Abstract: Aspects of the subject disclosure may include, for example, generating a set of SRLGs associated with a set of link bundles, wherein the set of SRLGs comprises for each SRLG in the set of SRLGs an indication for each failed link bundle in a particular SRLG a respective bandwidth failure fraction, and wherein for at least one of the failed link bundles the failure is less than a complete failure; generating a set of dominance relationships among the SRLGs in the set of SRLGs, and generating, based at least in part upon the set of SRLGs and the set of dominance relationships a packed set of SRLGs, wherein the packed set of SRLGs comprises a subset of the set of SRLGs. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, generating a set of SRLGs associated with a set of link bundles, wherein the set of SRLGs comprises for each SRLG in the set of SRLGs an indication for each failed link bundle in a particular SRLG a respective bandwidth failure fraction, and wherein for at least one of the failed link bundles the failure is less than a complete failure; generating a set of dominance relationships among the SRLGs in the set of SRLGs, and generating, based at least in part upon the set of SRLGs and the set of dominance relationships a packed set of SRLGs, wherein the packed set of SRLGs comprises a subset of the set of SRLGs. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, generating a set of SRLGs associated with a set of link bundles, wherein the set of SRLGs comprises for each SRLG in the set of SRLGs an indication for each failed link bundle in a particular SRLG a respective bandwidth failure fraction, and wherein for at least one of the failed link bundles the failure is less than a complete failure; generating a set of dominance relationships among the SRLGs in the set of SRLGs, and generating, based at least in part upon the set of SRLGs and the set of dominance relationships a packed set of SRLGs, wherein the packed set of SRLGs comprises a subset of the set of SRLGs. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, obtaining for a first path carrying first network traffic a corresponding first alternate path; obtaining for a second path carrying second network traffic a corresponding second alternate path, at least a first portion of the first alternate path being the same as at least a second portion of the second alternate path; responsive to a first detected failure on the first path, causing communication of first re-directed network traffic via the first alternate path instead of via the first path; responsive to a second detected failure on the second path, detecting whether total traffic, including the first re-directed network traffic, on the first portion of the first alternate path meets a threshold; responsive to the detected meeting of the threshold, obtaining for the second path a corresponding back-up alternate path; and causing communication of second re-directed network traffic via the back-up alternative path instead of via the second path.

Abstract: Systems and methods may use a math programming model for designing an edge cloud network. The edge cloud network design may depend on various factors, including the number of edge cloud nodes, edge cloud node location, or traffic coverage, among other things.

Abstract: Systems and methods may use a math programming model for designing an edge cloud network. The edge cloud network design may depend on various factors, including the number of edge cloud nodes, edge cloud node location, or traffic coverage, among other things.

Abstract: The technologies described herein are generally directed to centralized management of wireless relay node equipment in a fifth generation (5G) or other next generation networks. In accordance with one or more embodiments, a method described herein can include facilitating identifying a connection between first network equipment and second network equipment, the first network equipment being connected to a user equipment and the second network equipment. In embodiments, the method can further include facilitating monitoring the connection between the first network equipment and the second network equipment, resulting in monitoring data, wherein the second network equipment is communicatively coupled to backhaul network equipment, and with the user equipment being communicatively coupled to the backhaul network equipment via the second network equipment. Further, based on the monitoring data, adjusting, by the centralized controller system, a characteristic of the connection, in accordance with embodiments.

Abstract: The technologies described herein are generally directed to centralized management of wireless relay node equipment in a fifth generation (5G) or other next generation networks. In accordance with one or more embodiments, a method described herein can include facilitating identifying a connection between first network equipment and second network equipment, the first network equipment being connected to a user equipment and the second network equipment. In embodiments, the method can further include facilitating monitoring the connection between the first network equipment and the second network equipment, resulting in monitoring data, wherein the second network equipment is communicatively coupled to backhaul network equipment, and with the user equipment being communicatively coupled to the backhaul network equipment via the second network equipment. Further, based on the monitoring data, adjusting, by the centralized controller system, a characteristic of the connection, in accordance with embodiments.

Abstract: Systems and methods may use a math programming model for designing an edge cloud network. The edge cloud network design may depend on various factors, including the number of edge cloud nodes, edge cloud node location, or traffic coverage, among other things.

Abstract: Systems and methods may use a math programming model for designing an edge cloud network. The edge cloud network design may depend on various factors, including the number of edge cloud nodes, edge cloud node location, or traffic coverage, among other things.

Abstract: Systems and methods may use a math programming model for designing an edge cloud network. The edge cloud network design may depend on various factors, including the number of edge cloud nodes, edge cloud node location, or traffic coverage, among other things.

Abstract: A method and apparatus for detecting scans are described. In one example, a plurality of flows is allocated into a plurality of bins associated with different source internet protocol (SIP) addresses. A set of bin characteristics for at least one bin of the plurality of bins is generated if the at least one bin reaches a predefined flow capacity. Afterwards, the set of bin characteristics is compared to a scan characteristics list to determine if a potential scan exists.

Abstract: A method and apparatus for detecting scans are described. In one example, a plurality of flows is allocated into a plurality of bins associated with different source internet protocol (SIP) addresses. A set of bin characteristics for at least one bin of the plurality of bins is generated if the at least one bin reaches a predefined flow capacity. Afterwards, the set of bin characteristics is compared to a scan characteristics list to determine if a potential scan exists.

Abstract: A method and apparatus for detecting scans are described. In one example, a plurality of flows is allocated into a plurality of bins associated with different source Internet protocol (SIP) addresses. A set of bin characteristics for at least one bin of the plurality of bins is generated if the at least one bin reaches a predefined flow capacity. Afterwards, the set of bin characteristics is compared to a scan characteristics list to determine if a potential scan exists.

Abstract: A method and apparatus for detecting scans are described. In one example, a plurality of flows is allocated into a plurality of bins associated with different source Internet protocol (SIP) addresses. A set of bin characteristics for at least one bin of the plurality of bins is generated if the at least one bin reaches a predefined flow capacity. Afterwards, the set of bin characteristics is compared to a scan characteristics list to determine if a potential scan exists.

Abstract: A method and apparatus for detecting scans are described. In one example, a plurality of flows is allocated into a plurality of bins associated with different source internet protocol (SIP) addresses. A set of bin characteristics for at least one bin of the plurality of bins is generated if the at least one bin reaches a predefined flow capacity. Afterwards, the set of bin characteristics is compared to a scan characteristics list to determine if a potential scan exists.

Abstract: Methodologies and systems for detecting an anomaly in a flow of data or data stream are described herein. To detect an anomaly, an anomaly detection server may create a baseline based on historical or other known non-anomalous data within the data stream. The anomaly detection server then generates one or more test values based on current data in the data stream, and compares the test value(s) to the baseline to determine whether they vary by more than a predetermined amount. If the deviation exceeds the predetermined amount, an alarm is triggered. The anomaly detection server may continually adjust the baseline based on the current data in the data stream, and may renormalize the baseline periodically if desired or necessary.

Abstract: Methodologies and systems for detecting an anomaly in a flow of data or data stream are described herein. To detect an anomaly, an anomaly detection server may create a baseline based on historical or other known non-anomalous data within the data stream. The anomaly detection server then generates one or more test values based on current data in the data stream, and compares the test value(s) to the baseline to determine whether they vary by more than a predetermined amount. If the deviation exceeds the predetermined amount, an alarm is triggered. The anomaly detection server may continually adjust the baseline based on the current data in the data stream, and may renormalize the baseline periodically if desired or necessary.