Technologies for scheduling time-sensitive cyclical network traffic in real-time include an internet-of-things (IoT) device that includes at least one sensor for collecting sensor data. The IoT device is configured to store the collected sensor data in a data buffer, allocate a packet descriptor for the sensor data, and populate the allocated packet descriptor with a cyclic data port pointer indicative of a location of the data buffer. The IoT device is additionally configured to queue the packet descriptor into a media access control (MAC) unit transmit direct memory access (DMA) of the IoT device, fetch the sensor data, and packetize the fetched data to form a network packet. Further, the IoT device is configured to transmit the network packet to a target computing device based on a launch time, update the launch time, and requeue the packet descriptor into the MAC unit transmit DMA. Other embodiments are described herein.

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.

This application describes a network device, a controller, a queue management method, and a traffic management chip. The method may be applied to a traffic management chip that uses an HQoS technology, and can include receiving a queue management instruction sent by a controller, where the queue management instruction includes an identifier of a first scheduler and an identifier of a first queue, and the first scheduler is one of multiple first-level schedulers. The method may also include controlling, according to the queue management instruction, scheduling of the first queue by the first scheduler, where a queue scheduled by the first scheduler belongs to a queue resource pool of the TM chip, and the queue resource pool includes at least one to-be-allocated queue. In this application, decoupling between queue allocation and the first-level schedulers is implemented, flexibility of queue allocation is improved, and utilization of queue resources is improved.

Technologies for network packet processing include a computing device that receives incoming network packets. The computing device adds the incoming network packets to an input lockless shared ring, and then classifies the network packets. After classification, the computing device adds the network packets to multiple lockless shared traffic class rings, with each ring associated with a traffic class and output port. The computing device may allocate bandwidth between network packets active during a scheduling quantum in the traffic class rings associated with an output port, schedule the network packets in the traffic class rings for transmission, and then transmit the network packets in response to scheduling. The computing device may perform traffic class separation in parallel with bandwidth allocation and traffic scheduling. In some embodiments, the computing device may perform bandwidth allocation and/or traffic scheduling on each traffic class ring in parallel. Other embodiments are described and claimed.

Technologies for balancing throughput across input ports include a network switch. The network switch is to generate, for an arbiter unit in a first stage of a hierarchy of stages of arbiter units, turn data indicative of a set of turns in which to transfer packet data from devices connected to input ports of the arbiter unit. The network switch is also to transfer, with the arbiter unit, the packet data from the devices in the set of turns. Additionally, the network switch is to determine weight data indicative of the number of turns represented in the set and provide the weight data from the arbiter unit in the first stage to another arbiter unit in a subsequent stage to cause the arbiter unit in the subsequent stage to allocate a number of turns for the transfer of the packet data from the arbiter unit in the first stage.

A method comprising: receiving, by a first network packet scheduler, from each other network packet scheduler of a plurality of network packet schedulers, a virtual packet for each traffic class of a plurality of traffic classes defining relative transmission priority of network packets; receiving, by the first network packet scheduler, a network packet of a first traffic class of the plurality of traffic classes; transmitting, by the first network packet scheduler, each virtual packet into a virtual connection of a plurality of virtual connections created for each traffic class; scheduling, by the first network packet scheduler, a network packet or a virtual packet as a next packet in a buffer for transmission; determining, by the first network packet scheduler, that the next packet in the buffer is a virtual packet; and discarding, by the first network packet scheduler, the virtual packet, responsive to the determination that the next packet in the buffer is a virtual packet.

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.

Embodiments relate to allocating resources of computing devices for providing information service in a network. The computing devices may be hierarchically structured and may include, for instance, cloud servers, telecommunication servers, edge edges, gateways, and client devices. A system environment may include a hierarchical orchestrator coordinating with one or more local orchestrators to allocate service components (for example, a discrete functional software or hardware component) to computing devices. The orchestrators can automatically reallocate resources responsive to detecting update events such as a change in traffic or payload on the network.

According to a method for scheduling a user request in a distributed resource system, an apparatus, and a system that are provided by embodiments of the present invention, in a T.sub.n+1 period, an S.sub.d acquires, from a coordinator G.sub.k of a user z, a resource C.sub.z(T.sub.n) that is consumed by a user z request in a T.sub.n period, and the S.sub.d schedules, according to .sub.z, C.sub.z(T.sub.n), C.sub.z,d(T.sub.n), and N.sub.z,d(T.sub.n), a P.sup.i.sub.z,d by using a scheduling algorithm. The user z request can be scheduled without depending on a user agent. In addition, the S.sub.d schedules, according to .sub.z, C.sub.z(T.sub.n), C.sub.z,d(T.sub.n), and N.sub.z,d(T.sub.n), the P.sup.i.sub.z,d by using the scheduling algorithm, thereby implementing global scheduling on the user z request and ensuring a performance requirement of the user z.