 and performs the execution. In the case of a virtual function reboot, the service controller will communicate through a standardized protocol (such as OpenStack) and completes the action. It will also publish feedback information to the data bus to describe whether the action is a success or not. In the case of failure, subsequent recovery pro- cedures may be performed automatically or manually per service design. If automation ultimately fails, then a ticket is issued to the appropriate operations center to do a manual override. Building the Network of the Future Ops user Svc planner CLAMP Cockpit Control Loop Monitoring Work flow engine Data Bus Policy FIGURE 8.14 CLAMP execution engine. (From Gilbert et al. 2016. Control loop automation management platform, IEEE GlobalSip 2016.) As described above, CLAMP supports a model-driven approach in which service design is encapsulated as a data model and distributed to ONAP. This enables rapid development and scal- ability of closed-loop services. 8.3.5 MACHINE LEARNING FOR CLOSED-LOOP AUTOMATION Although there are numerous publications on the use of ML technologies for multimodal and mul- timedia applications [Mit97], there is far less research being done on the application of ML and AI for software-defined network. Some of the key opportunities we have identified include 1. Self-optimizing networks (SONs) 2. Cybersecurity and threat analytics 3. Fault management 4. Customer experience improvement 5. Traffic optimization We will describe these applications in a little more detail in the next section. What is common across those applications is that ML and AI help to provide more automation by leveraging the closed-loop automation paradigm we described earlier. Rather than performing reactive analytics, which is typically employed in unsupervised learning models, ML helps to predict anomalies or events well in advance, based on time-varying signals. Similarly, instead of applying hard-wired policies, or rules, to guide the system on what best next action to take, AI c