Techniques are disclosed for generating a virtual representation (e.g., one or more digital twin models) of a multi-access edge computing system environment, and managing the multi-access edge computing system environment via the virtual representation. By way of example only, such techniques enable understanding, prediction and/or optimization of performance of applications and/or systems operating in the multi-access edge computing environment.

Provided are a cloud computing power allocation method, a user terminal, a cloud computing power platform, and a system. The method includes: generating a computing power request including a computing power demand and account information of a computing power scheduling center; sending the computing power request to a cloud computing power platform, so that the cloud computing power platform sends a configuration instruction to a computing device cluster according to the computing power request where the configuration instruction is to allocate to the user terminal a target computing device meeting the computing power demand from the computing device cluster and configure based on the account information the target computing device to execute a computing task issued by the computing power scheduling center; and acquiring from the computing power scheduling center computing power information determined according to a computing result from the target computing device, by using the account information.

A communication method and a system for converging a 5.sup.th-Generation (5G) communication system for supporting higher data rates beyond a 4.sup.th-Generation (4G) system with a technology for Internet of Things (IoT) is provided. The disclosure is applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as a smart home, a smart building, a smart city, a smart car, a connected car, health care, digital education, a smart retail, security and safety services. A method performed by a first entity performing a network data analytics function (NWDAF) is provided. The method includes receiving, from a second entity performing network function (NF), a first message for requesting observed service experience analytics, the first message including single-network slice selection assistance information (S-NSSAI) indicating a network slice, transmitting, to a third entity performing application function (AF) associated with the S-NSSAI, a second message for requesting service data associated with the observed service experience analytics, the second message including information on at least one application, receiving, from the third entity, the service data including at least one service experience for the at least one application, identifying the observed service experience analytics based on the service data, and transmitting, to the second entity, the observed service experience analytics.

Policies can be applied to, and enforced for, specific resources by applying a corresponding tag to those resources. An entity, such as a customer of a resource provider, can generate one or more policies to be applied to a set of resources, where those policies can relate to data retention, backup, lifecycle events, and other such aspects. Each policy can be associated with a particular tag, which may comprise a key-value pair to be applied to various resources. A policy enforcement manager can determine the tagged resources and ensure that the relevant policies are applied. The policies can include logic or intelligence for performing a variety of tasks with respect to resources, groups of resources, or types of resources, as identified using the tags.

A method in which an enterprise switches its devices to various federated network slices across operators based on cost, time, quality, and/or availability parameters defined in flexible rules managed by the enterprise. The method includes obtaining, by a controller of an enterprise, one or more parameters of a device served by a network slice of a core network. The method further includes, based on the one or more parameters of the device and one or more rules, determining, by the controller, whether a triggering event associated with a slice reselection occurred and based on the triggering event and the one or more rules, selecting, by the controller, a federated network slice from among a plurality of network slices provided by a plurality of core networks. The method further includes the controller causing the device to switch from the network slice to the federated network slice.

Systems and methods described herein support a usage calculation process in a cloud infrastructure environment. The usage calculation process can be used to determine whether a requested transaction that targets a compartment within a tree-structure of compartments violates any compartment quota or limit within parent compartments within the tree-structure.

Methods and systems for managing distributed systems are disclosed. The distributed system may include any number of data processing systems that may contribute to the functionality of the distributed system. To contribute to the functionality of the distributed system, each of the data processing systems may need to be configured to facilitate cooperative operation. To manage configuration of data processing system, a control plane may be utilized. The control plane may utilize a consensus based process for managing leadership among members of the control plane.

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.

Systems and methods are disclosed for managing workload among server clusters is disclosed. According to certain embodiments, the system may include a memory storing instructions and a processor. The processor may be configured to execute the instructions to determine historical behaviors of the server clusters in processing a workload. The processor may also be configured to execute the instructions to construct cost models for the server clusters based at least in part on the historical behaviors. The cost model is configured to predict a processor utilization demand of a workload. The processor may further be configured to execute the instructions to receive a workload and determine efficiencies of processing the workload by the server clusters based at least in part on at least one of the cost models or an execution plan of the workload.

Described are examples for providing service level monitoring for a network hosting applications as a cloud service. A service level monitoring device may receive end-to-end measurements of service usage collected at user devices for a plurality of applications hosted as a cloud services. The service level monitoring device may determine degraded applications of the plurality of applications based on anomalies in the measurements. The service level monitoring device may determine a service level metric based on an aggregation of the degraded applications. In some examples, the service level monitoring device may detect a network outage affecting the service.