Embodiments of this application provide a communication method, apparatus. One example method includes: a first data analytics network element receive a first request from a sec data analytics network element, wherein the first request carries an analytics type identifier and second model requirement information, and the first request requests information of a model that corresponds to the analytics type identifier and meets the second model requirement information. The second data analytics network element receives the information of the model from the first data analytics network element.

There is provided a method and system for communication network management. There is provided an active TE architecture and procedure that rely on the epistemic uncertainty obtained from traffic forecasting models. According to embodiments, the traffic forecasting models can predict the mean of the network traffic demand and can extract one or more of the features relating epistemic uncertainty and the aleatoric uncertainty. According to embodiments, the epistemic uncertainty is used to vary the sampling frequency of network statistics in TE applications, for specific times or specific flows. A time-window can be used to predict network traffic can be varied (e.g. increased or decreased) to adjust the epistemic uncertainty.

The technologies described herein are generally directed to facilitating the allocation, scheduling, and management of network slice resources. According some embodiments, a system can facilitate performance of operations. The operations can include, based on a request for a network service type that was received from a user device, allocating a network slice of a network to the user device, with the network slice being previously assigned a capacity of a resource of the network in accordance with a resource profile. Further, operations include monitoring performance of the network slice, resulting in monitored slice performance compared to a performance requirement of the network service type. Another operation includes, based on the monitored slice performance, facilitating recalibration of the resource profile in accordance with a condition associated with the network service type, resulting in a modification of the capacity of the resource assigned to the network slice.

Technologies for allocating resources of managed nodes to workloads to balance multiple resource allocation objectives include an orchestrator server to receive resource allocation objective data indicative of multiple resource allocation objectives to be satisfied. The orchestrator server is additionally to determine an initial assignment of a set of workloads among the managed nodes and receive telemetry data from the managed nodes. The orchestrator server is further to determine, as a function of the telemetry data and the resource allocation objective data, an adjustment to the assignment of the workloads to increase an achievement of at least one of the resource allocation objectives without decreasing an achievement of another of the resource allocation objectives, and apply the adjustments to the assignments of the workloads among the managed nodes as the workloads are performed. Other embodiments are also described and claimed.

Methods and systems for performing operations comprising: accessing one or more data objects including a data set that has been collected over a given span of time, the data set representing a plurality of parameters corresponding to resource utilization of a given server; computing first and second statistical measures based on the plurality of parameters; obtaining current resource utilization corresponding to at least a subset of the plurality of parameters; determining a first condition in which values of the current resource utilization exceed a first threshold associated with the first statistical measure; determining a second condition in which values of the data set corresponding to a time period associated with the current resource utilization exceed a second threshold associated with the second statistical measure; and triggering an anomaly detection operation in response to determining the first and second conditions.

Example aspects include techniques for implementing resource governance in multi-tenant environment. These techniques may include receiving a service request for a multi-tenant service from a client device, and predicting a resource utilization value (RUV) resulting from execution of the service request based on text of the service request, an amount of data associated with the client device at the multi-tenant service, and/or a temporal execution value. In addition, the techniques may include determining that the RUV is greater than a preconfigured threshold identifying an expensive request, and applying a load balancing strategy to the service request based on the RUV being greater than the preconfigured threshold.

Cloud infrastructure planning systems and methods can utilize artificial intelligence/machine learning agents for developing a plan of demand, plan of record, plan of execution, and plan of availability for developing cloud infrastructure plans that are more precise and accurate, and that learn from previous planning and deployments. Some agents include one or more of supervised, unsupervised, and reinforcement machine learning to develop accurate predictions and perform self-tuning alone or in conjunction with other agents.

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.

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 circuitry 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.

A method of forecasting a future load may include: obtaining source data sets and a target data set that have been collected from a plurality of source base stations and a target base station, respectively; among a plurality of source machine learning models, selecting at least one machine learn source model that has a traffic load prediction performance higher than that of a target machine learning model through a negative transfer analysis; obtaining model weights to be applied to the target machine learning model and the selected at least one source machine learning model via an attention neural network that is jointly trained with the target machine learning model and the selected source machine learning models; obtaining a load forecasting model for the target base station by combining the target machine learning model and the selected at least one source machine learning model according to the model weights; and predicting a future communication traffic load of the target base station based on the load forecasting model.