steering.
5.3. End-to-end Service Latency
* Objective: To determine that CATS works properly under the pre-
defined test condition and prove its effectiveness in service end-
to-end latency guarantee.
* Procedure: Pre-define the CATS metrics distribution time to be T_1
seconds. Enable a host to send service requests. In distributed
model, log into the CATS ingress node to check if route entries
have been successfully created. Suppose the current selected edge
server is ES1. Then manually increase the load of ES1, and check
the CATS ingress node again. The selected instance has been
changed to ES2. CATS works properly. Then print the logs of the
ingress CATS-Forwarder to check the time it updates the route
entries. The time difference delta_T between when the new route
entry first appears and when the previous route entry last appears
should equals to T_1. Then check if service SLA can be satisfied.
In the centralized model and the hybrid model, implementors log into
the management interface of the SDN controller and can check routes
and sessions.
* Expected results:
** C-PS updates routing entries within delta_T, which is
approximately equal to T1.
** End-to-end latency remains within the predefined SLA threshold.
** Latency after steering is not higher than before steering.
** Service performance is stable during path switching.
5.4. System Utilization
* Objective: To determine that CATS can have better load balancing
effect at server side than simple network load balancing
mechanism, for example, Equal cost multi-path routing (ECMP).
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* Procedure: Enable several hosts to send service requests and
enable ECMP at network side. Then measure the bias of the CPU
utilization among different edge servers in time duration
delta_T_2. Stop services. Then enable the same number of service
requests and enable CATS at network side(the distributed model,
the centralized model, and the hybrid model are tested
separately.). Measure the bias of the CPU utilization among the
same edge servers in time duration delta_T_2. Compare the bias
value from two test setup.
* Expected results:
** CATS reduces CPU utilization bias compared to ECMP, and edge
server resources are used more evenly.
** No server is overloaded while others are underutilized.
** Overall system resource utilization is improved.
5.5. Load Balancing Variance
* Objective: To test the load balancing variance under different
path selection algorithms, which could evaluate the traffic
steering effectiveness of these algorithms. Low variance value
means the algorithm performs better for traffic steering.
Algorithms that are compared include ECMP, global-min, and
Proportional-Integral-Derivative (PID).
* Procedure: Three different path selection algorithm are tested
one-by-one. In distributed model, pre-configure the control plane
function C-PS in the ingress CATS-Forwarder, while in the
centralized and hybrid model, the path selection function is
configured in the SDN controller. For each test round,
implementors initiate the same number of service flows to multiple
service edge sites. For example, the number of service flows are
set to M, while the number of service edge sites is N.
Implementors need to record the number of service flows at each
site, and calculate the load balancing variance, according to the
following equations:
** n_avg = (n_s1 + n_s2 + .. + n_sN) / N **
** var_alg = sqrt( sum_{i=1 to N} (n_si - n_avg)^2 / N ) **
Where 'n_s1', 'n_s2', ..., 'n_sN' refer to the number of service
flows that are steered to the corresponding edge site, while 'n_avg'
refers to the average number of service flows per site. 'var_alg' is
the standard deviation of service flows across all N edge sites,
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which is used to evaluate the load balancing effectiveness of each
algorithm. A lower standard deviation indicates better load
balancing, as traffic is more evenly distributed among edge sites.
* Expected Results:
** CATS algorithms (global-min, PID) show lower variance than ECMP.
** Traffic is distributed evenly without flow loss or service
degradation.
6. Security Considerations
The benchmarking characterization described in this document is
constrained to a controlled environment (as a laboratory) and
includes controlled stimuli. The network under benchmarking MUST NOT
be connected to production networks. Beyond these, there are no
specific security considerations within the scope of this document.
7. IANA Considerations
This document has no IANA actions.
8. Acknowledgements
Authors give thanks to Longlong Zhu for review comments.
9. References
9.1. Normative References
[I-D.ietf-cats-framework]
Li, C., Du, Z., Boucadair, M., Contreras, L. M., and J.
Drake, "A Framework for Computing-Aware Traffic Steering
(CATS)", Work in Progress, Internet-Draft, draft-ietf-
cats-framework-24, 2 April 2026,
<https://datatracker.ietf.org/doc/html/draft-ietf-cats-
framework-24>.
[I-D.ietf-cats-metric-definition]
Yao, K., Li, C., Contreras, L. M., Ros-Giralt, J., and G.
Zeng, "CATS Metrics Definition", Work in Progress,
Internet-Draft, draft-ietf-cats-metric-definition-10, 22
June 2026, <https://datatracker.ietf.org/doc/html/draft-
ietf-cats-metric-definition-10>.
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[IEEE-1588-2008]
IEEE, "IEEE Standard for a Precision Clock Synchronization
Protocol for Networked Measurement and Control Systems",
July 2008.
[RFC2544] Bradner, S. and J. McQuaid, "Benchmarking Methodology for
Network Interconnect Devices", RFC 2544,
DOI 10.17487/RFC2544, March 1999,
<https://www.rfc-editor.org/rfc/rfc2544>.
[RFC4271] Rekhter, Y., Ed., Li, T., Ed., and S. Hares, Ed., "A
Border Gateway Protocol 4 (BGP-4)", RFC 4271,
DOI 10.17487/RFC4271, January 2006,
<https://www.rfc-editor.org/rfc/rfc4271>.
[RFC5905] Mills, D., Martin, J., Ed., Burbank, J., and W. Kasch,
"Network Time Protocol Version 4: Protocol and Algorithms
Specification", RFC 5905, DOI 10.17487/RFC5905, June 2010,
<https://www.rfc-editor.org/rfc/rfc5905>.
[RFC8456] Bhuvaneswaran, V., Basil, A., Tassinari, M., Manral, V.,
and S. Banks, "Benchmarking Methodology for Software-
Defined Networking (SDN) Controller Performance",
RFC 8456, DOI 10.17487/RFC8456, October 2018,
<https://www.rfc-editor.org/rfc/rfc8456>.
[RFC8986] Filsfils, C., Ed., Camarillo, P., Ed., Leddy, J., Voyer,
D., Matsushima, S., and Z. Li, "Segment Routing over IPv6
(SRv6) Network Programming", RFC 8986,
DOI 10.17487/RFC8986, February 2021,
<https://www.rfc-editor.org/rfc/rfc8986>.
9.2. Informative References
[I-D.ietf-cats-usecases-requirements]
Yao, K., Contreras, L. M., Shi, H., Zhang, S., and Q. An,
"Computing-Aware Traffic Steering (CATS) Problem
Statement, Use Cases, and Requirements", Work in Progress,
Internet-Draft, draft-ietf-cats-usecases-requirements-14,
2 February 2026, <https://datatracker.ietf.org/doc/html/
draft-ietf-cats-usecases-requirements-14>.
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[I-D.ietf-idr-5g-edge-service-metadata]
Dunbar, L., Majumdar, K., Li, C., Mishra, G. S., and Z.
Du, "BGP Extension for 5G Edge Service Metadata", Work in
Progress, Internet-Draft, draft-ietf-idr-5g-edge-service-
metadata-33, 29 May 2026,
<https://datatracker.ietf.org/doc/html/draft-ietf-idr-5g-
edge-service-metadata-33>.
Appendix A. Aggregation and Normalization Functions
This appendix specifies the detailed aggregation functions
(categorized by time-scale, spatial, and cross-category) and
normalization functions for CATS metrics, aligned with
[I-D.ietf-cats-metric-definition].
A.1. Time-scale Aggregation
Time-scale aggregation consolidates metric values over a specified
time window to reflect temporal trends (e.g., average computing load
over 1 minute). The following functions are defined for CATS
benchmarking:
** Time Average
Time_Average = (Sample_1 + Sample_2 + ... + Sample_n) / n
** Time Maximum
Time_Max = Maximum value of all samples in the time window
** Time Minimum
Time_Min = Minimum value of all samples in the time window
A.2. Spatial Aggregation
Spatial aggregation consolidates metric values across multiple
compute entities (e.g., multiple edge servers) to reflect regional/
global status.
** Spatial Sum
Spatial_Sum = Entity_1_Value + Entity_2_Value + ... + Entity_m_Value
** Spatial Average
Spatial_Average = (Entity_1_Value + Entity_2_Value + ... +
Entity_m_Value) / m
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** Spatial Median
Spatial_Median = Middle value of sorted entity values
A.3. Cross-category Aggregation
Cross-category aggregation combines metrics from different categories
(e.g., type 1: CPU load; type 2: GPU memory) to generate metrics with
level up for CATS decision-making. This documents adopts linear
combination as the cross-category aggregation function.
** Linear Combination
Linear_Combination = alpha * Metric_1 + beta * Metric_2 + ... (alpha
+ beta + ... = 1)
A.4. Normalization
Normalization scales CATS metrics to a uniform range (0-10) using
min-max scaling, ensuring comparability while ensuring simplicity.
The functions are defined as:
** Standard Normalization
Normalized_Value = 10 * (Raw_Value - Min_Value) / (Max_Value -
Min_Value)
** Inverse Normalization
Inverse_Normalized_Value = 10 * (Max_Value - Raw_Value) / (Max_Value
- Min_Value)
Authors' Addresses
Kehan Yao
China Mobile
Email: yaokehan@chinamobile.com
Peng Liu
China Mobile
Email: liupengyjy@chinamobile.com
Guanming Zeng
Huawei
Email: zengguanming@huawei.com
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Xinxin Yi
China Unicom
Email: yixx3@chinaunicom.cn
Quan Xiong
ZTE
Email: xiong.quan@zte.com.cn
Minh-Ngoc Tran
ETRI
Email: mipearlska@etri.re.kr
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