us Reactive versus Predictive versus Prescriptive Analytics 8.2.5.2 Supervised versus Unsupervised Learning 8.2.5.3 Deep Learning 8.2.5.4 Open Source Distributed Processing Toolkits for ML Big Data Meets Network Cloud 8.3.1 Data Collection, Analytics, and Events 8.3.2 DCAE Functional Components 8.3.2.1 Common Collection Framework 8.3.2.2 Data Movement 8.3.2.3 Edge, Central, and Core Lakes 8.3.2.4 Analytics Framework 8.3.3 MS Design Paradigm 8.3.4 Control-Loop Automation 8.3.4.1 Closed-Loop Modeling and Template Design 8.3.4.2 CLAMP System Architecture 8.3.5 Machine Learning for Closed-Loop Automation 8.3.6 Deep Learning and SDN Network Data Applications 8.4.1 Self-Optimizing Networks 8.4.2 A Customer Configurable Policy for a Content Filtering Smart Network 8.4.3 Traffic Shaping Based on Historical and Current Estimated Network Congestion 8.4.4 Utilizing SDN to Minimize Robocalling Building the Network of the Future 8.4.5 Emerging Applications of SDN and NFV Including IoT, M2X, Ambient Video, Virtual Reality, Security, and Intelligent Agents References Collecting and analyzing network event data can be a daunting processing challenge for today's larger networks. For example, AT&T's network in 2016 passes more than 118 petabytes (PB) per day; if each byte was to be treated as an "event," it would imply a need to process, analyze, and act on approximately 1.4 trillion events per second. This sort of scale implies a need for a big data framework for event processing. This chapter describes some of the concepts for such data collec- tion and analytics. NETWORK DATA AND THE ANALYTICS LAYER Having the ability to collect and analyze data from various points in the "end to end" dataflow is fundamental to being able to diagnose and optimize networks. While SDN separates the control plane and data plane, it is important to add an "instrumentation" layer to be able to collect this data as depicted in Figure 8.1. Notice that this "data instrumentation layer" can collect data from the modules (endpoints), the virtualized netw