Computing Information Description in Computing-Aware Traffic Steering
draft-du-cats-computing-modeling-description-00

CATS                                                               Z. Du
Internet-Draft                                                     Y. Fu
Intended status: Informational                              China Mobile
Expires: 7 September 2023                                          C. Li
                                                     Huawei Technologies
                                                                G. Huang
                                                                     ZTE
                                                            6 March 2023

 Computing Information Description in Computing-Aware Traffic Steering
            draft-du-cats-computing-modeling-description-00

Abstract

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

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   This Internet-Draft will expire on 7 September 2023.

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   Copyright (c) 2023 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Definition of Terms . . . . . . . . . . . . . . . . . . . . .   3
   3.  Problem Statement in Computing Resource Modeling  . . . . . .   4
     3.1.  Heterogeneous Chips and Different Computing Types . . . .   4
     3.2.  Multi-dimensional Modeling  . . . . . . . . . . . . . . .   4
     3.3.  Support to be used for Further Representation . . . . . .   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 . . . . . . . . . . . . . . . . .   6
     5.1.  Consideration of Using in CATS  . . . . . . . . . . . . .   7
   6.  Network Resource Modeling . . . . . . . . . . . . . . . . . .   8
     6.1.  Consideration of Using in CATS  . . . . . . . . . . . . .   8
   7.  Application Demands Modeling  . . . . . . . . . . . . . . . .   9
     7.1.  Consideration of Using in CATS  . . . . . . . . . . . . .   9
   8.  Security Considerations . . . . . . . . . . . . . . . . . . .   9
   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   9
   10. Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .   9
   11. Contributors  . . . . . . . . . . . . . . . . . . . . . . . .   9
   12. Informative References  . . . . . . . . . . . . . . . . . . .  10
   Appendix A.  Related Works on Computing Capacity Modeling . . . .  11
   Appendix B.  Architecture of Computing Modeling . . . . . . . . .  12
     B.1.  Computing Capacity  . . . . . . . . . . . . . . . . . . .  13
       B.1.1.  Types of Chips  . . . . . . . . . . . . . . . . . . .  13
       B.1.2.  Type of Computing . . . . . . . . . . . . . . . . . .  14
       B.1.3.  Relation of Computing Types and Chips . . . . . . . .  15
     B.2.  Communication, Cache and Storage Capacity . . . . . . . .  15
     B.3.  Comprehensive Computing Capability Evaluation . . . . . .  16
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  16

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1.  Introduction

   Computing-Aware Traffic Steering (CATS) is proposed to support
   steering the traffic among different edge sites according to both the
   real-time network and computing resource status as mentioned in
   [I-D.yao-cats-ps-usecases] and [I-D.yao-cats-gap-reqs].  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.

   In order to generate steering strategies, the modeling of computing
   capacity is required.  Different from the network, computing capacity
   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
   capacity 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 capacity.  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):  Aiming at computing and
     network resource optimization by steering traffic to appropriate
     computing resources considering not only routing metric but also
     computing resource metric.

   Service:  A monolithic functionality that is provided by an endpoint
     according to the specification for said service.  A composite
     service can be built by orchestrating monolithic services.

   Service instance:  Running environment (e.g., a node) that makes the
     functionality of a service available.  One service can have several
     instances running at different network locations.

   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.

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   Service transaction:  Has one or more service request that has
     several flows which require the affinity because of the transaction
     related state.

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

3.  Problem Statement in Computing Resource Modeling

3.1.  Heterogeneous Chips and Different Computing Types

   Different heterogeneous computing resources have different
   characteristics.  For example, CPUs usually deal with pervasive
   computing 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 algorithm computing.  FPGA
   and ASIC are usually used to handle customized computing.  At the
   same time, different computing tasks need to call different
   calculation types, such as integer calculation, floating-point
   calculation, hash calculation, etc.

3.2.  Multi-dimensional Modeling

   The network and computing 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 capacity.  Other factors besides the computing
   itself need to be considered in modeling.  At the same time, the form
   of computing resources is also hierarchical, such as computing type,
   chip type, hardware type, and converging with the network.  For
   different computing forms, such as gateway, all-in-one machine, edge
   cloud and central cloud, the computing capacity, and types provided
   are also different.  It is necessary to comprehensively consider
   multi-dimensional and multi-modal resources, and provide multi-level
   modeling according to application demands.

3.3.  Support to be used for Further Representation

   Modeling itself provides a general method to evaluate the capacities
   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

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   representation of resource information needs to support the necessary
   simplification and obfuscation.

4.  Usage of Computing Resource Modeling of CATS

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 capacity 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 capacity includes the static information of
   computing node category/level and the dynamic capabilities
   information of computing node.

   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

                                   |

                                   |

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           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 capacity 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.

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

        Figure 2: Modeling Based on Application-defined Method

5.  Computing Resource Modeling

   To support a computing service, we need to evaluate the comprehensive
   service performance in a service point, 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.  Therefore, in the modeling of the
   computing metric, we can provide not only the specification computing
   values provided by the manufacturer, such as FLOPS, but also some
   integrated index values that can comprehensively reflect the service
   support capabilities.

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   We can build up a computing resource modeling system which contains
   three levels of indicators, and the architecture can refer to
   Appendix B.

   The first level is about the heterogeneous hardware computing
   capability.  The indexes of this level can be the performance
   parameters provided by the manufacturer, such as CPU model, main
   frequency, number of cores, GPU model, single-precision floating-
   point performance, etc.  Meanwhile, the indexes can also be the test
   values of commonly used benchmark programs.

   The second-level indexes are abstracted from the first-level indexes,
   which are mainly used for the comprehensive evaluation of node's
   computing capability.  They can provide the ability of a certain
   aspect of the node, such as in the aspect of computing,
   communication, cache, and storage, or a general comprehensive service
   ability of the node.

   Level 3 indexes are related to the services deployed on the nodes.
   They mainly provide service-related evaluation parameters, such as
   the actual processing throughput that nodes can provide for a
   specific computing service.  It can also be a test value, but it is
   generated by running the real service.

5.1.  Consideration of Using in CATS

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

   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.
   In fact, the decision would influenced more by the dynamic indexes.
   An example of the decision process is described below.

   Firstly, the decision point needs to make sure the candidate service
   points can still access a new session.  If a service point claims
   that it is busy, no packet of new clients should be steered to it.

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   Secondly, the decision point can select a service point with a higher
   comprehensive performance evaluation value for the service.  If Level
   3 indexes for the service are available in the decision point, the
   decision point can use it directly because it is most
   straightforward.  If not, for example the service is not tested in
   the service point yet, Level 2 indicators can be used instead with
   configurable weight values for the four aspects as mentioned in
   Appendix B.

   In this example, the index in the first step is dynamic, and is
   related to the service status.  The index in the second step is
   relatively static, and is related to computing efficiency for the
   service.

   For a specific service, more indicators can still be provided.  It is
   to say that the computing information could be customized for
   different services.  Additionally, besides the computing information,
   the energy consumption index can also be included if it is considered
   necessary when making a decision.

   Therefore, in this example, CATS needs two indexes at least, one for
   the service status, and another for service ability.  Optionally,
   other information can also be provided if it is subscribed by the
   decision point.  The detailed decision process in the decision point
   is out of scope of this document.

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.

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   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.

7.  Application Demands Modeling

   The application is usually composed of several sub service that
   complete different functions, and the service is usually composed of
   several sub transactions, which would be the smallest schedulable
   unit.

   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.

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 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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           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.yao-cats-ps-usecases]
              Yao, K., Eardley, P., Trossen, D., Boucadair, M.,
              Contreras, L. M., Li, C., Li, Y., and P. Liu, "Computing-
              Aware Traffic Steering (CATS) Problem Statement and Use
              Cases", Work in Progress, Internet-Draft, draft-yao-cats-
              ps-usecases-00, 3 March 2023,
              <https://datatracker.ietf.org/doc/html/draft-yao-cats-ps-
              usecases-00>.

   [I-D.yao-cats-gap-reqs]
              Yao, K., Jiang, T., Eardley, P., Trossen, D., Li, C., and
              D. Huang, "Computing-Aware Traffic Steering (CATS) Gap
              Analysis and Requirements", Work in Progress, Internet-
              Draft, draft-yao-cats-gap-reqs-00, 3 March 2023,
              <https://datatracker.ietf.org/doc/html/draft-yao-cats-gap-
              reqs-00>.

   [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.

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

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   [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 Capacity Modeling

   Some related work has been proposed to measurement and evaluate the
   computing capacity, which could be the basis of computing capacity
   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:

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   [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
   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 capacity,
   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.

Appendix B.  Architecture of Computing Modeling

   This Appendix describes the potential architecture of computing
   resource modeling, regardless of any ways of the further usage of
   traffic steering of CATS, neither of the usage ways described in
   Section 4.

   According to the computing indicators and related work described in
   Section 2, computing capacity includes the types of computing
   resources and tasks, and also need to consider multi-dimensional
   capabilities such as communication, memory, and storage.  Because
   every factor will affect each others.  For instance, with the rapid
   growth of modern computer CPU performance, the communication
   bottleneck between CPU and cache has become increasingly prominent.
   Moreover, the storage capacity greatly affects the processing speed
   of a computer.  So the architecture of computing capacity modeling
   could be seen in Figure 3.

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                                                           +-------+      +-------+
                                                        +--|  CPU  |  +---|  GPU  |
                                       +-------------+  |  +-------+  |   +-------+
                                       |    Chips    |--+-------------+
                                    +--|  Category   |  |  +-------+  |   +-------+
                                    |  +-------------+  +--| FPGA  |  +---|  ASIC |
                   +-------------+  |                      +-------+      +-------+
                   |  Computing  |--+
                +--|  Capacity   |--+                      +----------------------+
                |  +-------------+  |                   +--|  intCalculationRate  |
                |  +-------------+  |  +-------------+  |  +----------------------+
                +--|Communication|  +--|  Computing  |  |  +----------------------+
+-------------+ |  |  Capacity   |     |    Types    |--+--| floatCalculationRate |
|  Computing  | |  +-------------+     +-------------+  |  +----------------------+
|  Resource   |-+  +-------------+                      |  +----------------------+
|  Modeling   | |  |   Cache     |                      +--|  hashCalculationRate |
+-------------+ +--|  Capacity   |                         +----------------------+
                |  +-------------+
                |  +-------------+
                +--|  Storage    |
                   |  Capacity   |
                   +-------------+

    Figure 3: Referecen Architecture of Computing Modeling Format

B.1.  Computing Capacity

   The computing capacity includes the chips category and computing
   types.  Common chip types include CPU, GPU, FPGA and ASIC.  CPU and
   GPU belong to von Neumann structure, with instruction decoding and
   execution and shared memory.  According to the different
   characteristics and requirements of computing programs, the computing
   performance can be divided into integer computing performance,
   floating-point computing performance and hash computing performance.

B.1.1.  Types of Chips

   CPU (Central Processing Unit) is a general-purpose processor needs to
   be able to handle comprehensive and complex tasks, as well as the
   synchronization and coordination between tasks.  Therefore, a lot of
   space is required on the chip to perform branch prediction and
   optimization and save various states to reduce the delay during task
   switching.  This also makes it more suitable for logic control,
   serial operation and universal type data operation.

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   GPU (Graphics Processing Unit) has a large-scale parallel computing
   framework composed of thousands of smaller and more efficient Alu
   cores.  Most transistors are mainly used to build control circuits
   and caches, and the control circuits are relatively simple.

   FPGA (Field Programmable Gate Array) is essentially an architecture
   without instructions and shared memory, which is more efficient than
   GPU and CPU.  The main advantage of FPGA in data processing tasks is
   its stability and extremely low latency, which is suitable for
   streaming computing intensive tasks and communication intensive
   tasks.

   ASIC (Application Specific Integrated Circuit) is a special
   integrated circuit, and its performance is actually better than FPGA.
   However, for customized customers, its cost is much higher than FPGA.

   On this basis, according to different computing task requirements,
   chip manufacturers have also developed various "xpus", including APU
   (Accelerated Processing Unit), DPU (Deep-learning Processing Unit),
   TPU (Tensor Processing Unit), NPU (Neural-network Processing Unit)
   and BPU (Brain Processing Unit), which are made based on the CPU,
   GPU, FPGA and ASIC.

B.1.2.  Type of Computing

   At present, the computing type in computer mainly includes integer
   calculation, floating-point calculation, and hash calculation.

   The integer calculation rate is expressed as the calculation rate of
   the integer data operation benchmark program running on the CPU.
   Integer computing capability has its specific application scenarios,
   such as discrete-time processing, data compression, search, sorting
   algorithm, encryption algorithm, decryption algorithm, etc.

   Floating point calculation rate is expressed as the calculation rate
   of the floating-point data operation benchmark program running on the
   CPU.  There are many kinds of benchmark programs, each of which can
   reflect the floating-point computing performance of nodes from
   different aspects.

   The hash calculation rate refers to the output speed of the hash
   function when the computer performs intensive mathematical and
   encryption related operations.  For example, in the process of
   obtaining bitcoin through "mining", how many hash collisions can a
   mining machine do per second, and the unit is hash/s.

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B.1.3.  Relation of Computing Types and Chips

   The differences computing capacity of the above different chip types
   is summarized as figure 4 shows.  CPU is good at intCalculation, GPU
   and FPGA are good at floatCalculation, and ASIC is good at
   intCalculation.

   +-----+------------------+------------------+------------------+
   |     |  intCalculation  | floatCalculation |  hashCalculation |
   +-----+------------------+------------------+------------------+
   | CPU |        good      |      Ordinary    |      Ordinary    |
   +-----+------------------+------------------+------------------+
   | GPU |      Ordinary    |        good      |      Ordinary    |
   +-----+------------------+------------------+------------------+
   | FPGA|      Ordinary    |        good      |      Ordinary    |
   +-----+------------------+------------------+------------------+
   | ASIC|      Ordinary    |        good      |        good      |
   +-----+------------------+------------------+------------------+

              Figure 4: Relation of Computing Types and Chips

B.2.  Communication, Cache and Storage Capacity

   Besides the computing capacity, the communication, cache, and storage
   capacity should also be considered because each of them can
   potentially influence the comprehensive capacity of computing
   resource nodes.

   The communication capacity is the external communication rate of
   computing nodes.  From the point of view of a single node, the
   communication capability indicator of a node mainly includes the
   network bandwidth.  Moreover, it is often to have cluster of service
   instances for one task (like Hadoop architecture).  Therefore the
   network capacity among those instances are also important factor in
   assessing the capability of the cluster of the service nodes for one
   task.

   The cache(memory) capacity describers the amount of of the cache unit
   on a node.  The memory (CACHE) indicator mainly includes the
   cache(memory) capacity and cache(memory) bandwidth.

   The storage capacity is the external storage (for example, hard disk)
   of the computing node.  The storage indicators of a node mainly
   includes the storage capacity, storage bandwidth, operations per
   second (IOPs) and response time of the node.

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B.3.  Comprehensive Computing Capability Evaluation

   Based on the architecture of computing resource modeling, this
   Section proposes the comprehensive performance evaluation methods
   based on the vectors to represent each capability of computing,
   communication, cache, and storage.

   Figure 5~8 shows the vector of computing node(i) including each
   aspects.

        +-                         -+
   A(i)=|   Computing Capacity(i)   |
        +-                         -+

                   Figure 5: Computing Performance Vector

        +-                         -+
   B(i)=|  Communication Capacity(i) |
        +-                         -+

                 Figure 6: Comunication Performance Vector

        +-                        -+
   C(i)=|     Cache Capacity(i)    |
        +-                        -+

                     Figure 7: Cache Performance Vector

        +-                         -+
   D(i)=|    Storage Capacity(i)    |
        +-                         -+

                    Figure 8: Storage Performance Vector

   The vector of computing capacity, communication capacity, cache
   capacity and storage capacity could be further weighted to a
   comprehensive vector.

   V = aA+bB+cC+dD

                       Figure 9: Comprehensive Vector

   Where, a, b, c and d are the weight coefficients corresponding to the
   evaluation indicators of computing capacity, communication capacity,
   cache capacity and storage capacity respectively, and a+b+c+d=1.

Authors' Addresses

Du, et al.              Expires 7 September 2023               [Page 16]
Internet-Draft  Computing Information Description in CAT      March 2023

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

   Yuexia Fu
   China Mobile
   No.32 XuanWuMen West Street
   Beijing
   100053
   China
   Email: fuyuexia@chinamobile.com

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

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

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