A network device may receive configuration data identifying resource subscription thresholds associated with a plurality of respective endpoint devices and may receive traffic from the plurality of endpoint devices. The network device may process the traffic and the configuration data, with a resource allocation model, to determine that processing traffic associated with a first endpoint device requires allocating a resource of the network device, and may process the configuration data, with the resource allocation model, to identify the resource of the network device from a particular resource of the network device that is currently allocated to traffic associated with a second endpoint device. The network device may allocate the particular resource of the network device to the traffic associated with the first endpoint device, and may process the traffic associated with the first endpoint device with the particular resource to generate processed traffic.

Disclosed is a method of network resource allocation. The method includes: generating an adjacency matrix of nodes in a metropolitan area network (MAN) according to spatial adjacency relationships of the nodes; generating a network state feature matrix according to traffic information of each node; extracting traffic spatial features of the nodes from the adjacency matrix and the network state feature matrix through a traffic spatial feature extraction model; obtaining predicted traffic of the nodes from the traffic spatial features through a traffic prediction model; and performing a network resource allocation according to the predicted traffic of the nodes. Further, a device of network resource allocation and a non-transitory computer-readable storage medium are also disclosed.

Methods, systems, and apparatuses related to edge processing of sensor data using a neural network to reduce network traffic to and/or from a server. In one approach, a cloud server processes sensor data from a vehicle using an artificial neural network (ANN). The ANN has several layers. Based on analyzing at least one characteristic of the sensor data received from the vehicle and/or a context associated with processing the sensor data, the cloud server determines to send one or more of the layers of the ANN for edge processing on the vehicle itself. In other cases, the cloud server decides to send the one or more layers to an edge server device located on a communication path between the vehicle and the cloud server. The edge processing reduces network data traffic.

The present disclosure relates to devices, methods, and computer-readable medium for providing recommendations for alternate resources to use for cloud services. The devices, methods, and computer-readable medium may receive a resource allocation request for a new resource of a computing system and may predict an occurrence of a capacity related allocation for the resource allocation request. The devices, methods, and computer-readable medium may identify alternate resources to use for the resource allocation request and may provide recommendations with the alternate resources.

Aspects of the subject disclosure may include, for example, obtaining predicted available bandwidths for an end user device, monitoring buffer occupancy of a buffer of the end user device, determining bit rates for portions of media content according to the predicted available bandwidths and according to the buffer occupancy, and adjusting bit rates for portions of media content according to the predicted available bandwidths and according to the buffer occupancy during streaming of the media content to the end user device over a wireless network. Other embodiments are disclosed.

A system for provisioning an elastic computing infrastructure is provided. The system includes a memory and at least one processor coupled to the memory. The system also includes a management component executed by the at least one processor and configured to instantiate an objective object having a resource collection and instructions that specify processing performed by the objective object, the resource collection identifying at least one resource object that controls a capacity of at least one resource provided by at least one computer system, the capacity being sufficient for processing to be performed at a predetermined performance level.

A method of allocating cloud computing resources in a cloud computing environment to a user is disclosed. The method comprises determining a prediction accuracy score for each user indicative of their user predictability, and allocating cloud computing resources to each user dependent on their user predictability.

The present disclosure relates to devices, methods, and computer-readable medium for providing recommendations for alternate resources to use for cloud services. The devices, methods, and computer-readable medium may receive a resource allocation request for a new resource of a computing system and may predict an occurrence of a capacity related allocation for the resource allocation request. The devices, methods, and computer-readable medium may identify alternate resources to use for the resource allocation request and may provide recommendations with the alternate resources.

Technologies for switching network traffic include a network switch. The network switch includes one or more processors and communication circuitry coupled to the one or more processors. The communication circuity is capable of switching network traffic of multiple link layer protocols. Additionally, the network switch includes one or more memory devices storing instructions that, when executed, cause the network switch to receive, with the communication circuitry through an optical connection, network traffic to be forwarded, and determine a link layer protocol of the received network traffic. The instructions additionally cause the network switch to forward the network traffic as a function of the determined link layer protocol. Other embodiments are also described and claimed.

Embodiments of this application disclose a radio frequency resource allocation method, apparatus, device, system, and a storage medium. The method includes: obtaining radio frequency information of an access point AP (for example, RSSI signal strength between the AP and each neighboring AP, and data traffic of the AP in a data collection period), predicting, based on the radio frequency information of the AP, load of the AP that is in target duration after a current moment, and allocating a radio frequency resource to the AP based on the load of the AP. This implementation can reduce actual interference on an entire network and improve user experience.