.h2o.ai/ https://deeplearning4j. apache.org/ org/mllib/ 2. Creating reusable platform resources (such as data collectors and analytics capabilities) as plug-n-play components. These components, which are referred to as microservices (MSs), are stand-alone, model-driven and policy-enabled applications. (A deeper dive into MSs is provided later in this chapter.) 3. Allowing resources to be automatically configured and activated/deactivated to optimize performance and minimize cost. Network Data and Optimization ECOMP DCAE Analytic Applications Interfaces Real-time Analytic Applications Other Applications Scaling Anomalies Other Capacity Signatures Testing Other Controller Health Customized Analytics Reusable Analytic Framework / Library Start/Stop applets/functions/queries Cross-domain, domain-specific applets/functions/queries Reports Scaling Policy DCAE Platform Analytic Framework Unstructured & Structured Data Persistence Platform Stream Operators, DAG Operators, DAGs, Baseline, Inventory, Trends Policies, Topology On-boarding Application Data Movement Calls Sessions Event, Message & Data Routing Edge/Core Events Other Library Stream Data Collection Batch Data Collection Guides Events Flows Usage Other Counters Files Other Certification AIC INFRASTRUCTURE/VM/VR INSTRUMENTATION 2014 AT&T Proprietary Use Pursuant To Company Instructions NFVi/VF Elements FIGURE 8.9 A platform approach to analytics applications in Network Cloud. 4. Orchestrating a model-driven service design dynamically. 5. Employing standardized data models to enable service chaining. Figure 8.9 shows a high-level architecture of a big data platform supporting the Network Cloud. There are essentially four basic functionalities in the data platform: 1. Analytics framework-a data development and processing platform with a catalog of micro-services that can support structured and unstructured data processing. 2. Collection framework-a set of streaming and batch collectors that support virtualized network, device and infrastructure data. 3. Data distri