APPLICATIONS ON TOP OF DNA CENTER: AUTOMATIC CONFIGURATION VALIDATION BASED ON TOPOLOGY FOR BASIC INTEROPERATION

A rule based service is provided for Configuration Simulation and Validation (CSV) for the devices managed on given network. With an understanding of the architecture of a network, the service can be employed to determine which devices need to be in the same Internet Protocol (IP) subnet. The service can be employed to suggest one or more configuration changes to the administrator such as, changes to address as a potential misconfiguration, improve operation, etc

APPLICATIONS ON TOP OF DNA CENTER: TOPOLOGY EVOLUTION BASED ON CUSTOMER NETWORK DYNAMICS

Techniques are provided for detecting and managing dynamically changing loads in a network, for example, during large scale events that may cause a bottleneck such as a stadium game event or a musical concert. These techniques allow network administrators to improve network efficiency by monitoring and tweaking the network capabilities in order to handle network load during periods of high demand without a detailed background knowledge of the network

MACHINE-LEARNING OF LEADING INDICATORS FROM A PLURALITY OF SERVICES TO PERFORM MEMORY MANAGEMENT ON ANOTHER PLURALITY OF SERVICES WITH CORRELATING MEMORY USAGE PROFILE

Presented herein are techniques for machine-learning of leading indicators from a plurality of services and the use of these leading indicators to perform memory management operations on another plurality of services with correlating memory usage profiles. The techniques presented herein include collecting, as leading indicators, service event logs from a plurality of services. Machine-learning is then used to cross-correlate the leading indicators with memory use pattern snapshots at subsequent times for another plurality of services to predict the optimal when, how much, and how for execution of memory garbage collection on the latter set of services

WIRELESS NETWORK LOAD CORROBORATION USING MACHINE LEARNING (ML) BASED VIDEO ANALYTICS

Presented herein are techniques for correlating the output of a crowd counting machine learning (ML) algorithm, which operates on surveillance video, with observed network load to determine if a load spike is due to a valid network usage or an attacker trying to sabotage the network. The techniques presented herein include vision field classification based on access point (AP) coverage, linking of vision fields to AP coverage in DNAC UI, and consensus-based threat assessment and alerts

OUTCOME OF MACHINE REASONING IN A NETWORK MANAGEMENT SYSTEM TOPOLOGY VIEW

A technique is described herein to provide a visualization overlaid on a network topology that illustrates the cascading impact of a network event before it happens. The technique may empower a network administrator to perform one or more steps to mitigate the issue and/or minimize its impact before the issue manifests itself into a critical network condition