Abstract: A method for storing time-based data streams in a high-capacity network is provided. A time-based data storage request is received from an application. The data storage request is associated with one or more data streams. Each of the data streams includes a plurality of time-ordered items having a header. The header includes two or more timestamps representing a time interval associated with each of the plurality of time-ordered items. The received data storage request is processed to accumulate time-ordered data items in a plurality of data files and to identify time-based information and corresponding virtual offset information related to the accumulated data items. The identified time-based information and the virtual offset information related to the accumulated data items are stored in a data repository. A determination is made whether adjustment is necessary for sorting the stored data in the repository. The sorting adjustment is selectively performed responsive to the determination.

Abstract: A method of configuring a filter to perform pattern matching against input data is provided. The method includes receiving one or more rules, each rule including one or more field specifiers, each field specifier including a value specifier that specifies a value to be matched and a location specifier that specifies a location in the input data. For each rule of the one or more rules an empty buffer is initialized. For each field specifier the value specified by the field specifier is appended to the buffer, and the buffer contents are inserted into contents of a probabilistic data structure representing all of the field specifiers of the rule. The probabilistic data structure is configured to receive a query that includes query buffer contents determined from the input data and respond with a match status of probably present based on a predetermined probability, or definitely not present.

Abstract: A machine learning method performed by a communication network monitoring device in which an incoming signaling record is received that includes radio signal attributes from a UE in the cellular communication network. A determination is made as to whether the UE incoming signaling record contains location (GPS) data. If the UE incoming signaling record contains GPS data, a machine learning model is generated for determining a location of future UEs in the communication network utilizing the GPS data and the radio signal attributes from the incoming UE signaling record. And if GPS data is not included in the UE incoming signaling record, then first a corrected TA value is determined which is then used, along with other radio signal attributes of the UE, to determine/predict a geolocation for the UE using machine learning techniques.

Abstract: A computer method and system for analyzing performance of a 5G communication network for determining 4G traffic offload characteristics. Determine relative spectrum efficiency for data downlinks and uplinks in the 5G communication network and determine values for pairs of 4G LTE primary cells and 5G NR primary cells. A heatmap is generated utilizing the determined values for depicting 4G traffic offload characteristics in a 5G communication network wherein the generated heatmap includes a generated line depicting a connection between a LTE PCell to a 5G NR PScell wherein a width of the generated line is scalable relative to a determined percentage of 5G utilized traffic in the 5G communication system.

Abstract: A processor having kernel space and user space and a method is provided. The method includes receiving in the user space at least one interface statistic about each hardware interface of one or more hardware interfaces receiving packets, wherein the at least one interface statistic is provided from the hardware interface. The method further includes dynamically adjusting, from within the user space, a priority at which each of the one or more hardware interfaces is polled as a function of the at least one interface statistic.

Abstract: A method of configuring a filter to perform pattern matching against input data is provided. The method includes receiving one or more rules, each rule including one or more field specifiers, each field specifier including a value specifier that specifies a value to be matched and a location specifier that specifies a location in the input data. For each rule of the one or more rules an empty buffer is initialized. For each field specifier the value specified by the field specifier is appended to the buffer, and the buffer contents are inserted into contents of a probabilistic data structure representing all of the field specifiers of the rule. The probabilistic data structure is configured to receive a query that includes query buffer contents determined from the input data and respond with a match status of probably present based on a predetermined probability, or definitely not present.

Abstract: A machine learning method performed by a communication network monitoring device in which an incoming signaling record is received that includes radio signal attributes from a UE in the cellular communication network. A determination is made as to whether the UE incoming signaling record contains location (GPS) data. If the UE incoming signaling record contains GPS data, a machine learning model is generated for determining a location of future UEs in the communication network utilizing the GPS data and the radio signal attributes from the incoming UE signaling record. And if GPS data is not included in the UE incoming signaling record, then first a corrected TA value is determined which is then used, along with other radio signal attributes of the UE, to determine/predict a geolocation for the UE using machine learning techniques.

Abstract: A machine learning method performed by a communication network monitoring device in which an incoming signaling record is received that includes radio signal attributes from a UE in the cellular communication network. A determination is made as to whether the UE incoming signaling record contains location (GPS) data. If the UE incoming signaling record contains GPS data, a machine learning model is generated for determining a location of future UEs in the communication network utilizing the GPS data and the radio signal attributes from the incoming UE signaling record. And if GPS data is not included in the UE incoming signaling record, then the geolocation for the UE is predicted using machine learning techniques utilizing a previous generated machine learning model as applied to the radio signal attributes from the incoming UE signaling record.

Abstract: A machine learning method performed by a communication network monitoring device in which an incoming signaling record is received that includes radio signal attributes from a UE in the cellular communication network. A determination is made as to whether the UE incoming signaling record contains location (GPS) data. If the UE incoming signaling record contains GPS data, a machine learning model is generated for determining a location of future UEs in the communication network utilizing the GPS data and the radio signal attributes from the incoming UE signaling record. And if GPS data is not included in the UE incoming signaling record, then first a corrected TA value is determined which is then used, along with other radio signal attributes of the UE, to determine/predict a geolocation for the UE using machine learning techniques.

Abstract: A system and method for performing dynamic load balancing in a network load balancer device. A network packet flow is monitored in a load balancer device for detecting dropped network packets, or a sampling of traffic flow, in the monitored network packet flow. Metadata (e.g., a 5-tuple) is created for the detected dropped network packets or sampling of network traffic flow. The metadata is then transmitted to a flow processing analysis device for performing load balancing flow processing. Upon analysis, a determination is made in the flow processing analysis device for determining reconfiguration load balancing settings for the load balancer to mitigate occurrence of unintended dropping of network packets in the load balancer and/or improve its overall performance. The reconfiguration load balancing settings are then transmitted to the load balancer device, whereupon receipt the load balancer changes its settings in accordance with the determined reconfiguration settings.

Abstract: A method includes receiving call records from a control plane, each call record including a cell list identifying the server cell for the UE call session at the time the call record was generated and an ordered set of neighbor cells, ordered based on a characteristic of signals from the neighbor cells. Call records having truth data are selected, wherein the truth data includes geolocation (GL) data reported to be a GL associated with the call record. GL data of the selected call records is stored in association with the cell list for the selected call records. A centroid is determined as a function of the GL data associated with each of the selected call records that includes the associated cell list. The centroids are stored in association with the corresponding cell list, and can be retrieved as a prediction for a GL based on submission of a cell list.

Abstract: A method includes receiving call records from a control plane, each call record including a cell list identifying the server cell for the UE call session at the time the call record was generated and an ordered set of neighbor cells, ordered based on a characteristic of signals from the neighbor cells. Call records having truth data are selected, wherein the truth data includes geolocation (GL) data reported to be a GL associated with the call record. GL data of the selected call records is stored in association with the cell list for the selected call records. A centroid is determined as a function of the GL data associated with each of the selected call records that includes the associated cell list. The centroids are stored in association with the corresponding cell list, and can be retrieved as a prediction for a GL based on submission of a cell list.

Abstract: A method is provided of limiting machine learning (ML) model training data, including receiving a maximum number of entries per grid box N. Grids are defined, each grid having grid boxes and covering a geographic area defined by a different cell list of a communication network, each grid box covering a different portion of the geographic area. Truth call records having truth data reporting geolocation (GL) data are selected from multiple received call records. A grid box is determined for each truth call record that covers a geographic area including the GL indicated by the truth call record's GL data. Truth call records are selectively assigned to their determined grid box in a fashion to not exceed the maximum number of entries per grid box N, and the truth data and signal detail data of only the assigned truth call records are output as the ML model training data.

Abstract: A machine learning method performed by a communication network monitoring device in which an incoming signaling record is received that includes radio signal attributes from a UE in the cellular communication network. A determination is made as to whether the UE incoming signaling record contains location (GPS) data. If the UE incoming signaling record contains GPS data, a machine learning model is generated for determining a location of future UEs in the communication network utilizing the GPS data and the radio signal attributes from the incoming UE signaling record. And if GPS data is not included in the UE incoming signaling record, then the geolocation for the UE is predicted using machine learning techniques utilizing a previous generated machine learning model as applied to the radio signal attributes from the incoming UE signaling record.

Abstract: A machine learning method performed by a communication network monitoring device in which an incoming signaling record is received that includes radio signal attributes from a UE in the cellular communication network. A determination is made as to whether the UE incoming signaling record contains location (GPS) data. If the UE incoming signaling record contains GPS data, a machine learning model is generated for determining a location of future UEs in the communication network utilizing the GPS data and the radio signal attributes from the incoming UE signaling record. And if GPS data is not included in the UE incoming signaling record, then first a corrected TA value is determined which is then used, along with other radio signal attributes of the UE, to determine/predict a geolocation for the UE using machine learning techniques.

Abstract: A method for monitoring spectral efficiency in a wireless network includes determining total number of resource blocks or resource elements allocated for each of one or more calls in the wireless network during a given time period and determining a corresponding total number of bits transmitted for each of the one or more calls during the given time period. A spectral efficiency metric is calculated for each of the one or more calls based at least in part on the total number of bits transmitted during the given time period and the total number of resource blocks or resource elements allocated for transmission during the given time period. A heat map for each of the one or more calls is generated based on geographic locations of the one or more calls.

Abstract: A method and system for testing of a communication network utilizing a plurality of test agents in the communication network using a deterministic decentralized scheduling of the plurality of test agents to test a single network service. Determining an optimal time period for executing a test of the single network service by each of the plurality of test agents without requiring global knowledge of the system and without requiring centralized coordination of individual agent test schedules.

Abstract: A method and system for testing of a communication network utilizing a plurality of test agents in the communication network using a deterministic decentralized scheduling of the plurality of test agents to test a single network service. Determining an optimal time period for executing a test of the single network service by each of the plurality of test agents without requiring global knowledge of the system and without requiring centralized coordination of individual agent test schedules.

Abstract: A computer method and system for analyzing performance of a 5G communication network for determining 4G traffic offload characteristics. Determine relative spectrum efficiency for data downlinks and uplinks in the 5G communication network and determine values for pairs of 4G LTE primary cells and 5G NR primary cells. A heatmap is generated utilizing the determined values for depicting 4G traffic offload characteristics in a 5G communication network wherein the generated heatmap includes a generated line depicting a connection between a LTE PCell to a 5G NR PScell wherein a width of the generated line is scalable relative to a determined percentage of 5G utilized traffic in the 5G communication system.

Abstract: A method for multi-protocol monitoring and correlation includes aggregating captured network performance data including a plurality of captured network performance metrics for a plurality of subscriber session records associated with a plurality of protocols. A first level of correlation is performed between the plurality of subscriber session records using one or more direct correlation rules. The correlated subscriber session records associated with the plurality of protocols are related to the same subscriber session. A second level of correlation is performed between the correlated subscriber session records using one or more indirect correlation rules based on the performed first level of correlation.