Blueprint Overview 

CFN, defined as computing force network, is a new information infrastructure that takes computing as the center, and network as the foundation, and deeply integrates Network, Cloud, Big Data, Artificial Intelligence, Security, Edge, Terminal and Blockchain to provide integrated services. 

According to the unified ubiquitous scheduling management layer, the computing force of public cloud, on premise, edge cloud, or external third parties is managed to achieve consistent cluster, strategy, configuration, and traffic management, and to achieve resource-level and task-level scheduling. 

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In this blueprint,

Business Drivers

Computing Force Network（CFN）puts forward higher requirements for the management and collaboration ability of ubiquitous computing power. Ubiquitous computing power scheduling technology involves cross-layer scheduling of cloud, edge, and end multi-level computing power, and it needs to meet the scene requirements of various heterogeneous computing power management and multi-party social computing power access. At present, the core management framework, key technical solutions, and products of ubiquitous computing scheduling technology are gradually developing, and have not yet entered the mature stage of technology.

Overall Architecture

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Control plane: one k8s cluster is deployed in private lab.

Traffic plane: two K8s clusters are deployed in private lab.

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Road map:

(already done) 1、业务多集群分分发部署：Scheduling computing force by cluster weight. already release in R7;

(already done) 2、容灾场景下的业务部署调度：Rescheduling computing force when a cluster resource is abnormal. already release in R7;

(next step) 3. Designing of integrated computing and network resource scheduling algorithm (next step, envisioned Signpost) 基于算网状态的联合调度算法

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In the future stage, on the one hand, when more nodes are added to the experiment environment, the complexity of network topologies will emerge. Thus, the computing task scheduling results without timely and sufficient information on the network will probably impact the performance of the application. Integrated computing and network resource sensing will be of first importance, which forms the basis for making resonable scheduling decisions. On the other, as for applications, when micro-service architectured or cloud-native applications are introduced in the experiment. The sequential dependency between microservices components in the application should be carefully considered when planning the distributive deployment, adding more complexity to the task scheduling problem. The rapid development of graph learning makes feature extraction from graphic data about networked infrastructure and componentized applications fast and easy, greatly facilitating the training of deep learning models and reinforcement learning agents. Thus, artificial intelligence techniques-based task and resource scheduling approaches are considered in the future stage of the CFN BP.

(next steo) 4. Scheduling algorithm enhancement considering higher-level factors.（next step, envisioned Signpost）基于成本、资源利用率的调度算法增强

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