capturing data from the edge of the network, we can perform both primitive and com- plex data processing ranging from data aggregation to advanced ML and predictive analytics. For example, by collecting performance and application data from virtual machines supporting virtual firewalls and routers, we can run advanced analytics at the edge to perform closed-loop automation. This may involve automatic identification of cyber-attacks and taking actions to terminate. Those actions may include shifting traffic to new virtual machines, deactivating IP addresses, or rebooting those machines (see Chapter 9). In the Network Cloud, virtualized functions are expected to be instantiated in a significantly dynamic manner that requires the ability to provide real-time responses to actionable events from virtualized resources to applications and services. The big data platform is expected to gather key performance usage, telemetry, and events to compute various analytics and respond with appropri- ate actions based on observed anomalies or significant events. These significant events sometimes referred to as signatures, enable applications to dynamically perform resource scaling, configu- ration changes, traffic optimization, and detection of faults and performance degradation. Open DCAE enables collection and analysis of data for supporting such control systems at the edge of the network at a centralized location. 8.3.2 DCAE FUNCTIONAL COMPONENTS 8.3.2.1 Common Collection Framework The collection framework supports virtualized instrumentation for gathering data. The scope of the data collection would include all the physical and virtual elements (compute, storage, and network) and the management control functions in the cloud infrastructure. The collection data would include the types of events data necessary to monitor the health of the managed environment, the types of data to compute the key performance and capacity indicators necessary for elastic management of the resources, the types of granular data (e.g., flow, sessio