A procedure for evaluating a network, and a system, apparatus, and computer program that operate in accordance with the procedure. The procedure includes aggregating packet information from one or more sources in a network, and executing a correlation algorithm to determine traffic flow information based on the packet information. The aggregating includes obtaining information from a header of a packet being communicated in the network, in one example embodiment. In another example, the executing includes tracing a traffic flow from a source node to a destination node, and the tracing includes determining, based on the packet information, each link by which the traffic flow is communicated from the source node to the destination node.

An apparatus is provided for measuring the performance of a mobile communication system. The apparatus includes two measurement mobile interfaces and a measurement probe. The probe is connected to the backhauling interface of a base station of the system. Then, one measurement mobile interface transmits packets to the other measurement mobile interface via the base station. These packets are received at the base station, forwarded to the packet gateway of the system, sent back to the same base station, and finally received at the destination measurement mobile interface. Since the measurement probe belongs to the same apparatus as the mobile interfaces, it may detect the packets as transmitted/received at the backhauling interface. The probe then generates performance parameters relating to the packets as transmitted/received by the mobile interfaces and/or as detected at the backhauling interface. The system performance is then measured based on such parameters.

A method is provided of managing a non-static collection of machines. A first client machine runs a first communication protocol. The non-static collection of machines includes a first linear communication orbit, the first linear communication orbit comprising a sequence of machines that run the first communication protocol, and a second linear communication orbit, the second linear communication orbit comprising a sequence of machines that run a second communication protocol distinct from the first communication protocol. The first client machine receives an instruction from a server to install the second communication protocol, installs the second communication protocol, and then submits a registration request to the server. The first client machine receives, from the server, contact information of a list of potential neighbors. The first client machine then, proactively constructs and maintains a respective local segment of the second linear communication orbit.

A method is provided of managing a non-static collection of machines. A first client machine runs a first communication protocol. The non-static collection of machines includes a first linear communication orbit, the first linear communication orbit comprising a sequence of machines that run the first communication protocol, and a second linear communication orbit, the second linear communication orbit comprising a sequence of machines that run a second communication protocol distinct from the first communication protocol. The first client machine receives an instruction from a server to install the second communication protocol, installs the second communication protocol, and then submits a registration request to the server. The first client machine receives, from the server, contact information of a list of potential neighbors. The first client machine then, proactively constructs and maintains a respective local segment of the second linear communication orbit.

This disclosure falls into the field of voice communication systems, more specifically it is related to the field of voice quality estimation in a packet based voice communication system. In particular the disclosure provides a method and device for 5 reducing a prediction error of the voice quality estimation by considering the content of lost packets. Furthermore, this disclosure provides a method and device which uses a voice quality estimating algorithm to calculate the voice quality estimate based on an input which is switchable between a first and a second input mode.

Example apparatus disclosed herein are to obtain, from a network monitor, an address associated with one or more network communications used to convey a media stream to a first input device of a plurality of input devices in communication with a media device, query one or more data structures based on the address to identify a first input source of the media device, the one or more data structures to map respective ones of the plurality of input devices to corresponding ones of a plurality of input sources of the media device, the plurality of input sources including the first input source, obtain a watermark from a meter that is to monitor an output of the media device, and identify the first input source of the media device as associated with media presented by the media device in response to determination that the watermark corresponds to the media stream.

Example apparatus disclosed herein are to obtain, from a network monitor, an address associated with one or more network communications used to convey a media stream to a first input device of a plurality of input devices in communication with a media device, query one or more data structures based on the address to identify a first input source of the media device, the one or more data structures to map respective ones of the plurality of input devices to corresponding ones of a plurality of input sources of the media device, the plurality of input sources including the first input source, obtain a watermark from a meter that is to monitor an output of the media device, and identify the first input source of the media device as associated with media presented by the media device in response to determination that the watermark corresponds to the media stream.

The disclosure herein describes predicting potential impact of issues reported in incident ticket data on infrastructure element. A ticket manager component includes an impact model utilizing machine learning to analyze real-time event and metric data with incident-related data to generate predicted impact data. The predicted impact data identifies potentially impacted infrastructure elements, such as, potentially impacted users, predicted infrastructure components impacted by the issue and/or an updated time-period associated with the issue. The ticket manager component creates labeled incident tickets by updating user-generated incident tickets with additional data generated by the impact model, including predicted impact data and/or additional details associated with the issue. The labeled incident tickets are provided back to the model as training data to further refine predictions generated by the model.

The disclosure herein describes predicting potential impact of issues reported in incident ticket data on infrastructure element. A ticket manager component includes an impact model utilizing machine learning to analyze real-time event and metric data with incident-related data to generate predicted impact data. The predicted impact data identifies potentially impacted infrastructure elements, such as, potentially impacted users, predicted infrastructure components impacted by the issue and/or an updated time-period associated with the issue. The ticket manager component creates labeled incident tickets by updating user-generated incident tickets with additional data generated by the impact model, including predicted impact data and/or additional details associated with the issue. The labeled incident tickets are provided back to the model as training data to further refine predictions generated by the model.

A performance indicator monitoring system (PIMS) receives a plurality of operational measurements (OMs) for each performance indicator (PI), which may be stored on a first storage. A rate of access by a monitoring system is determined for the PI. The rate may be determined, for example, related to a counter of the PI. An enriched dataset related to a subset of the plurality of OMs is generated, upon determination that the selected PI rate of use is higher than a primary threshold value. The generated enriched dataset may be stored in a second storage. In some embodiments, a component of the PIMS, such as a processing unit, is determined. A usage level of the component is then determined, and the enriched dataset is generated if the usage level of the component is below a secondary threshold.