ed to as "Open DCAE," adopts an open source philosophy. Shown Figure 7.8, it fosters a data marketplace and a set of micro-service-based collectors and analytics functions. With this approach, a secure ecosystem is created where developers, third-party pro- viders, data scientists, and engineers can contribute to the platform. The platform also supports analytics, collections, policy enablement, compute, and storage in three places: at the edge, in the The Network Operating System Edge Processing Central Processing Core Processing Edge Lakes Central Lakes Core Lakes Analytics Analytics Analytics Collections Collections Collections Policy Policy Policy Edge Storage CentralStorage Core Storage Distribution Streaming analytics Closed loop control Batch analytics Data collection & filtering Priority open loop control Testing micro-services environment FIGURE 7.8 Open DCAE and control loop automation. central sites (where there are regional cloud zones), and in the core (where batch-mode processing is enabled) of the network. Compute, data distribution, and storage resources can be optimized to achieve desirable scale, performance, reliability, and cost efficiencies. Open DCAE enables a new generation of intelligent services. By capturing data from the edge of the network cloud, data can be analyzed securely and efficiently to identify and eliminate poten- tial security threats. By taking data from VMs supporting virtual firewalls and routers, machine learning and advanced analytics can be applied to perform closed-loop automation (the automatic identification and resolution of failures). Those actions may include shifting traffic to new VMs or rebooting those machines during off-peak times. There are some key technical challenges with Open DCAE development: 1. Scalability and movement in processing and management of heterogeneous and large-scale data, from structured to unstructured, and messages to events and files. 2. Development, certification, and automated onboarding of micro-services. These functions are typica