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　　应用程序的放置和动态流量路由是自动化服务生命周期管理提供的关键区别。实现布局的关键因素是拓扑感知编排，其中包含有关如何将 5G 连接连接到企业和消费者应用程序的知识。增强的拓扑发现和统一的拓扑模型是编排最重要的功能。

　　智能工作负载放置 (I-WLP) 服务具有四个关键特性。首先，它必须由 CSP 的业务意图驱动。其次，它必须尊重组件的启动状态和可用性、SLA 目标以及资源和硬件可用性。第三，它必须能够在不断变化的环境下工作，包括入职变更、软件升级、新资源和能力、业务意图或 CSP 运营策略的变化，以及 HCP 云位置的成本和可用性的变化。，它需要了解分布式系统的运行状态——即，哪些服务实例在哪里运行，具有哪些依赖关系。库存可以提供服务的运行、计划和待定保留状态，

　　从算法的角度来看，I-WLP 是一个多约束、多目标的图分配问题，不能在多项式时间内求解。因此，我们创建了基于贪婪启发式的方法，这些方法在支持服务约束（例如延迟界限、QoS 类别、供应商偏好、位置和反亲和性）方面非常灵活。例如，他们还可以考虑与成本、利用率、性能和弹性优化相关的不同运营策略的组合。

　　所有资源、服务和能力的清单是 I-WLP 的核心。网络和云都被构造成若干层，每一层代表上层的服务、资源和能力。I-WLP 包含适用于 CSP 产品的每一层的模型。每一层都在堆栈中进行生命周期管理，这意味着如果较高层需要重新配置较低层，则重新配置步骤包含在与技术无关的归位和分配建议中。例如，如果一项服务需要两个数据中心之间的额外带宽，则可以重新配置两个数据中心之间的 VPN 连接以支持这些需求。

　　在服务实例设计期间，服务编排器也会调用 I-WLP，从而产生由具有各种南向适配器的工作流引擎处理的归位和分配建议。

　　I-WLP 将 HCP 域集成到 CSP 资源和能力池中。它可以设计和分配网络服务和应用程序以进行协同定位，例如双栈部署。由于 I-WLP 自动执行归位和分配过程，因此不需要手动归位和分配所需的信息。这使得将曝光、API 和与消费者的互动保持在更高水平成为可能。基于意图的服务编排，其中归位和分配源自服务级别要求和约束，是一个很好的例子。

　　展望未来，我们正在研究如何将基于意图的操作 [2] 扩展到整个堆栈，以及如何为网络计算结构 [3] 提供 I-WLP。

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　　Optimal placement of applications and dynamic traffic routing are key differentiators that automated service life-cycle management provides. The key enabler for the optimal placement is topology-aware orchestration, where the knowledge about how to connect 5G connectivity to enterprise and consumer applications resides. Enhanced topology discovery and unified topology models are the most significant capabilities of the orchestration.

　　An intelligent workload placement (I-WLP) service is characterized by four key features. First and foremost, it must be driven by the CSP’s business intents. Secondly, it must respect the onboarding status and availability, SLA objectives, and resource and hardware availability of the components. Thirdly, it must be able to work under continuously changing circumstances, including onboarding changes, software upgrades, new resources and capabilities, changes to the business intents or CSP operational policies, and changes to the costs and availabilities of HCP cloud locations. And lastly, it needs to have knowledge of the operational state of the distributed system – that is, which service instances are running where with what dependencies. Inventories can provide running, planned and pending reservation states of services, while monitoring systems can provide actual and historical resource usage and SLA metrics.

　　From the algorithm perspective, I-WLP is a multi-constraint, multi-objective graph allocation problem, which cannot be solved in polynomial time. Therefore, we have created methods based on greedy heuristics, which are very flexible in terms of supporting service constraints such as latency bounds, QoS classes, vendor preferences, locations and anti-affinities. They can also consider combinations of different operational policies relating to cost, utilization, performance and resilience optimizations, for example.

　　An inventory of all resources, services and capabilities is central to I-WLP. Both the network and the cloud are structured into several layers, each of which represents services, resources and capabilities to the layers above. I-WLP incorporates models for every layer of the CSP’s offering. Each layer is life-cycle-managed in the stack, which means that if a higher layer requires reconfiguration of a lower layer the reconfiguration steps are included in a technology-agnostic homing and assignment recommendation. For example, if a service requires additional bandwidth between two datacenters then a VPN connection between the two datacenters can be reconfigured to support those needs.

　　I-WLP is also invoked by the service orchestrator during service instance design, resulting in a homing and assignment recommendation that is processed by a workflow engine with various southbound adapters.

　　I-WLP integrates HCP domains into CSP resource and capability pools. It can design and assign network services and applications for co-location, such as dual-stack deployments. Since I-WLP automates the homing and assignment process, the information that would be required for manual homing and assignment is unnecessary. This makes it possible to keep the exposure, APIs and interaction with consumers at a higher level. Intent-based service orchestration, where homing and assignment is derived from service-level requirements and constraints, is an excellent example of this.

　　Looking ahead, we are investigating how to extend intent-based operation [2] to the full stack and how to provide I-WLP for the network compute fabric [3].