Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing dynamic request queues to process electronic requests in a shared infrastructure environment. The disclosed system dynamically generates a plurality of separate request queues for tenant computing systems that utilize a shared processing infrastructure to issue electronic requests for processing by various recipient processors (e.g., one or more processing threads) by separating a primary request queue into the separate requests queues based on the tenant computing systems. The disclosed system also generates a plurality of queue order scores for the request queues based in part on a processing recency of each of the request queues and whether the request queues have pending electronic requests. The disclosed system processes electronic requests in the request queues by selecting a request queue based on the queue order scores and processing a batch of electronic requests utilizing a recipient processor.

Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing dynamic request queues to process electronic requests in a shared infrastructure environment. The disclosed system dynamically generates a plurality of separate request queues for tenant computing systems that utilize a shared processing infrastructure to issue electronic requests for processing by various recipient processors (e.g., one or more processing threads) by separating a primary request queue into the separate requests queues based on the tenant computing systems. The disclosed system also generates a plurality of queue order scores for the request queues based in part on a processing recency of each of the request queues and whether the request queues have pending electronic requests. The disclosed system processes electronic requests in the request queues by selecting a request queue based on the queue order scores and processing a batch of electronic requests utilizing a recipient processor.

A resource-access management system detects whether a user is authorized to access resources. The system may include a user device being configured to include a sensor that detects sensor data associated with the user. Further, the system includes a client qualification engine that determines whether or not a client is authorized to access the resources by comparing the sensor data with a plurality of patterns for evaluating whether or not the user is an authorized user. User scores are generated based on the compared sensor data and the plurality of patterns. Further, a composite score corresponding to the user is generated using the sensor data, plurality of patterns, and one or more additional criteria. Whether the user is granted access to the resources, presented with unauthorized user tests, or blocked from access to the resources depends on the composite score and threshold values.

A resource-access management system detects whether a user is authorized to access resources. The system may include a user device being configured to include a sensor that detects sensor data associated with the user. Further, the system includes a client qualification engine that determines whether or not a client is authorized to access the resources by comparing the sensor data with a plurality of patterns for evaluating whether or not the user is an authorized user. User scores are generated based on the compared sensor data and the plurality of patterns. Further, a composite score corresponding to the user is generated using the sensor data, plurality of patterns, and one or more additional criteria. Whether the user is granted access to the resources, presented with unauthorized user tests, or blocked from access to the resources depends on the composite score and threshold values.

A system and method for autonomous data and signalling traffic management in a distributed infrastructure is disclosed. The method includes a distributed multi-cloud computing system with machine learning based intelligence across heterogenous computing platforms hosting mobile network functions, capable of leveraging AI based distribution across all the resources to creates autonomous network operations and intelligently work around any impairments. The method includes determining one or more service nodes by using a trained traffic management based ML model and establishing one or more cloud mesh links between the one or more service nodes at multiple levels of hierarchy based on the system, environment and network parameters and the current network demand. Further, the method includes processing the request by providing access of the one or more services hosted on the one or more external devices to the one or more electronic devices via the one or more cloud mesh links.

A system and method for autonomous data and signalling traffic management in a distributed infrastructure is disclosed. The method includes a distributed multi-cloud computing system with machine learning based intelligence across heterogenous computing platforms hosting mobile network functions, capable of leveraging AI based distribution across all the resources to creates autonomous network operations and intelligently work around any impairments. The method includes determining one or more service nodes by using a trained traffic management based ML model and establishing one or more cloud mesh links between the one or more service nodes at multiple levels of hierarchy based on the system, environment and network parameters and the current network demand. Further, the method includes processing the request by providing access of the one or more services hosted on the one or more external devices to the one or more electronic devices via the one or more cloud mesh links.

The present disclosure relates to an SDN-based VPN traffic scheduling method and scheduling system. The method includes: configuring an SDN switching device to implement establishment of a communication link between a CE device and a PE device; performing VPN configuration on a controller; distributing, by the controller, a corresponding flow table to the SDN switching device, the flow table being used to translate a repeated address within a VPN to a non-conflict space address to distinguish different VPN traffic; configuring, by the controller, different traffic scheduling paths for the different VPN traffic according to a preset traffic scheduling strategy; and distributing, by the controller, the traffic scheduling paths to the PE device.

The present disclosure relates to an SDN-based VPN traffic scheduling method and scheduling system. The method includes: configuring an SDN switching device to implement establishment of a communication link between a CE device and a PE device; performing VPN configuration on a controller; distributing, by the controller, a corresponding flow table to the SDN switching device, the flow table being used to translate a repeated address within a VPN to a non-conflict space address to distinguish different VPN traffic; configuring, by the controller, different traffic scheduling paths for the different VPN traffic according to a preset traffic scheduling strategy; and distributing, by the controller, the traffic scheduling paths to the PE device.

A traffic scheduling method includes determining, by a first network device, first traffic scheduling information and a transmission path of a first data stream based on a first talker attribute message received from a talker device and a listener attribute message received from a listener device, and then sending, by the first network device, a first traffic scheduling message to a network device on the transmission path. The first traffic scheduling message includes the first traffic scheduling information. The first traffic scheduling information indicates the network device on the transmission path to generate a gate control list. The gate control list indicates the network device on the transmission path to control, based on the gate control list, a state of a port used to transmit the first data stream.

A resource-access management system detects whether a user is authorized to access resources. The system may include a user device being configured to include a sensor that detects sensor data associated with the user. Further, the system includes a client qualification engine that determines whether or not a client is authorized to access the resources by comparing the sensor data with a plurality of patterns for evaluating whether or not the user is an authorized user. User scores are generated based on the compared sensor data and the plurality of patterns. Further, a composite score corresponding to the user is generated using the sensor data, plurality of patterns, and one or more additional criteria. Whether the user is granted access to the resources, presented with unauthorized user tests, or blocked from access to the resources depends on the composite score and threshold values.