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Machine Learning and Big Data Analytics

A technique used for network analytics

Machine learning helps extract hidden network insights

Machine learning refers to the ability for machines to learn without being explicitly programmed. A branch of AI, it focuses on recognition of data patterns or trends, enabling software programs to take actions based on them. It also involves processes like data classification and clustering that deal with the complexity of disparate data sources.

Unlike traditional analysis, machine learning thrives on growing datasets. The more ‘good’ data fed into a machine learning system, the more it can learn and apply the results to higher quality insights. In networking, maching learning involves big data analytics and can provide the following types of data-driven insights:

  • Descriptive: Interprets historical data to determine what has happened
  • Diagnostic: Determines why something has happened using techniques such as data mining, discovery, and correlation for a root-cause analysis
  • Predictive: Predicts what will happen in the future based on historical patterns that are sometimes combined with external data
  • Prescriptive: Predicts multiple outcomes for a given scenario based upon actions that are taken. The idea is to show how a different set of actions will affect the situation and steering the user toward the best possible option

For network operators, machine learning and big data analytics can address a wide range of business and operational needs. For example, predictive analytics based on machine learning can help ensure service reliability by keeping the network’s health at its optimum state. Analytics can learn from historical performance metrics and trouble ticketing data and then combine that knowledge with real-time network telemetry to anticipate future network failures. This enables operators to take proactive action on the network to prevent failures from actually happening. Analytics can also be used to optimize service performance (such as video) or ensure that the network’s capacity is best aligned with service demand.

Machine learning benefits from Blue Planet Unified Assurance and Analytics

Blue Planet® Unified Assurance and Analytics (UAA) is a comprehensive software suite that utilizes a holistic approach to ensure network resiliency and service QoE. It uses machine learning to continuously assess the changing conditions of the network and applies new actions to keep the network in its optimum state for meeting service requirements while aligning with operational and business goals. This machine-learning-driven application of new actions on the network is what we refer to as ‘closed-loop automation’. It’s a continuous process—and it’s the heart of the Adaptive Network™.

UAA is based on an open framework and allows for quick and easy integration of multiple data sources, whether physical or virtual. Integration with cluster-based storage systems allows for scalable datasets and efficient machine learning. Its microservices-based architecture provides modularity and scale to enable rapid adoption of data-driven automation.

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