A new generation of end-user applications portends to not only drive continued demand for increased capacity, but also to create formidable operational challenges resulting from rapid and unprecedented shifts in traffic patterns.Application of cognitive theory in learning therefore, we hear so much in the industry today about the importance of network automation. As fiber is pushed deeper into the network and becomes increasingly critical to emerging services such as the internet of things (iot), 5G and augmented and virtual reality (AR/VR), what will it take to transform the underlying transport infrastructure to meet these challenges?Application of cognitive theory in learning what innovative solutions are required to evolve the network beyond simple automation to become a truly cognitive network? Figure 1: changes in traffic patterns and network complexity drive the need for network automation

application of cognitive theory in learning

The cognitive network is a multi-layer, self-aware, self-organizing and self-healing infrastructure that can take predictive and/or prescriptive action based on real-time knowledge gleaned from collected data and experience.Application of cognitive theory in learning while no network can completely plan or run itself, the cognitive network will dramatically reduce the number of manual tasks required across a multi-layer, multi-domain, multi-vendor network.Application of cognitive theory in learning there are three main building blocks of a typical cognitive network:

CenturyLink and infinera recently joined forces to showcase innovation toward building a true cognitive network and to demonstrate data science use cases within the metro ethernet forum (MEF) lifecycle service orchestration (LSO) architecture.Application of cognitive theory in learning featured at the recent MEF18 and SC18 industry conferences, this proof of concept (poc) marked a key step toward advancing open application programming interfaces (apis) and microservices-based application development critical to the implementation of data science solutions in a new era of communications.Application of cognitive theory in learning

This centurylink and infinera poc demonstration featured automatic reconfiguration of the network (e.G., dynamic ethernet virtual connection creation) based on intelligent decision criteria, which was supported by well-defined and configured policy engine rules and supplemented with time-series telemetry information.Application of cognitive theory in learning it also highlighted the integration of various third-party toolkits that can be easily integrated into the MEF LSO ecosystem in alignment with MEF’s third network vision, resulting in practical realization of the MEF standards work.Application of cognitive theory in learning

The poc will help advance standards activity in the areas of artificial intelligence and machine learning while integrating the MEF LSO apis toward intelligence-based automation, which represents one of the key building blocks of the cognitive network.Application of cognitive theory in learning the poc also illustrated a means to advance the user experience in the devops area by promoting more open apis, ensuring the fast and agile introduction of features and using a generic policy engine and actions.Application of cognitive theory in learning figure 3: centurylink and infinera poc implementation of data science applications for the MEF LSO architecture

• data is the cornerstone. It is extremely critical to stream the “value-added” KPI data at a decent frequency interval with time stamping.Application of cognitive theory in learning the streaming of KPI data needs to be based on flexibility (policy-based) such that the kpis being watched will result in deterministic inference in the learning process with very high confidence levels.Application of cognitive theory in learning for multi-layer, multi-vendor deployments, kpis need to be standardized at all layers of the network. This ensures that all operators (service providers and customers) can adopt the same methodologies and terminology for the kpis.Application of cognitive theory in learning

• A fine mix of machine learning based on the large data sample, together with intelligent domain knowledge expertise, results in solving critical use cases.Application of cognitive theory in learning the AI/ML provides two functions. The ML part uses data samples to arrive at an inference. The AI part uses the inference and domain knowledge (intelligence) to accomplish a given use case by orchestrating the network configuration directly or via the policy-based action.Application of cognitive theory in learning in either case, a closed loop is achieved without human intervention. Please note that this architecture can also encompass the basic use cases in which closed loop automation is achieved by simply monitoring any attribute of the sensor device.Application of cognitive theory in learning in such scenarios, the ML part can be skipped while the AI part still provides the required intelligent piece of functionality.

• performance, reliability, scalability and agility.Application of cognitive theory in learning the AI/ML intelligent engine can be installed in the cloud. Depending on the number of sensors being monitored from many network elements, the central and graphics processing units, memory and data store can be adjusted to support performance and scale.Application of cognitive theory in learning the high availability requirement is also met by deploying the AI/ML in multi-core redundant instances. It is important to avoid false positives while inference is made.Application of cognitive theory in learning proper hysteresis is built into the ML algorithms to avoid transient inferences. The robust handling of time-series kpis so that they arrive at reliable inferences is key to a highly dependable cognitive-based automated network.Application of cognitive theory in learning it is also apparent that the amount of usable open source solutions can be easily leveraged to build applications at a faster pace.

Cognitive networking is the result of seamless and highly dynamic interaction between software and hardware assets across network layers and brings networking to a new level of scalability, flexibility and automation.Application of cognitive theory in learning the path toward a true cognitive network will pave the way for innovations in service delivery that will redefine next-generation communications services for a wide variety of users and verticals, including banking and finance, government and education.Application of cognitive theory in learning