Abstract: A method performed by a processing system including at least one processor includes grouping a plurality of nodes of a telecommunications network into a plurality of reference groups, based on a plurality of configuration attributes and on a plurality of load, mobility, radio frequency attributes for the plurality of nodes, selecting a first reference group of the plurality of reference groups, where the first reference group includes a subset of the plurality of nodes, selecting a first configuration parameter of the first reference group to be tuned, identifying a first value for the first configuration parameter that is most prevalent among the subset of the plurality of nodes, and setting the first configuration parameter for all nodes in the subset of the plurality of nodes to the first value.

Abstract: Aspects of the subject disclosure may include, for example, obtaining a natural language statement of an optimization problem that is to be solved, analyzing the natural language statement to select a template included within a plurality of templates, generating a definition for the optimization problem using the template, constructing a model in accordance with the definition, resulting in a constructed model, and validating the constructed model, resulting in a validated model. Other embodiments are disclosed.

Abstract: A method includes selecting a study group including a first network element and a second network element of a network, selecting a control group including a third network element, identifying times at which a change is deployed at the first network element and the second network element, time-aligning the change at the first element and the change at the second network element to a common time, performing a statistical analysis that compares the performance of the network before the common time to the performance of the network after the common time, detecting an impact of the change on a performance of the network based on the statistical analysis, and initiating a remedial action when the impact comprises a degradation to the performance.

Abstract: Aspects of the subject disclosure may include, for example, obtaining a natural language statement of an optimization problem that is to be solved, analyzing the natural language statement to select a template included within a plurality of templates, generating a definition for the optimization problem using the template, constructing a model in accordance with the definition, resulting in a constructed model, and validating the constructed model, resulting in a validated model. Other embodiments are disclosed.

Abstract: A method includes selecting a study group including a first network element and a second network element of a network, selecting a control group including a third network element, identifying times at which a change is deployed at the first network element and the second network element, time-aligning the change at the first element and the change at the second network element to a common time, performing a statistical analysis that compares the performance of the network before the common time to the performance of the network after the common time, detecting an impact of the change on a performance of the network based on the statistical analysis, and initiating a remedial action when the impact comprises a degradation to the performance.

Abstract: A new scalable approach to conflict-free deployment of changes across networks. The conflict rules or constraints may be modeled using policies and algorithms to determine an optimized schedule for change deployment.

Abstract: A new scalable approach to conflict-free deployment of changes across networks. The conflict rules or constraints may be modeled using policies and algorithms to determine an optimized schedule for change deployment.

Abstract: A processing system may obtain a data set with records of network parameter changes, each record including at least one network parameter change and at least one attribute associated with a first aspect of a communication network, and a corresponding network performance indicator change. A first record may include a plurality of network parameter change groups. The processing system may next perform a de-confusion process by identifying a second record comprising a single network parameter change group, determining that a corresponding network performance indicator change is different from that of the at least the first record, and updating the data set to replace the first record with at least two replacement records. The processing system may apply at least one of the network parameter change groups to a second aspect of the communication network based upon a decision output of a classifier that is trained using the updated data set.

Abstract: A framework referred to as COmposition fRamework for chaNge management (CORNET) may integrate re-usable abstraction, modular composition with plug-and-play capabilities, or automated translation of high-level change management intent into low-level implementations and mathematical models. CORNET may use real-world data collected from cellular networks (e.g., 4G or 5G) and virtualized services, such as virtual private networks (VPN) and software defined wide area networks (SDWAN) running in the cloud.

Abstract: A method performed by a processing system including at least one processor includes grouping a plurality of nodes of a telecommunications network into a plurality of reference groups, based on a plurality of configuration attributes and on a plurality of load, mobility, radio frequency attributes for the plurality of nodes, selecting a first reference group of the plurality of reference groups, where the first reference group includes a subset of the plurality of nodes, selecting a first configuration parameter of the first reference group to be tuned, identifying a first value for the first configuration parameter that is most prevalent among the subset of the plurality of nodes, and setting the first configuration parameter for all nodes in the subset of the plurality of nodes to the first value.

Abstract: A framework referred to as COmposition fRamework for chaNge management (CORNET) may integrate re-usable abstraction, modular composition with plug-and-play capabilities, or automated translation of high-level change management intent into low-level implementations and mathematical models. CORNET may use real-world data collected from cellular networks (e.g., 4G or 5G) and virtualized services, such as virtual private networks (VPN) and software defined wide area networks (SDWAN) running in the cloud.

Abstract: A new scalable approach to conflict-free deployment of changes across networks. The conflict rules or constraints may be modeled using policies and algorithms to determine an optimized schedule for change deployment.

Abstract: A method of reverse migrating traffic in a network includes receiving, via an application programming interface (API), a request to migrate traffic, the request identifying a target around which the traffic is to be migrated and a peer to which the traffic is to be migrated. The computer-implemented method further includes generating one or more masks comprising weights indicating a percentage of traffic to send to each port of at least two ports. The computer-implemented method further includes stacking the one or more masks on a base mask denoting a default configuration of a network component. The computer-implemented method further includes facilitating migration of the traffic using the one or more masks. The computer-implemented method further includes performing a reverse migration from the target to the peer by removing each of the one or more masks.

Abstract: A framework referred to as COmposition fRamework for chaNge management (CORNET) may integrate re-usable abstraction, modular composition with plug-and-play capabilities, or automated translation of high-level change management intent into low-level implementations and mathematical models. CORNET may use real-world data collected from cellular networks (e.g., 4G or 5G) and virtualized services, such as virtual private networks (VPN) and software defined wide area networks (SDWAN) running in the cloud.

Abstract: A new scalable approach to conflict-free deployment of changes across networks. The conflict rules or constraints may be modeled using policies and algorithms to determine an optimized schedule for change deployment.

Abstract: A method includes selecting a study group including a first network element and a second network element of a network, selecting a control group including a third network element, identifying times at which a change is deployed at the first network element and the second network element, time-aligning the change at the first element and the change at the second network element to a common time, performing a statistical analysis that compares the performance of the network before the common time to the performance of the network after the common time, detecting an impact of the change on a performance of the network based on the statistical analysis, and initiating a remedial action when the impact comprises a degradation to the performance.

Abstract: A processing system may obtain values of carrier attributes for a new carrier to be deployed in a cellular network, obtain values of the carrier attributes and values of a configuration parameter for a plurality of existing carriers in the cellular network, and construct an attribute value matrix comprising the values of the carrier attributes for the plurality of existing carriers, where the attribute value matrix associates the carrier attributes to the plurality of existing carriers, apply a regression to a function associating the attribute value matrix to a vector of the values of the configuration parameter for the plurality of existing carriers to learn coefficients of the function, apply the coefficients to a vector comprising the values of the plurality of carrier attributes for the new carrier to generate a value of the configuration parameter for the new carrier, and apply the generated value to the new carrier.

Abstract: A method includes selecting a study group including a first network element and a second network element of a network, selecting a control group including a third network element, identifying times at which a change is deployed at the first network element and the second network element, time-aligning the change at the first element and the change at the second network element to a common time, performing a statistical analysis that compares the performance of the network before the common time to the performance of the network after the common time, detecting an impact of the change on a performance of the network based on the statistical analysis, and initiating a remedial action when the impact comprises a degradation to the performance.

Abstract: A processing system may obtain values of carrier attributes for a new carrier to be deployed in a cellular network, obtain values of the carrier attributes and values of a configuration parameter for a plurality of existing carriers in the cellular network, and construct an attribute value matrix comprising the values of the carrier attributes for the plurality of existing carriers, where the attribute value matrix associates the carrier attributes to the plurality of existing carriers, apply a regression to a function associating the attribute value matrix to a vector of the values of the configuration parameter for the plurality of existing carriers to learn coefficients of the function, apply the coefficients to a vector comprising the values of the plurality of carrier attributes for the new carrier to generate a value of the configuration parameter for the new carrier, and apply the generated value to the new carrier.

Abstract: A method for multi-dimensional impact detection and diagnosis of networks. The methods and systems dynamically explore only those network feature combinations that are likely to have problems by using a summary structure Sketch, for example. The method and systems capture fine-grained anomalies at a coarse level which allows for on-demand zoom into the finer-grained levels for further analysis.