Provided are a method for performing task processing on a common service entity, a common service entity, an apparatus and a medium for performing task processing. The method includes: receiving a task processing request; determining whether the common service entity itself performs the processing request; forwarding the request to another common service entity in a case where it is determined that the common service entity itself does not perform the task processing request, wherein the common service entity is associated with the another common service entity; performing, by the common service entity, the task processing request in a case where it is determined that the common service entity itself performs the task processing request.

Provided are a method for performing task processing on a common service entity, a common service entity, an apparatus and a medium for performing task processing. The method includes: receiving a task processing request; determining whether the common service entity itself performs the processing request; forwarding the request to another common service entity in a case where it is determined that the common service entity itself does not perform the task processing request, wherein the common service entity is associated with the another common service entity; performing, by the common service entity, the task processing request in a case where it is determined that the common service entity itself performs the task processing request.

A system configured to track network slicing operations within a 5G communication network includes processing circuitry configured to determine a network slice instance (NSI) associated with a QoS flow of a UE. The NSI communicates data for a network function virtualization (NFV) instance of a Multi-Access Edge Computing (MEC) system within the 5G communication network. Latency information for a plurality of communication links used by the NSI is retrieved. The plurality of communication links includes a first set of non-MEC communication links associated with a radio access network (RAN) of the 5G communication network and a second set of MEC communication links associated with the MEC system. A slice configuration policy is generated based on the retrieved latency information and slice-specific attributes of the NSI. Network resources of the 5G communication network used by the NSI are reconfigured based on the generated slice configuration policy.

A multi-access edge computing (MEC) platform may receive an indication that a user device has downloaded a MEC application client associated with a MEC application and may send, to the user device, instructions to install a device client. The device client may transmit device information associated with the user device to the MEC platform. The MEC platform may receive the device information associated with the user device and determine, based on the received device information, performance information associated with the MEC application.

A multi-access edge computing (MEC) platform may receive an indication that a user device has downloaded a MEC application client associated with a MEC application and may send, to the user device, instructions to install a device client. The device client may transmit device information associated with the user device to the MEC platform. The MEC platform may receive the device information associated with the user device and determine, based on the received device information, performance information associated with the MEC application.

Various embodiments disclosed herein are related to an apparatus. In some embodiments, the apparatus includes a processor and a memory. In some embodiments, the memory includes instructions that, when executed by the processor, cause the apparatus to collect, at a cloud server, service data from a collector framework service of an edge network. In some embodiments, the memory includes instructions that, when executed by the processor, cause the apparatus to provide a configuration to the collector framework service based on the service data.

Some examples described herein relate to providing a customized cloud service. In an example, Key Service Indicators (KSI) may be received for a cloud service. The Key Service Indicators may be associated with a cloud service template for providing the cloud service. The resources required for providing the cloud service may be identified based on the Key Service Indicators.

The subject technology selects a particular zone among multiple zones based on a target skew to meet a global balancing of cluster instances. The subject technology deploys a particular type of cluster instance to the particular zone. The subject technology, for each zone from the multiple zones, determines a respective number of cluster instances. The subject technology identifies a second particular type of cluster instance to remove based on a total number of the second particular type of cluster instance in the multiple zones and a second total number of the particular type of cluster instance in the multiple zones. The subject technology removes the second particular type of cluster instance from a second particular zone to meet the global balancing of cluster instances in the multiple zones.

Disclosed are systems, apparatuses, methods, and computer-readable media to measure performance of distinct paths of a network. A method includes determining a collection of hashes of a network based on a network probe event, each hash in the collection of hashes corresponding to a distinct path from a first edge device to a second edge device through the network; transmitting a collection of probes from the first edge device in the network, wherein each probe in the collection of probes is assigned a hash selected from the collection of hashes; receiving probes from the collection of probes at the second edge device; and determining a network performance of each distinct path through the network.

Technologies are described for determining between synchronous and asynchronous modes for computing service requests. Computing service requests are received by a computing service from clients. The computing service dynamically determines whether to use synchronous mode or asynchronous mode for processing the computing service requests. The computing service makes the dynamic determination of which mode to use (synchronous or asynchronous) based on various criteria, which can include synchronous/asynchronous mode recommendations generated by machine learning models and/or synchronous/asynchronous mode recommendations generated by static rules.