Abstract: Systems and methods for calculating and presenting confidence interval(s) for key performance indicator(s) (KPIs) are described. For example, in some embodiments, a method may include identifying vectors representing network events observed by a network monitoring system, each vector including: a dimension, an indication of a sampling ratio with which a respective event was observed, and a value associated with the dimension. The method may also include calculating a KPI corresponding to the observed events for the dimension based, at least in part, upon the values. The method may further include calculating a confidence associated with the KPI, based, at least in part, upon the sampling ratios. In some cases, events may be observed with different sampling ratios. Additionally or alternatively, sampling ratios may include adaptive sampling ratios controlled by the network monitoring system in response to network or resource loading (e.g.

Abstract: Systems and methods for data integrity scoring and visualization for network and customer experience monitoring are described. In some embodiments, a method may include receiving a first set of vectors, each vector representing a network event generated by a network testing system, each vector including a plurality of dimensions and a first plurality of values, each value associated with a corresponding one of the dimensions. The method may also include identifying a second set of vectors representing at least a portion of the network events as observed by a network monitoring system, each vector in the second set of vectors including the plurality of dimensions and a second plurality of values. The method may further include calculating a presence score as a ratio between a number of vectors in the second and first sets of vectors, and/or an accuracy score as a measure of a discrepancy between corresponding values.

Abstract: Systems and methods for the adaptive monitoring of telecommunications networks are described. In some embodiments, a method may include monitoring traffic transmitted through a mobile telecommunications network (e.g., 3G, 4G, LTE, etc.) and identifying a monitoring rule. The monitoring rule may associate a selected portion of the traffic (e.g., based on content type, source, destination, transport protocol, session, etc.) with a monitoring sampling ratio, and the monitoring sampling ratio may determine a fraction of the selected portion of the traffic usable to provide one or more performance indicators (e.g., service indicators, network congestion, connection maintenance, service quality, and/or network availability). The method may further include modifying the monitoring sampling ratio in response to a change in network monitoring conditions (e.g., one or more probes operating at or near capacity, scheduled maintenance events, peak traffic times, etc.

Abstract: Systems and methods for the adaptive monitoring of telecommunications networks are described. In some embodiments, a method may include monitoring traffic transmitted through a mobile telecommunications network (e.g., 3G, 4G, LTE, etc.) and identifying a monitoring rule. The monitoring rule may associate a selected portion of the traffic (e.g., based on content type, source, destination, transport protocol, session, etc.) with a monitoring sampling ratio, and the monitoring sampling ratio may determine a fraction of the selected portion of the traffic usable to provide one or more performance indicators (e.g., service indicators, network congestion, connection maintenance, service quality, and/or network availability). The method may further include modifying the monitoring sampling ratio in response to a change in network monitoring conditions (e.g., one or more probes operating at or near capacity, scheduled maintenance events, peak traffic times, etc.

Abstract: Systems and methods for intelligent and scalable network monitoring using a hierarchy of devices are described. In some embodiments, a method may include monitoring network traffic having a first data rate and identifying a portion of that traffic. For example, the network traffic may include packet-based traffic in a mobile telecommunications network (e.g., 3G, 4G, LTE, etc.), and identifying the portion of the traffic may include identifying high-value and/or low-value portions as determined by one or more traffic identification rules (e.g., by user, session, transport protocol, type of content, etc.). The method may also include selecting a network analyzer to receive the high (or low) value traffic, and which may not be capable of and/or configured to analyze packets at the first data rate. Accordingly, the method may further include transmitting the identified traffic portion to the selected analyzer with a second data rate smaller than the first data rate.

Abstract: Systems and methods for data integrity scoring and visualization for network and customer experience monitoring are described. In some embodiments, a method may include receiving a first set of vectors, each vector representing a network event generated by a network testing system, each vector including a plurality of dimensions and a first plurality of values, each value associated with a corresponding one of the dimensions. The method may also include identifying a second set of vectors representing at least a portion of the network events as observed by a network monitoring system, each vector in the second set of vectors including the plurality of dimensions and a second plurality of values. The method may further include calculating a presence score as a ratio between a number of vectors in the second and first sets of vectors, and/or an accuracy score as a measure of a discrepancy between corresponding values.

Abstract: Systems and methods for calculating and presenting confidence interval(s) for key performance indicator(s) (KPIs) are described. For example, in some embodiments, a method may include identifying vectors representing network events observed by a network monitoring system, each vector including: a dimension, an indication of a sampling ratio with which a respective event was observed, and a value associated with the dimension. The method may also include calculating a KPI corresponding to the observed events for the dimension based, at least in part, upon the values. The method may further include calculating a confidence associated with the KPI, based, at least in part, upon the sampling ratios. In some cases, events may be observed with different sampling ratios. Additionally or alternatively, sampling ratios may include adaptive sampling ratios controlled by the network monitoring system in response to network or resource loading (e.g.

Abstract: Video data packets transmitted through a wireless network are captured by a network monitoring system. Video data sessions are detected from the video data packets. Key parameters are identified within the video data packets, such as video bit rate, resent or failed video packets, and video session duration. A Quality of Experience (QoE) is determined for some or all users associated with the video sessions based upon the key parameters. A header extension is added to the video data packets by a transcoding system. The header extension includes data associated with original and transcoded video data packets. The network monitoring system monitors the header extension and evaluates the effect of video transcoding upon the overall QoE for users. The monitoring system provides feedback to a transcoding policy engine based upon the effect of transcoding upon QoE.

Abstract: Systems and methods for monitoring over-the-top adaptive video streaming in a network are described. In an embodiment, a method may include identifying a protocol used in the communication based, wherein the protocol may be one among a plurality of different adaptive video streaming protocols. Video sessions using a plurality of different protocols are monitored and video session data collected. User Quality of Experience (QoE) and Key QoE Indicators (KQI) are generated for the network. Network operators may access video session data to analyze initial buffering delays, rebuffering delays, bandwidth changes, and gaps during the video session.

Abstract: Video data packets transmitted through a wireless network are captured by a network monitoring system. Video data sessions are detected from the video data packets. Key parameters are identified within the video data packets, such as video bit rate, resent or failed video packets, and video session duration. A Quality of Experience (QoE) is determined for some or all users associated with the video sessions based upon the key parameters. A header extension is added to the video data packets by a transcoding system. The header extension includes data associated with original and transcoded video data packets. The network monitoring system monitors the header extension and evaluates the effect of video transcoding upon the overall QoE for users. The monitoring system provides feedback to a transcoding policy engine based upon the effect of transcoding upon QoE.

Abstract: The data rate for video data being transmitted through a wireless network is adjusted based upon cell congestion levels. A network monitoring system identifies the congestion levels in network cells based upon data traffic captured from network interfaces. When a cell congestion level reaches a first level, an alert is sent to a video transcoding device. The video transcoding device adjusts the data rate for video data being sent to one or more subscribers in the congested cell. The data rate adjustments may be based upon a subscriber profile or a user equipment type. When cell congestion levels drop below a second threshold, the monitoring system sends a second alert indicating that the data rate can be increased.