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Computing Information Description in Computing-Aware Traffic Steering
draft-du-cats-computing-modeling-description-03

Document Type Active Internet-Draft (individual)
Authors Zongpeng Du , Kehan Yao , Cheng Li , Daniel Huang , Zhihua Fu
Last updated 2024-07-06
Replaces draft-liu-can-computing-resource-modeling
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draft-du-cats-computing-modeling-description-03
CATS                                                               Z. Du
Internet-Draft                                                    K. Yao
Intended status: Informational                              China Mobile
Expires: 7 January 2025                                            C. Li
                                                     Huawei Technologies
                                                                G. Huang
                                                                     ZTE
                                                                   Z. Fu
                                                    New H3C Technologies
                                                             6 July 2024

 Computing Information Description in Computing-Aware Traffic Steering
            draft-du-cats-computing-modeling-description-03

Abstract

   This document describes the considerations and requirements of the
   computing information that needs to be notified into the network in
   Computing-Aware Traffic Steering (CATS).

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
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   This Internet-Draft will expire on 7 January 2025.

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   Copyright (c) 2024 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.
   Please review these documents carefully, as they describe your rights
   and restrictions with respect to this document.  Code Components

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   extracted from this document must include Revised BSD License text as
   described in Section 4.e of the Trust Legal Provisions and are
   provided without warranty as described in the Revised BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Definition of Terms . . . . . . . . . . . . . . . . . . . . .   3
   3.  Problem Statement in Computing Resource Modeling  . . . . . .   4
     3.1.  Heterogeneity of Computing Resources  . . . . . . . . . .   4
     3.2.  Diversity of Service Types Resulting in Modeling
           Complexity  . . . . . . . . . . . . . . . . . . . . . . .   4
   4.  Usage of Computing Resource Modeling of CATS  . . . . . . . .   5
     4.1.  Modeling Based on CATS-defined Format . . . . . . . . . .   5
     4.2.  Modeling Based on Application-defined Method  . . . . . .   6
   5.  Computing Resource Modeling . . . . . . . . . . . . . . . . .   7
     5.1.  Requirements of Using in CATS . . . . . . . . . . . . . .   7
     5.2.  Considerations of Using in CATS . . . . . . . . . . . . .   9
     5.3.  Default Policy Discussion On Decision Point . . . . . . .  10
   6.  Network Resource Modeling . . . . . . . . . . . . . . . . . .  11
     6.1.  Consideration of Using in CATS  . . . . . . . . . . . . .  11
   7.  Application Demands Modeling  . . . . . . . . . . . . . . . .  12
     7.1.  Consideration of Using in CATS  . . . . . . . . . . . . .  12
   8.  Security Considerations . . . . . . . . . . . . . . . . . . .  12
   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  12
   10. Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  12
   11. Contributors  . . . . . . . . . . . . . . . . . . . . . . . .  12
   12. Informative References  . . . . . . . . . . . . . . . . . . .  13
   Appendix A.  Related Works on Computing Capability Modeling . . .  14
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  15

1.  Introduction

   Computing-Aware Traffic Steering (CATS) is proposed to support
   steering the traffic among different service sites according to both
   the real-time network and computing resource status as mentioned in
   [I-D.ietf-cats-usecases-requirements].  It requires the network to be
   aware of computing resource information and select a service instance
   based on the joint metric of computing and networking.

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   In order to generate steering strategies, the modeling of computing
   capability is required.  Different from the network, computing
   capability is more complex to be measured.  For instance, it is hard
   to predict how long will be used to process a specific computing task
   based on the different computing resource.  It is hard to calculate
   and will be influenced by the whole internal environments of
   computing nodes.  But there are some indicators has been used to
   describe the computing capability of hardware and computing service,
   as mentioned in Appendix A.

   Based on the related works and the demand of CATS traffic steering,
   this document analyzes the types of computing resources and tasks,
   providing the factors to be considered when modeling and evaluating
   the computing resource capability.  The detailed modeling job of the
   computing resource is not the object of this document.

2.  Definition of Terms

   This document makes use of the following terms:

   Computing-Aware Traffic Steering (CATS):  A traffic engineering
     approach [I-D.ietf-teas-rfc3272bis] that takes into account the
     dynamic nature of computing resources and network state to optimize
     service-specific traffic forwarding towards a given service contact
     instance.  Various relevant metrics may be used to enforce such
     computing-aware traffic steering policies.

   Service:  An offering that is made available by a provider by
     orchestrating a set of resources (networking, compute, storage,
     etc.).

   Service instance:  An instance of running resources according to a
     given service logic.

   Service identifier:  Used to uniquely identify a service, at the same
     time identifying the whole set of service instances that each
     represents the same service behavior, no matter where those service
     instances are running.

   Computing Capability:  The ability of nodes with computing resource
     achieve specific result output through data processing, including
     but not limited to computing, communication, memory and storage
     capability.

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3.  Problem Statement in Computing Resource Modeling

   Modeling itself provides a general method to evaluate the
   capabilities of computing resource.  For CATS, modeling-based
   computing resource representation is the basis for subsequent traffic
   steering.  In addition, for different applications, it may be
   optimized based on general modeling methods to establish a set of
   models that conform to their own characteristics, so as to generate
   corresponding representation methods.  Moreover, in order to use
   computing resource status more efficiently and protect privacy,
   modeling for the further representation of resource information needs
   to support the necessary simplification and obfuscation.  However,
   there are difficulties in compute resources modeling.

3.1.  Heterogeneity of Computing Resources

   Heterogeneous computing resources have different characteristics.
   For example, CPUs usually deal with serial processing and are most
   widely used.  GPUs usually handle parallel computing, such as
   rendering of display tasks, and are widely used in artificial
   intelligence and neural network.  FPGA and ASIC are usually used to
   handle domain specific computing tasks.  These basic computing chips
   are constructed to be different device types.  For example, standard
   servers, AI servers, all-in-one machines, etc.  These computing
   devices have multi-dimensional and hierarchical resources, such as
   cache, storage, communication, etc., and these dimensions will affect
   each other and further affect the overall level of computing
   capability.  Moreover, these computing resources may further be
   virtualized to provide on-demand cloud services, which make the
   modeling even harder.

3.2.  Diversity of Service Types Resulting in Modeling Complexity

   Modeling computing resources also depends on service types.  For
   example, videos and distributed transaction systems may need higher
   computing capabilities measured in queries per second(QPS), while AI
   inference systems may need higher computing capabilities measured in
   tokens per second(TPS), and video or object recognition may need
   higher computing capabilities measured in frame per second(FPS).
   Computing capabilities have different meanings towards different
   applications.  Moreover, different computing tasks require different
   computing precision, such as integer calculation, floating-point
   calculation, hash calculation, etc.

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4.  Usage of Computing Resource Modeling of CATS

   We need to use the computing resource modeling in two procedures.
   The first is the service deployment, and the second is the traffic
   steering, in which the later is more related to the CATS work.
   However, the service deployment is the precondition of CATS, which
   enables the assumption that the service can be accessed in multiple
   places.

   In the procedure of service deployment, a control or management
   device either in the CATS domain or in the Computing domain can
   collect the computing information and make the service deployment
   decisions.  As the procedure is not that real time, it can collect
   more information about the service points.  Many existing jobs can be
   reused here such as the ones used in the data centers.

   In the procedure of traffic steering, we can use limited metrics to
   trigger the change of the policy for the service on path, so that a
   quick response can be ensured for the change of the computing status.

   For the modeling mechanism based on CATS-defined format, the decision
   point can collect more information to support both the service
   deployment and the traffic steering.  On the contrary, the mechanism
   based on application-defined method will be more suitable for the
   CATS, in which only necessary metrics need to be notified into the
   network or called the CATS domain.  The detailed requirements of
   metric definition can be found in Section 5.

4.1.  Modeling Based on CATS-defined Format

   Figure 1 shows the case of modeling based on CATS-defined Format.
   CATS provides the modeling format to the computing domain to evaluate
   the computing resource capability of computing domain and then get
   the result based on the unified interface, which will define the
   properties should be notified to CATS.  Then CATS could select the
   specific service instance based on the computing resource and network
   resource status.

   In this way, the CATS domain and computing domain has the relative
   loose boundary based on the situation that the CATS service and
   computing resource belongs to the same provider, CATS could be aware
   of computing resource more or less, depending on the privacy
   preserving demand of the computing domain at the same time.  The
   exposed computing capability includes the static information of
   computing node category/level and the dynamic capabilities
   information of computing node.

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   Based on the static information, some visualization functions can be
   implemented on the management plane to know the global view of
   computing resources, which could also help the deployment of
   applications considering the overall distributed status of computing
   and network resource.  Based on the dynamic information, CATS could
   steer category-based applications traffic based on the unified
   modeling format and interface.

                                 |

         CATS Domain             |                   Computing Domain

+--------+  ---------------------->------------------->  +-------------+
|visuali-|                 Modeling Format               |  Computing  |
|zation  |                       |                       |             |
+--------+  <--------------------<---------------------  |  Resource   |
|Traffic |     Static level/category of computing node   |             |
|Steering|                       |                       |  Modeling   |
+--------+  <--------------------<---------------------  +-------------+
                  Dynamic capability of computing node

                                 |

                                 |

           Figure 1: Modeling Based on CATS-defined Format

4.2.  Modeling Based on Application-defined Method

   Figure 2 shows the case of modeling based on application-defined
   method.  Computing resource of the specific application evaluates its
   computing capability by itself, and then notifies the result which
   might be the index of real time computing level to CATS.  Then CATS
   selects the specific service instance based on the computing index.

   In this way, the CATS domain and computing domain has the strict
   boundary based on the situation that the CATS service and computing
   resource belongs to the different providers.  CATS is just aware of
   the index of computing resource which is defined by application,
   don't know the real status of computing domain, and the traffic
   steering right is potentially controlled under application itself.
   If CATS is authorized by application, it could steer traffic based on
   network status at the same time.

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                       |                     |
                       |                     |
       CATS Domain     |                     |       Computing Domain
                       |                     |
                       |                     |           +-------------+
+--------+             |                     |           |  Computing  |
|Traffic |             |                     |           |             |
|        |  <---------------------<---------- ---------- |  Resource   |
|Steering|    dynamic index of computing capability level  |             |
+--------+             |                     |           |  Modeling   |
                       |                     |           +-------------+
                       |                     |
                       |                     |
                       |                     |
                       |                     |

        Figure 2: Modeling Based on Application-defined Method

5.  Computing Resource Modeling

   To support a computing service in CATS, we need to evaluate the
   comprehensive service performance in a service instance, which is
   influenced by the coordination of chip, storage, network, platform
   software, etc.  It is to say that the service support capabilities
   are influenced by multidimensional factors.  After the capability
   values are generated, they are notified to the decision point in the
   network to influence the traffic steering.  However, the decision
   point in the network, for example the Ingress Node, only cares about
   how to use to capability values to do the traffic steering, but does
   not care about the way how the capability values are generated.

   From the aspect of services, they need an evaluating system to
   generate one or more capability values.  To achieve the best LB
   result, different services or service types may have different ways
   to evaluate the capability.  However, it is out of scope of the
   document.

   From the aspect the decision point in the network, it only needs to
   understand the way to use the values, and implement the related
   policy.  This document would mainly discuss about this aspect.

5.1.  Requirements of Using in CATS

   It is assumed that the same service can be provided in multiple
   places in the CATS.  In the different service instances, it is common
   that they have different kinds of computing resources, and different
   utilization rate of the computing resources.

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   In the CATS, the decision point, which should be a node in the
   network, should be aware of the network status and the computing
   status, and accordingly choose a proper service point for the client.

   A general process to steer the CATS traffic is described as below.
   The CATS packets have an destination address as the service ID that
   is announced by the different service points.

   Firstly, the service points need to collect some specific computing
   information that need to be sent into the network following a uniform
   format so that the decision point can understand the computing
   information.  In this step, only necessary computing information
   needs to be considered, so as to avoid exposing too much information
   of the service points.

   Secondly, the service instances send the computing information into
   the network by some means, and update it periodic or on demand.

   Thirdly, the decision point receives the computing information, and
   makes a decision for the specific service related to the service ID.
   Hence, the route for the service ID on the Ingress is established or
   updated.

   Fourthly, the traffic for the service ID reaching the Ingress node
   would be identified and steered according to the policy in the step3.

   In fact, what to send, how to send, and the optimization objective of
   the policy are all related to the design of the computing resource
   modeling in CATS, meanwhile they would influence each other.  Some
   requirements are listed below.

   1.  The optimization objective of the policy in the decision point
       may be various.  For example, it may be the lowest latency of the
       sum of the network delay and the computing delay, or it may be an
       overall better load balance result, in which we would prefer the
       service points that could support more clients.

   2.  The update frequency of the computing metrics may be various.
       Some of the metrics may be more dynamic, and some are relatively
       static.

   3.  The notification ways of the computing metrics may be various.
       According to its update frequency, we may choose different ways
       to update the metric.

   4.  Metric merging process should be supported when multiple service
       instances are behind the same Egress.

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   The target in CATS mainly concerns about the service point selection
   and traffic steering in Layer3, in which we do not need all computing
   information of the service points.  Hence, we can start with simple
   cases in the work of the computing resource modeling in CATS.  Some
   design principles can be considered.

   1.  Simplicity: The computing metrics in CATS SHOULD be few and
       simple, so as to avoid exposing too much information of the
       service points.

   2.  Scalability: The computing metrics in CATS SHOULD be evolveable
       for the future extensions.

   3.  Interoperability: The computing metrics in CATS SHOULD be vendor-
       independent, and OS-independent.

   4.  Stability: computing metrics SHOULD NOT incur too much overhead
       in protocol design, and it can be stabilized to be used.

   5.  Accuracy: computing metrics SHOULD be effective for path
       selection decision making, and the accuracy SHOULD be guaranteed.

5.2.  Considerations of Using in CATS

   Various metrics can be considered in CATS, and perhaps different
   services would need different metrics.  However, we can start with
   simple cases.

   In CATS, a straightforward intent is to minimal the total delay in
   the network domain and the computing domain.  Thus, we can have a
   start point for the metric designation in CATS considering only the
   delay information.  In this case, the decision point can collect the
   network delay and the computing delay, and make a decision about the
   optimal service point accordingly.  The advantage of this method is
   that it is simple and easy to start; meanwhile, the network metric
   and the computing metric have the same unit of measure.  The network
   delay can be the latency between the Ingress node and Egress node in
   the network.  The computing delay can be generated by the server,
   which has the meaning of “the estimate of the duration of my
   processing of request”. It is usually an average value for the
   service request.  The optimization objective of traffic steering in
   this scenario is the minimal total delay for the client.

   Another metric that can be considered is the server capability.  For
   example, one server can support 100 simultaneous sessions and another
   can support 10,000 simultaneous sessions.  The value can be generated

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   by the server when deploying the service instance.  The metric can
   work alone.  In this scenario, the decision point can do a Load
   Balance job according to the server capability.  For example, the
   decision process can be load balancing after pruning the service
   points with poor network latency metrics.  Also, the metric can work
   with the computing delay metric.  For example, in this scenario, we
   can prune the service points with poor total latency metrics before
   the load balancing.

   In future, we can also consider other metrics, which may be more
   dynamic.  Besides, for some other optimization objectives, we can
   consider other metrics, even metrics about energy consumption.
   However, in this cases, the decision point needs to consider more
   dimensions of metrics.  A suggestion is that we should firstly make
   sure the service point is available, which means the service point
   can still accept more sessions, and then select a optimal target
   service point according to the optimization objective.

5.3.  Default Policy Discussion On Decision Point

   To enable the basic cooperation in CATS, we need one or a set of
   default computing metrics to be notified into the network.  All the
   CATS Ingresses need to understand the default metrics and trigger the
   same or similar operations, i.e., as the default policies, inside the
   router.  The detailed procedures inside the Ingresses are vendor-
   specific.

   By comparison, other metrics would be optionally, although perhaps
   they can obtain a better or more preferred LB result than the default
   ones.  If the Ingress receives the additional metrics and can
   understand them, it can use the optional metrics to update the
   default forwarding policy for the routes of the anycast IP.

   There are two kinds of forwarding treatments on the Ingress.
   Although they are implementations inside the equipment, we give a
   general description about them here, because they are related to the
   default metric selection.

   The first one is that the Ingress will deploy several routes for the
   anycast IP, but among them only one is active, and others are for
   backup and are set to inactive.  The second one it that the Ingress
   can have multiple active routes for the anycast IP, and each route
   has a dedicated weight, so that a load balancing can be done within
   the Ingress.

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   The advantage of the first one is that it can select a best service
   instance for the client according to the network and computing
   status.  However, its disadvantage is that the Ingress will forward
   all the new clients to a single service point before the policy is
   updated, which will potentially cause the service point to become
   busy.  For the second one, it may achieve a better LB result.

   An initial proposal of the default metrics for the default policies
   is that we can always send the two metrics mentioned in the last
   paragraph, i.e., the computing delay and the server capability.  At
   least one of them should be valid.  The bits of the computing delay
   or the server capability are set to all "zero" will be considered
   invalid, and other values are considered valid.  Meanwhile, the bits
   of the computing delay or the server capability are set to all "one"
   stands for the service point is temporary busy, and the Ingress
   should not send new clients to that service point.  Alternatively, we
   can also add another simple metric to indicate the busy or not
   status.  However, this metric is relatively more dynamic than the
   former two.

6.  Network Resource Modeling

   The modeling of the network resource is optional, which depends on
   how to select the service instance and network path.  For some
   applications which care both network and computing resource, the CATS
   service provider also need to consider the modeling of network and
   computing together.

   The network structure can be represented as graphs, where the nodes
   represent the network devices and the edges represent the network
   path.  It should evaluate the single node, the network links and the
   E2E performance.

6.1.  Consideration of Using in CATS

   When to consider both the computing and network status at the same
   time, the comprehensive modeling of computing and network might be
   used.  For example, to measure all the resource in a unified
   dimension, such as latency, reliability, etc.

   If there is no strict demand of consider them at same time, for
   instance, consider computing status first and then network status.
   CATS could select the service instance at first, then to mark
   identifier for network path selection of network itself.  In this
   situation, the network modeling is not that needed.  Existing
   mechanisms on the control plane or the management plane in the
   network can be used to obtain the network metrics.

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7.  Application Demands Modeling

   The application always has its own demands for network and computing
   resource, for instance we can see the HD video always requires the
   high bandwidth and the PC game always requires the better GPU and
   memory.  The application is identified by using the Service
   Identifier in the network, which can indicate its demands in a
   certain degree.

7.1.  Consideration of Using in CATS

   The modeling of the application demand is optional, which depends on
   whether the application could tell the demands to the network, or
   what it could tell.  Once the CATS knows the application's demand,
   there should be a mapping between application demand and the modeling
   of the computing and/or network resource.

8.  Security Considerations

   TBD.

9.  IANA Considerations

   TBD.

10.  Acknowledgements

   The author would like to thank Adrian Farrel, Joel Halpern, Tony Li,
   Thomas Fossati, Dirk Trossen, Linda Dunbar for their valuable
   suggestions to this document.

11.  Contributors

   The following people have substantially contributed to this document:

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           Yuexia Fu
           China Mobile
           fuyuexia@chinamobile.com

           Jing Wang
           China Mobile
           wangjingjc@chinamobile.com

           Peng Liu
           China Mobile
           liupengyjy@chinamobile.com

           Wenjing Li
           Beijing University of Posts and Telecommunications
           wjli@bupt.edu.cn

           Lanlan Rui
           Beijing University of Posts and Telecommunications
           llrui@bupt.edu.cn

12.  Informative References

   [I-D.ietf-cats-usecases-requirements]
              Yao, K., Trossen, D., Contreras, L. M., Shi, H., Li, Y.,
              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-03, 3 July 2024,
              <https://datatracker.ietf.org/doc/html/draft-ietf-cats-
              usecases-requirements-03>.

   [I-D.ietf-teas-rfc3272bis]
              Farrel, A., "Overview and Principles of Internet Traffic
              Engineering", Work in Progress, Internet-Draft, draft-
              ietf-teas-rfc3272bis-27, 12 August 2023,
              <https://datatracker.ietf.org/doc/html/draft-ietf-teas-
              rfc3272bis-27>.

   [One-api]  One-api, "http://www.oneapi.net.cn/", 2020.

   [Amazon]   Amaozn,
              "https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-
              scaling-target-tracking.html#available-metrics", 2022.

   [Aliyun]   Aliyun, "https://help.aliyun.com/?spm=a2c4g.11186623.6.538
              .34063af89EIb5v", 2022.

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   [Tencent-cloud]
              Tencent-cloud, "https://buy.cloud.tencent.com/pricing",
              2022.

   [cloud-network-edge]
              cloud-network-edge, "A new edge computing scheme based on
              cloud, network and edge fusion", 2020.

   [heterogeneous-multicore-architectures]
              access, I., "Towards energy-efficient heterogeneous
              multicore architectures for edge computing", 2019.

   [ARM-based]
              Guide, S., "A heterogeneous CPU-GPU cluster scheduling
              model based on ARM", 2017.

Appendix A.  Related Works on Computing Capability Modeling

   Some related work has been proposed to measurement and evaluate the
   computing capability, which could be the basis of computing
   capability modeling.

   [cloud-network-edge] proposed to allocate and adjust corresponding
   resources to users according to the demands of computing, storage and
   network resources.

   [heterogeneous-multicore-architectures] proposed to design
   heterogeneous multi-core architectures according to different
   customization, such as CPU microprocessors with ultra-low power
   consumption and high code density, low power microprocessor with FPU,
   and a high-performance application processor with FPU and MMU support
   based on a completely unordered multi problem architecture.

   [ARM-based] proposed the cluster scheduling model that is combined
   with GPU virtualization and designed a hierarchical cluster resource
   management framework, which can make the heterogeneous CPU-GPU
   cluster be effectively used.

   The hardware cloud service providers have also disclosed their
   parameter indicators for computing services:

   [One-api] provides a collection of programming languages and cross
   architecture libraries across different architectures, to be
   compatible with heterogeneous computing resources, including CPU,
   GPU, FPGA, and others.  [Amazon] uses the computing resource
   parameters when evaluating the performance, including the average CPU
   utilization, average number of bytes received and sent out, and

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Internet-Draft  Computing Information Description in CAT       July 2024

   average application load balancer.  Alibaba cloud [Aliyun] gives the
   indicators including vcpu, memory, local storage, network basic and
   burst bandwidth capacity, network receiving and contracting
   capability, etc., when providing cloud servers service.
   [Tencent-cloud] uses vcpu, memory (GB), network receiving and sending
   (PPS), number of queues, intranet bandwidth capacity (Gbps), dominant
   frequency, etc.

Authors' Addresses

   Zongpeng Du
   China Mobile
   No.32 XuanWuMen West Street
   Beijing
   100053
   China
   Email: duzongpeng@foxmail.com

   Kehan Yao
   China Mobile
   No.32 XuanWuMen West Street
   Beijing
   100053
   China
   Email: yaokehan@chinamobile.com

   Cheng Li
   Huawei Technologies
   Email: c.l@huawei.com

   Guangping Huang
   ZTE
   Email: huang.guangping@zte.com.cn

   Zhihua Fu
   New H3C Technologies
   Email: fuzhihua@h3c.com

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