A method for managing network slices for the benefit of users monitors and obtains key performance indicators configured by a user, the indicator values being collected in real time and visually presented. When a user wants to optimize the network slices, weightings, value intervals, and variables are applied by the user to target key performance indicators. The network slices are optimized by a particle swarm algorithm configured by the user. A device and a computer readable and permanent storage medium for executing the network slices management method are also disclosed.

The disclosed system and method obtain a report of an issue reported by a user of the wireless telecommunication network, and historical information associated with the user and the wireless telecommunication network. The historical information includes multiple issues reported by users similar to the user, and multiple user statuses associated with the users similar to the user. The user status among the multiple user statuses includes active and inactive, indicating whether the user is an active member of the telecommunication network or has left the network. The system provides the historical information to an AI model, and obtains from the AI model a priority associated with the issue experienced by the user. The system causes a resolution of the issue based on the priority.

The disclosed system and method obtain a report of an issue reported by a user of the wireless telecommunication network, and historical information associated with the user and the wireless telecommunication network. The historical information includes multiple issues reported by users similar to the user, and multiple user statuses associated with the users similar to the user. The user status among the multiple user statuses includes active and inactive, indicating whether the user is an active member of the telecommunication network or has left the network. The system provides the historical information to an AI model, and obtains from the AI model a priority associated with the issue experienced by the user. The system causes a resolution of the issue based on the priority.

The present approach relates to the use of time series analyses to estimate times or time intervals when a user of IT resources is likely to schedule or request that an operation is run on those services. In certain implementations, the present approach performs forecasting using time series data and supervised machine learning techniques. These techniques may be used to help predict future times when an operation or operations may be requested for execution. Based on these predicted future time, automations (e.g., the automated execution of operations) may be scheduled so as to effectively utilize available resources and efficiently perform the operations.

A method includes receiving a representation of a predefined planned event that includes the use of a first set of cellular data service infrastructure elements. A performance of the first set of cellular data service infrastructure elements is simulated, and a predicted failure of at least one cellular data service infrastructure element from the first set of cellular data service infrastructure elements is identified based on the simulation. In response to identifying the predicted failure, a modification to the at least one cellular data service infrastructure element or an additional cellular data service infrastructure element is determined and included in a second set of cellular data service infrastructure elements whose performance is subsequently simulated. The simulated performance of the first set of cellular data service infrastructure elements is compared with the simulated performance of the second set of cellular data service infrastructure elements to determine a performance improvement.

A computing system identifies mitigation actions in response to failures within a computer network. A service level objective is obtained by the computing system for client-resource data flows traversing the computer network between client-side and resource-side nodes. Indication of a failure event at a network location of the computer network is obtained. For each mitigation action of a set of candidate mitigation actions, an estimated impact to a distribution of the service level objective is determined for the mitigation action by applying simulated client-resource data flows to a network topology model of the computer network in combination with the mitigation action and the failure event. One or more target mitigation actions are identified by the computing system from the set of candidate mitigation actions based on a comparison of the estimated impacts of the set of candidate mitigation actions.

A method for operating a telecom network having an SLA agreement is disclosed. The method comprises obtaining annotated alarm data comprising an indication of a threshold crossing of at least one state variable of the telecom network defined by the SLA. The annotated alarm data further comprises an indication of a desired value of the at least one state variable of the telecom network. Moreover, the method comprises determining a desired state of the telecom network based on the desired value of at least one state variable, and selecting a set of policy actions from a policy action bank. The policy action bank comprises a plurality of policy actions, where each policy action is associated with at least one estimated action effect. The selection of the set of policy actions is accordingly based on the estimated action effects of the plurality of policy actions and on the determined desired state of the telecom network such that the desired state is reachable upon execution of the selected set of policy actions. The method further comprises

Network traffic data associated with data requests to computer applications based on static policies is collected. An optimization order is established among network parameters. A first network parameter of a higher rank in the optimization order is estimated based on the collected network traffic data before one or more other network parameters of lower ranks are estimated. Optimal values for the other network parameters are estimated based at least in part on the estimated first optimal value for the first network parameter. The estimated first optimal value of the first network parameter and the estimated optimal values for the other network parameters are propagated to be used by user devices to make new data requests to the computer applications.

There is disclosed a method, performed by a first network entity, for load distribution in a network comprising the first entity and a second network entity providing network analytics. The method includes: receiving, from the second entity, slice analytics; and determining, based on the slice analytics, to perform a network operation.

A pre-5th-Generation (5G) or 5G communication system for supporting higher data rates Beyond 4th-Generation (4G) communication system such as Long Term Evolution (LTE). Method and/or electronic device for managing a software upgrade of a network element of a random access network (RAN) by a management data analytics service (MDAS) producer is provided. The method comprises: receiving a request related to an optimal time for the software upgrade of the network element in the RAN, from a MDAS consumer; identifying information related to a dedicated radio bearer (DRB); identifying information related to the optimal time for the software upgrade of the network element based on the information related to the DBR; and transmitting a report including the information related to the optimal time for the software upgrade of the network element.