Technologies for context-aware dynamic bandwidth allocation include a network compute device configured to collect context inputs from a plurality of compute devices communicatively coupled to the network compute device. The network compute device is further configured to identify a context of each compute device based on the collected context inputs and determine a bandwidth priority for each compute device based on the identified context. Additionally, the network compute device is configure to determine an amount of bandwidth from a total available bandwidth to allocate to the compute device based on the determined bandwidth priority and update a moderated bandwidth allocation policy to reflect the determined amount of bandwidth allocated to the compute device. Other embodiments are described herein.

Resource allocation of network traffic comprising extended reality network traffic (e.g., using a computerized tool) is enabled. For example, a method can comprise: determining, by network equipment comprising a processor, whether network traffic via a radio access network comprises extended reality network traffic; in response to a determination that the network traffic comprises the extended reality network traffic, determining, by the network equipment, a traffic characteristic of the extended reality network traffic; based on the traffic characteristic, determining, by the network equipment, a resource allocation for the network traffic; and in response to determining the resource allocation for the network traffic, applying, by the network equipment, the resource allocation to a network node of the radio access network.

An application relocation method includes: a source edge enabler server (EES) sends a first message to a target EES, where the first message is used to request to relocate an application of a terminal apparatus, and the first message includes an identifier of the terminal apparatus and an identifier of the application; and the source EES receives a second message from the target EES, where the second message is a response message of the first message, and the second message is used to indicate whether a relocation of the application of the terminal apparatus is allowed.

The techniques disclosed herein enable a system to perform a robust evaluation of resource requirement recommendations through a simulated computing environment that closely resembles current conditions of a live computing environment. To achieve this, system characteristics such as CPU, RAM, and storage are extracted from currently available computing resources at the live computing environment. In addition, active software deployments at the live computing environment are randomly sampled to generate an activity dataset. The system characteristics and the activity dataset are then used to generate the simulated computing environment. Instances of a pending software deployment are then assigned to the simulated computing environment according to a resource requirement recommendation. The instances are then executed across various scenarios and analyzed to calculate a level of resource utilization. Consequently, several resource requirement recommendations can be evaluated and compared simultaneously thereby enabling the system to select the best resource requirement recommendation.

One example method includes, within a microservice architecture: (i) obtaining a resource utilization associated with a set of parameters for each microservice within a set of consistency groups, wherein, for each particular consistency group within the set of consistency groups, each microservice within the particular consistency group is associated with a particular distributed operation, and wherein each respective consistency group of the set of consistency groups defines, for each microservice within the respective consistency group, one or more threshold values associated with each parameter of the set of parameters, (ii) for multiple consistency groups of the set of consistency groups, determining whether resource utilization satisfies corresponding thresholds of the one or more threshold values, (iii) determining respective priority levels for each of the multiple consistency groups, and (iv) selectively modifying resource utilization for one or more microservices within the multiple consistency groups in accordance with the respective priority levels.

A method, system, and computer program product that includes a processor assigning a network connection to an application, based upon the application requesting the network connection from a pool of network connections for connecting applications to a network resource, the assigned network connection for communicating a message with the network resource. The processor replaces the assigned network connection in the pool with a placeholder comprising configuration data of the assigned connection. The processor determined a period of inactivity of the assigned network connection, and the processor returns the assigned network connection to the pool, based upon the period reaching a defined threshold of inactivity.

Edge networking router devices and systems for identifying a software application are described herein. One or more embodiments include an edge networking router device for identifying a software application comprising a packet collector to receive packet data in the edge networking router device and an artificial intelligence (AI) model configured to process the packet data received by the packet collector to identify the software application, wherein the artificial intelligence (AI) model is trained using a cloud entity and received from the cloud entity.

Example information transmission methods are described. One example method includes that a first base station receives resource status information from a second base station. The resource status information includes a service classification identifier and a resource status corresponding to the service classification identifier. The service classification identifier is a service type identifier or slice identification information.

Techniques for network scheduling that may provide consistent latency in content delivery are described. For example, the techniques may include receiving, by a scheduler of a carrier network, a consistent latency request associated with an application operating on a user equipment (UE), the consistent latency request including a specified latency value. Based at least in part on the specified latency value, the scheduler of the carrier network may schedule transmission of one or more packets associated with the application operating on the UE to cause the one or more packets to arrive at the UE with an inter-packet delay substantially equal to the specified latency value.

A new approach to resiliency management is provided in a data center wherein servers are constructed dynamically, on-demand and based on workload requirements and a tenant's resiliency requirements by allocating resources from these pools. In this approach, a set of functionally-equivalent “interchangeable compute units” (ICUs) are composed of resources from resource pools that have been extended to include not only different resource types (CPU, memory, accelerators), but also resources of different specifications (specs) and flavors. As a workload is being processed, the health or status of the resources are monitored. Upon a performance issue or failure event, a resiliency manager can swap out a current ICU and replace it with a functionally-equivalent ICU. Preferably, individual ICUs are hosted on one of: resources of a same type each with different specifications, and resources of a same type and specification and different flavors. The approach enables failure independence in a disaggregated environment.