Abstract: A method and related system for application resilience by proactively switching data center regions based on detected failures in shared intermittent components by determining shared components of a first data center region based on monitoring data associated with a set of deployed applications with or without requiring the occurrence of active traffic. The method includes determining an intermittent component of the shared components based on the monitoring data and an activity gap threshold. The method further includes probing the intermittent component, obtaining a set of responses from the intermittent component, and determining a combined resource value based on performance data associated with the set of deployed applications. The method further includes, in response to a determination that the set of responses satisfies a set of region-switching criteria, provisioning a second set of infrastructure resources of a second data center region based on the combined resource value.

Abstract: A device may receive a configuration of a network device, where the configuration includes configuration blocks with references to high level objects. The device may receive an out-of-band configuration change from the network device, and may compare the out-of-band configuration change and the configuration to identify an impacted high level object. The device may determine an impacted reference and an impacted configuration block associated with the impacted high level object, and may determine whether to reject the out-of-band configuration change, accept the out-of-band configuration change, or merge the out-of-band configuration change with the configuration based on the impacted high level object, the impacted reference, and the impacted configuration block. The device may perform one or more actions based on determining whether to reject the out-of-band configuration change, accept the out-of-band configuration change, or merge the out-of-band configuration change with the configuration.

Abstract: A method for predicting traffic suppression, an electronic device and a storage medium are disclosed. The method may include: determining a traffic value of suppression point according to a preset network traffic model which represents a mapping relationship between a numerical value of a network parameter of a transmission network and a traffic value, wherein the traffic value of suppression point is a traffic threshold of the transmission network under a current running policy; determining a suppression reference value of a target network parameter corresponding to the traffic value of suppression point; and acquiring a parameter prediction value corresponding to the target network parameter, and determining a traffic suppression prediction result according to the parameter prediction value and the suppression reference value.

Abstract: The technology disclosed herein enables continuity of a call recording when a recording system restarts. In a particular example, a method includes establishing a recording session between a session recording client and a session recording system. The recording session is associated with a communication session being recorded. The method also includes periodically transmitting requests for counter values from the session recording system. The counter value indicates a number of times the session recording system has restarted. The method further includes receiving a first counter value of the counter values from the session recording system and receiving subsequent counter values of the counter values from the session recording system after receiving the first counter value. In response to determining that one of the subsequent counter values does not match the first counter value, the method includes ending the recording session.

Abstract: Disclosed is a content distribution system including a content server communicatively coupled to a data packet network and running one or more server-side components. A mobile computing device also communicatively coupled to the data packet network and running a client-side component may be configured to: (a) receive from said one or more server-side components a mobile device operational scenario definition; (b) activate or configure one or more monitoring routines in accordance with the device operational scenario definition; and (c) monitor said mobile computing device for an occurrence of the defined operating scenario using the activated or configured monitoring routines.

Abstract: Techniques and architecture are described that provide a multi-platform tool focused on displaying various levels of a topology of a network to a user while maintaining an original context of the user that was initially selected. Elements within the topology are dynamically added or removed from the user's view, e.g., the display, as the elements come in and out of focus. For example, in configurations, as the user traverses the topology, e.g., graph, the nodes that are not of interest generally become invisible. Thus, the user receives a clear view of how the selected component changed according to the user's actions and may achieve an easily maintained history of the current hops through the network topology.

Abstract: An initial Network on Chip (NoC) topology based on a set of initial requirements is incrementally modified to satisfy a set of different requirements. Each incremental modification includes minimizing a number of changes to existing components in the initial topology. Minimizing the changes includes preserving names of the existing components in the initial NoC topology.

Abstract: As described herein, a system, method, and computer program are provided for intent translation to Topology and Orchestration Specification for Cloud Applications (TOSCA) in intent-based orchestration. A microservice of a service and network orchestrator receives an intent defined in a first format associated with a first standard used for modeling and management of network services. The microservice translates the intent defined in the first format to a second format associated with a TOSCA-based model used for modeling and management of network services, to form the intent defined in the second format. The microservice outputs the intent defined in the second format.

Abstract: A method for reducing latency in communication over a network incudes obtaining historical data concerning communications including latency, inter-message timing, volume and number of sessions, converting the obtained data into feature parameters, including an inter-message timing metric, for use as inputs to a machine learning algorithm, training the machine learning algorithm using the feature parameters to predict communication latency.

Abstract: Disclosed are a traffic message forwarding method, a client, a controller, and a storage medium. The traffic message forwarding method may includes: receiving a traffic message; determining, in response to the traffic message comprising a block resource identifier, a target block resource according to the block resource identifier and filtering rule information, wherein the filtering rule information is received from a border gateway controller; and forwarding the traffic message using the target block resource.

Abstract: A message management server may receive selections, from a message publisher, to build a message series. The message series includes one or more messages and a live chat triggering condition. The message management server may generate a message series preview of the message series. The message series preview includes a simulation of a message in the message series. The message management server may receive a command from the message publisher to launch the message series. The message management server may transmit at least one of the messages in the message series to an end user. The message management server may determine that the live chat triggering condition is met. The message management server may launch a live chat based on the live chat triggering condition being met. The live chat connects the end user to a natural person agent.

Abstract: The described technology generally relates to modifying network routing and bandwidth balancing for lower latency and high quality fair traffic. For instance, based on network routing data comprising network quality data representative of a network quality applicable to a wide area network and using machine learning applied to historical network routing data, other than the network routing data, available routes are classified, the available routes being between a source node on the wide area network and a destination node on the wide area network. Further, based on the network routing data and using a result of the machine learning applied to the historical network routing data, bandwidth allocations applicable to the classified available routes can be determined. Then, based on the classified available routes and the predicted bandwidth allocations, a route is set for data transmitted from the source node to the destination node.

Abstract: A network parameter configuration method, where in the method, network running data corresponding to a first period is input into a prediction model, so that the prediction model predicts, based on the input network running data, a value of a parameter of a network device in a second period, and the parameter of the network device in the second period is configured to the value predicted by the prediction model.

Abstract: There is provided a computer program, method and apparatus for an application function that causes the application function to: signal, to a first network function, a first request to be notified of at least one change in first user plane functions associated with a first virtual network, the first virtual network comprising the first user plane functions and a first interface interfacing the first user plane functions with a first data network; receive, from the first network function, a first indication that said at least one change has occurred; and configure connectivity with at least one interface to the first virtual network in dependence on the at least one change indicated in the said first indication.

Abstract: A framework for inductive tasks in RANs includes a source learner, a target learner and an inference entity that can be merged into existing or future RAN functions. The signaling mechanism between different framework entities may also be merged into existing or future network interfaces. The framework can be reused by multiple inductive tasks to perform a variety of tasks such as prediction of some network phenomena or to improve certain KPIs in the network.

Abstract: A method for execution, by a first intermediate node of a plurality of nodes in a database management system, includes processing a message that includes data that is being sent in accordance with a routing path from a source node to a destination node, is a first size, and indicates a next node of the routing path, wherein the first intermediate node is limited to communication with a subset of nodes of the plurality of nodes, and wherein the subset of nodes includes the next node. The method further includes maintaining a tracking table that indicates a total amount of data sent to each node of the subset of nodes during a first time period. The method further includes resetting the total amount of data sent to each of the subset of nodes to zero based one or more of a command and an initiation of a second time period.

Abstract: 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.

Abstract: Methods, systems, and devices for communications are described. One or more flows between a node and one or more other nodes in a communication network may be monitored over a time period. During the monitoring, it may be identified that, during a subset of the time period, communications over at least one of the flows were restricted by the communication network based on receiving at least one indicator of congestion for the at least one flow. A quantity of traffic communicated over the one or more flows during the subset of the time period may then be determined, and respective flow rates of the one or more flows may be obtained. The obtained flow rates may be used to calculate a data rate of one or more connections between the node and the one or more other nodes.

Abstract: A system is configured to obtain constituent data streams for each data object of objects; to analyze the constituent data streams to allocate each constituent data stream to one of a set of primary groups; to assign the constituent data streams within each of the primary groups having aligned timing to a netting group of the primary group; to generate, for each netting group of each of the primary groups, continuous data streams by combining the constituent data streams within the netting groups; to define new data objects from the generated continuous data streams, each new data object specifying continuous data streams associated with a pair the primary groups; to incorporate a new data object into a set of data objects to effectuate adjustments to the set of data objects; and to cause the processor to incorporate store, the adjustments to the set of data objects.

Abstract: A system, method, and computer-readable medium for performing a data center management and monitoring operation. The data center management and monitoring operation includes: receiving operational status analysis (OSA) model data from a plurality of data center models; assigning the OSA model data to a vectorized input space; reducing a dimension of the vectorized input space to a latent space, the latent space providing an OSA model dimension; decoding the latent space to provide a vectorized decoded output space; and, performing a model operational status forecasting operation using the vectorized decoded output space, the model operational status forecasting operation generating model operational status forecasting data.