Abstract: Systems and methods for managing routing involving an indicated point of interest associated with a plurality of levels of a multilevel (overlapping or stacked) roadway are provided. In example embodiments, a networked system aggregates trip data received from user devices that includes location information and detected attributes for points of interest. The networked system analyzes the location information and the detected attributes to determine a height parameter and, in some cases, a characteristic associated with different levels of the multilevel roadway for points of interest. The height parameters and characteristics for each point of interest are stored to a database in a data storage. During runtime, the networked system receives a request that includes a point of interest. In response, the networked system detects a level of roadway at the point of interest using the database and real-time device data. Based on the detected level of the multilevel roadway, a route is generated and presented.

Abstract: Systems and methods for managing routing involving an indicated point of interest associated with a plurality of levels of a multilevel (overlapping or stacked) roadway are provided. In example embodiments, a networked system aggregates trip data received from user devices that includes location information and detected attributes for points of interest. The networked system analyzes the location information and the detected attributes to determine a height parameter and, in some cases, a characteristic associated with different levels of the multilevel roadway for points of interest. The height parameters and characteristics for each point of interest are stored to a database in a data storage. During runtime, the networked system receives a request that includes a point of interest. In response, the networked system detects a level of roadway at the point of interest using the database and real-time device data. Based on the detected level of the multilevel roadway, a route is generated and presented.

Abstract: Systems and methods for managing routing involving an indicated point of interest associated with a plurality of levels of a multilevel (overlapping or stacked) roadway are provided. In example embodiments, a networked system aggregates trip data received from user devices that includes location information and detected attributes for points of interest. The networked system analyzes the location information and the detected attributes to determine a height parameter and, in some cases, a characteristic associated with different levels of the multilevel roadway for points of interest. The height parameters and characteristics for each point of interest are stored to a database in a data storage. During runtime, the networked system receives a request that includes a point of interest. In response, the networked system detects a level of roadway at the point of interest using the database and real-time device data. Based on the detected level of the multilevel roadway, a route is generated and presented.

Abstract: A method includes collecting, at a monitoring and recovery node, a virtual network function key performance index data through multiple channels from a corresponding containerized virtual network function. The method includes maintaining, at the monitoring and recovery node, state information of the corresponding containerized virtual network function and running, at the monitoring and recovery node, a machine learning algorithm that, once trained, learns and predicts whether the corresponding containerized virtual network function requires one of a scaling, a healing or a context switching to sister virtual network function to yield a determination and meet the service level agreement of a network service.

Abstract: A method includes collecting, at a monitoring and recovery node, a virtual network function key performance index data through multiple channels from a corresponding containerized virtual network function. The method includes maintaining, at the monitoring and recovery node, state information of the corresponding containerized virtual network function and running, at the monitoring and recovery node, a machine learning algorithm that, once trained, learns and predicts whether the corresponding containerized virtual network function requires one of a scaling, a healing or a context switching to sister virtual network function to yield a determination and meet the service level agreement of a network service.