diff --git a/CFN-Overview-&-Use-Case-Exploration-cn.md b/CFN-Overview-&-Use-Case-Exploration-cn.md index bac51f1..146eb7b 100644 --- a/CFN-Overview-&-Use-Case-Exploration-cn.md +++ b/CFN-Overview-&-Use-Case-Exploration-cn.md @@ -105,7 +105,7 @@ Jianchao Guo (AsiaInfo), Jian Xu (China Mobile), Jie Nie (China Mobile), Jintao ## 4.1 基于算网融合的VDI云桌面 |
场景名称
| 基于算网融合的VDI云桌面 | -|------------------------|--------------------------------------| +|:----|:----| | 贡献者 | 新华三-付志华 | | 应用名称 | VDI云桌面 | | 场景描述 | 随着全社会全行业数字化转型的不断变革，云上办公变得越来越普遍。云上办公具有资源随选，方便快捷，移动性强等特点，受到大中型企业的青睐。云桌面是一种具体的实现方式。通过集中管理企业员工所需要的办公计算资源，采用规模化，数字化的手段，可以减少企业IT运营和成本支出，提高生产效率。由于企业有遍布全国乃至全球的分支机构，既有对算力的要求，也有对网络的要求。因此，此场景可以认为是算力网络的一个典型场景。| @@ -119,7 +119,7 @@ Jianchao Guo (AsiaInfo), Jian Xu (China Mobile), Jie Nie (China Mobile), Jintao ## 4.2 基于AI的算力网络流量控制与算力匹配 |
场景名称
| AI as a Service/AI即服务 | -|------------------------|-----------------| +|:----|:----| | 贡献者 | 中国移动美研所-潘伟森 | | 应用名称 | 基于AI的算力网络流量控制与算力匹配 | | 场景描述 | 1. 算力网络集成了分布于不同地理位置的分布式和泛在化的计算能力，其来源包括了各种计算设备如云计算节点、边缘计算节点、终端设备、网络设备等， 在算力网络环境中的计算任务量大、类型多样，包括数据分析、AI推理、图形渲染等各类计算任务，在这种情况下，传统的流量控制策略可能无法有效处理任务的多样性和量级，可能导致计算资源的浪费、计算任务的延迟、服务质量下降等问题。为了解决这些问题，可以采用基于AI的流量控制与算力匹配，通过收集大量的网络流量数据、设备状态数据和任务需求数据，使用深度学习算法训练AI模型。模型能够学习到网络流量和计算任务的模式，预测未来的流量变化和任务需求，以及设备的计算能力，并根据这些信息实时调整流量控制策略与算力匹配策略。
2. 在AI的帮助下，运营商能够更有效地管理流量和算力，减少网络拥堵，提高计算资源的利用率，降低计算任务的延迟，提高服务质量。例如，在预测到大量的数据分析任务将要到来时，AI系统可以提前调整网络配置，优先将计算资源分配给这些任务，以满足需求。在预测到计算设备的能力不足以处理即将到来的任务时，AI系统可以提前调整流量控制策略，将部分任务重定向到其他设备，以防止拥堵。
3. 基于AI的算力网络流量控制与算力匹配将大规模的算力网络带来了显著的性能提升，使得运营商能够更好地管理计算资源，满足各类计算任务的需求。
![AI流量协同示意图](https://opendev.org/cfn/use-case-and-architecture/src/branch/master/figures/4.2%E5%9B%BE-AI-workload.png)| @@ -132,7 +132,7 @@ Jianchao Guo (AsiaInfo), Jian Xu (China Mobile), Jie Nie (China Mobile), Jintao ## 4.3 算力网络视频调度 |
场景名称
| 算力网络视频调度 | -|------------------------|-----------------------------------------| +|:----|:----| | 贡献者 | 浪潮-耿晓巧 | | 应用名称 |面向视频应用的算网一体调度| | 场景描述 | 基于轨道交通等智能视频场景，以客户业务为中心，构建算网一体的任务式服务调度能力，感知并解析用户对时延、成本等的业务需求，进行计算、存储等资源的协同调度和分配，并实现调度策略的动态优化，实现向客户提供按需、灵活、智能的算力网络服务，同时广泛适配各类行业应用场景，赋能视频业务智能转型。 | @@ -144,7 +144,7 @@ Jianchao Guo (AsiaInfo), Jian Xu (China Mobile), Jie Nie (China Mobile), Jintao ## 4.4 基于多方安全计算的借贷风险评估 |
场景名称
| 基于多方安全计算的借贷风险评估 | -|------------------------|-------| +|:----|:----| | 贡献者 | 亚信-郭建超 | | 应用名称 | 隐私计算 | | 场景描述 |当个人/企业向银行进行贷款申请时，银行需评估借贷风险，排查用户多头借贷及超额借贷的风险。通过搭建的隐私计算平台，运用隐私查询、多方联合统计，联合多家银行，在贷前对用户各银行的借贷总额进行联合统计。银行收到联合统计结果后，决定是否向用户发放贷款。
在该场景下，隐匿查询服务和多方联合统计服务与算力网络高度相关：
1. 隐匿查询服务：通过隐私计算，查询方可隐藏被查询对象关键词或客户ID信息，数据提供方提供匹配的查询结果却无法获知具体对应哪个查询对象，能杜绝数据缓存、数据泄漏、数据贩卖的可能性。
2. 多方联合统计服务：通过隐私计算，可使多个非互信主体在数据相互保密的前提下进行高效数据融合计算，达到“数据可用不可见”，最终实现数据的所有权和数据使用权相互分离。
![隐私计算场景示意图](https://opendev.org/cfn/use-case-and-architecture/src/branch/master/figures/4.4%E5%9B%BE-%E9%9A%90%E7%A7%81%E8%AE%A1%E7%AE%97-1.png)| @@ -156,7 +156,7 @@ Jianchao Guo (AsiaInfo), Jian Xu (China Mobile), Jie Nie (China Mobile), Jintao ## 4.5 基于算力网络的AI应用跨架构部署迁移 |
场景名称
| 基于算力网络的AI应用灵活部署迁移 | -|------------------------|-------------------------------------------------------| +|:----|:----| | 贡献者 | 中国移动研究院-赵奇慧 | | 应用名称 | / | | 场景描述 | 用户在向算力网络申请AI服务时，以人脸识别业务（AI推理类）为例，将提供应用名称、待处理的数据集信息（如图片传输接口、图片数量、图片大小）、倾向的检测地点、处理速率、成本约束等，由算力网络自行根据用户需求选择合适的算力集群部署人脸识别业务、完成配置并提供服务。
由于算力网络将来源各异、类型各异的算力构成一张统一的网络，凡符合用户SLA需求，具体承载该人脸识别业务并提供计算的底层算力可以有多种选择，可为英伟达GPU、Intel CPU、华为NPU、寒武纪MLU、海光DCU及众多其他智算芯片中的任意一种或多种组合。因此，AI应用在算力网络中的多种异构异厂商智算芯片上部署、运行、迁移，是算力网络的典型应用场景之一。此场景与云计算智算应用跨架构迁移场景类似。
除用户向算力网络申请AI服务外，上述场景也适合用户在算力网络中部署自研AI应用。 | @@ -168,7 +168,7 @@ Jianchao Guo (AsiaInfo), Jian Xu (China Mobile), Jie Nie (China Mobile), Jintao ## 4.6 算力网络裸金属管理 |
场景名称
| 算力网络裸金属管理 | -|------------------------|--------------------------------------------| +|:----|:----| | 贡献者 | 中国移动研究院-王锦涛、中国移动研究院-赵奇慧 | | 应用名称 | / | | 场景描述 | 根据2.3节，算力网络提供资源型服务模式，支持用户直接向算力网络申请裸机、虚机、容器等资源。部分用户也因性能、安全、业务虚拟化/容器化改造难度等方面的考虑，更倾向于使用裸金属服务。传统裸金属服务资源难以灵活配置、客户需要定向适配各类驱动、发放和管理流程冗长，无论是客户体验还是管理运维手段，较虚拟化服务有较大差距。因此如何实现算力网络服务商灵活管理和运维多样基础设施、并简化用户使用裸金属是算力网络数据中心的重要需求。该场景也适用于云计算及云网融合领域。 | diff --git a/CFN-Overview-&-Use-Case-Exploration-en.md b/CFN-Overview-&-Use-Case-Exploration-en.md index 33ca4d3..331ba05 100644 --- a/CFN-Overview-&-Use-Case-Exploration-en.md +++ b/CFN-Overview-&-Use-Case-Exploration-en.md @@ -72,6 +72,7 @@ Jianchao Guo (AsiaInfo), Jian Xu (China Mobile), Jie Nie (China Mobile), Jintao **S**imilar to the SaaS provided by public cloud, the software-based service mode of Compute Power Network generally means that users choose a specific application software through the service provider platform. Compute Power Network deploys application for users, and users can access application through APIs, without paying attention to the details of the system, software, deployment location, etc. 4. **Task-based service mode** + **T**ask-based service mode, which can also be represented as Task as a Service, is the most intelligent and ideal service mode for computing force network. Users only need to provide descriptive explanations of the tasks to computing force network (such as "deploying a video surveillance business near school A"). The computing force network service provider can understand user needs (such as business type, service quality, reliability, security, cost, etc.), select computing centers independently and flexibly, and select suitable software/application for described scenarios. After deploying and configuring the software/application, the resources and business information of software/application instances will be registered to computing force network control platform, and the instances status will be monitored and updated regularly. Computing force network needs to perceive the location and status of software/application instances, generate routing strategies for accessing these instances, and distribute the strategies to routing nodes in the network. After receiving the message to access the software/application instances, the routing node forwards it according to the issued routing strategies. These strategies often require a comprehensive consideration of computing force state and network state to make the optimized decision. The perception and routing strategy generation for software/application can also be completed by network nodes themselves. ## 2.4 Computing force network VS Integrated cloud and network @@ -111,7 +112,7 @@ Jianchao Guo (AsiaInfo), Jian Xu (China Mobile), Jie Nie (China Mobile), Jintao ## 4.1 Virtual Desktop Infrastructure Based on Computing and Network Convergence |
Use Case Name
| Virtual Desktop Infrastructure Based on Computing and Network Convergence | -|------------------------|--------------------------------------| +|:----|:----| | Contributor | Zhihua Fu - New H3C Technologies Co., Ltd. | | Application Name | Virtual Desktop Infrastructure | | Use Case Description | With the deepening of digital transformation of the whole society and industry, cloud office has become more and more common. Cloud based office has the characteristics of resource on-demand, convenience and high mobility, and is favored by large and medium-sized enterprises. Cloud desktop is a specific implementation method. By centrally managing the office computing resources required by enterprise employees and adopting large-scale and digital methods, IT Capital Expenditure and Operating Expense(CAPEX & OPX) can be reduced, and production efficiency can be improved. Due to the presence of branches throughout the country and even globally, enterprises have requirements for both computing resource and network connectivity. Therefore, this scenario can be considered a typical scenario for computing force network.| @@ -124,7 +125,7 @@ Jianchao Guo (AsiaInfo), Jian Xu (China Mobile), Jie Nie (China Mobile), Jintao ## 4.2 AI-based Computer Force Network Traffic Control and Computer Force Matching |
Use Case Name
| AI-based Computer Force Network Traffic Control and Computer Force Matching | -|------------------------|-----------------| +|:----|:----| | Contributor | China Mobile Research Institute: Weisen Pan | | Application Name | AI-based Computer Force Network Traffic Control and Computer Force Matching | | Use Case Description | 1. Computer Force Network integrates distributed and ubiquitous computing capabilities in different geographic locations, and its sources include various computing devices such as cloud computing nodes, edge computing nodes, end devices, network devices, etc. The computing tasks in the CFN environment are large in volume and diverse in type, including data analysis, AI reasoning, graphics rendering, and other computing tasks. In this case, the traditional traffic control strategy may not be able to effectively handle the diversity and magnitude of tasks, which may lead to the waste of computing resources, delay of computing tasks, and degradation of service quality. To solve these problems, AI-based traffic control and computing force matching can be used to train AI models using deep learning algorithms by collecting a large amount of network traffic data, device state data, and task demand data. The model can not only learn the pattern of network traffic and computing tasks but also predict future traffic changes and task demands, as well as the computing capacity of devices, and adjust the traffic control strategy and arithmetic matching strategy in real-time based on this information.
2.With the help of AI, operators can manage traffic and computing force more effectively, reduce network congestion, improve the utilization of computing resources, reduce the latency of computing tasks, and improve the quality of service. For example, when a large number of data analysis tasks are predicted to be coming, AI systems can adjust network configurations in advance to prioritize allocating computing resources to these tasks to meet demand. When the capacity of computing devices is predicted to be insufficient to handle the upcoming tasks, the AI system can adjust the traffic control policy in advance to redirect some tasks to other devices to prevent congestion.
3. AI-based Computer Force Network traffic control and computer force matching bring significant performance improvements to large-scale CFN, enabling operators to manage computing resources better to meet the demands of various computing tasks.
![AI流量协同示意图](https://opendev.org/cfn/use-case-and-architecture/src/branch/master/figures/4.2%E5%9B%BE-AI-workload.png)| @@ -137,7 +138,7 @@ Jianchao Guo (AsiaInfo), Jian Xu (China Mobile), Jie Nie (China Mobile), Jintao ## 4.3 Computing Force Network scheduling for video service scenario |
Use Case Name
| Computing Force Network scheduling for video service scenario | -|------------------------|-----------------------------------------| +|:----|:----| | Contributor | Inspur - Geng Xiaoqiao | | Application Name |Integrated computing and network scheduling for video applications in rail transit scenario| | Use Case Description | Based on the intelligent video scene of rail transit and focusing on customer business, integrated computing and network scheduling capability is built. Perceiving and analyzing users' requirements on delay and cost, coordinately scheduling and allocating computing and storage resources, as well as realizing dynamic optimization of scheduling policies, so as to provide customers with on-demand, flexible and intelligent computing force network services. At the same time, it is widely adapted to various industry application scenarios, enabling intelligent transformation and innovation of video services. | @@ -148,7 +149,7 @@ Jianchao Guo (AsiaInfo), Jian Xu (China Mobile), Jie Nie (China Mobile), Jintao ## 4.4 Scheduling of Private Computing Service Based on Computing Force Network |
Use Case Name
| Scheduling of Private Computing Service Based on Computing Force Network | -|------------------------|-------| +|:----|:----| | Contributor | Asia Info-Guo Jianchao | | Application Name | Private Computing | | Use Case Description |When individuals/enterprises apply for loans from banks, banks need to assess lending risks and identify the risks of users borrowing excessively or excessively. By building a privacy computing platform, it is possible to  utilizes privacy queries and multi-party joint statistics, and collaborate with multiple banks to jointly calculate the total loan amount of each bank before lending. After receiving the joint statistical results, the bank decides whether to issue loans to users.| @@ -160,7 +161,7 @@ Jianchao Guo (AsiaInfo), Jian Xu (China Mobile), Jie Nie (China Mobile), Jintao ## 4.5 Cross-Architecture deployment and migration of AI Applications in CFN |
Use Case Name
| Cross-Architecture deployment and migration of AI Applications in CFN | -|------------------------|-------------------------------------------------------| +|:----|:----| | Contributor | China Mobile Research Institute – Qihui Zhao | | Application Name | AI applications | | Use Case Description | When users apply for AI services from computing force network, taking facial recognition as an example, they will provide facial recognition task type, data to be processed (such as image transmission interface, image quantity, image size), preferred detection location, processing speed, cost constraints, etc. The computing force network will choose a suitable facial recognition software and a computing force cluster to deploy facial recognition software, complete configuration, and provide services based on user needs.
Since the computing force network forms a unified network with computing force of different sources and types, the underlying computing force that used to carries the facial recognition service can be any one or more of Nvidia GPU, Intel CPU, Huawei NPU, Cambrian MLU, Haiguang DCU and many other intelligent chips. Therefore, the deployment, operation, and migration of AI applications on heterogeneous computing chips from multiple vendors is one of the typical use cases of computing force networks. This use case is similar to the AI application’s cross architecture migration scenario in cloud computing.
In addition to users applying for AI services from computing force network, the above use case is also suitable for users to deploy self-developed AI applications into computing force network. | @@ -171,7 +172,7 @@ Jianchao Guo (AsiaInfo), Jian Xu (China Mobile), Jie Nie (China Mobile), Jintao ## 4.6 CFN Elastic bare metal |
Use Case Name
| CFN Elastic bare metal | -|------------------------|--------------------------------------------| +|:----|:----| | Contributor | China Mobile-Jintao Wang、China Mobile-Qihui Zhao | | Application Name | / | | Use Case Description | According to Section 2.3, the CFN provides a resource-based service model, allowing users to directly apply for resources such as bare metal, virtual machines, and containers to the CFN. Some users are also more inclined to use bare metal services due to considerations such as performance, security, and the difficulty of business virtualization/containerization transformation. Traditional bare metal service resources are difficult to flexibly configure. Customers need to adapt to various drivers. The distribution and management process is lengthy. Whether it is customer experience or management and operation methods, there is a big gap compared with virtualization services. Therefore, how to realize the flexible management and operation and maintenance of various infrastructures by CFN providers, and simplify the use of bare metal by users is an important requirement of the CFN data center. This scenario is also applicable to the fields of cloud computing and cloud-network integration. | diff --git a/figures/3.图-CFN-Technology-Topo-en.png b/figures/3.图-CFN-Technology-Topo-en.png new file mode 100644 index 0000000..a0bceae Binary files /dev/null and b/figures/3.图-CFN-Technology-Topo-en.png differ diff --git a/figures/4.1图-VDI-en.png b/figures/4.1图-VDI-en.png new file mode 100644 index 0000000..e0b1d6d Binary files /dev/null and b/figures/4.1图-VDI-en.png differ diff --git a/figures/4.2图-AI-workload-en.png b/figures/4.2图-AI-workload-en.png new file mode 100644 index 0000000..4f8aa0b Binary files /dev/null and b/figures/4.2图-AI-workload-en.png differ diff --git a/figures/4.3图-算力网络一体化调度-en.png b/figures/4.3图-算力网络一体化调度-en.png new file mode 100644 index 0000000..0aac8e0 Binary files /dev/null and b/figures/4.3图-算力网络一体化调度-en.png differ diff --git a/figures/4.3图-视频网络图-en.png b/figures/4.3图-视频网络图-en.png new file mode 100644 index 0000000..dd28bb4 Binary files /dev/null and b/figures/4.3图-视频网络图-en.png differ diff --git a/figures/4.4图-隐私计算-workflow-en.png b/figures/4.4图-隐私计算-workflow-en.png new file mode 100644 index 0000000..fa052ab Binary files /dev/null and b/figures/4.4图-隐私计算-workflow-en.png differ diff --git a/figures/4.5-跨架构工作流程-en.png b/figures/4.5-跨架构工作流程-en.png new file mode 100644 index 0000000..871242f Binary files /dev/null and b/figures/4.5-跨架构工作流程-en.png differ diff --git a/figures/4.5图-加速硬件生态壁垒-en.png b/figures/4.5图-加速硬件生态壁垒-en.png new file mode 100644 index 0000000..f13469e Binary files /dev/null and b/figures/4.5图-加速硬件生态壁垒-en.png differ diff --git a/figures/4.5图-算力原生逻辑方案-en.png b/figures/4.5图-算力原生逻辑方案-en.png new file mode 100644 index 0000000..22c997a Binary files /dev/null and b/figures/4.5图-算力原生逻辑方案-en.png differ diff --git a/figures/4.6图-DPU裸机管理方案-en.png b/figures/4.6图-DPU裸机管理方案-en.png new file mode 100644 index 0000000..0b4d125 Binary files /dev/null and b/figures/4.6图-DPU裸机管理方案-en.png differ diff --git a/figures/4.6图-传统裸机管理方案-en.png b/figures/4.6图-传统裸机管理方案-en.png new file mode 100644 index 0000000..c45e73b Binary files /dev/null and b/figures/4.6图-传统裸机管理方案-en.png differ