A Computer Network Service Providing System including Self Adjusting Volume enforcement functionality and methods for diminishing or minimizing volume leakage.

The technology disclosed relates to building ensemble analytic rules for reusable operators and tuning an operations monitoring system. In particular, it relates to analyzing a metric stream by applying an ensemble analytical rule. After analysis of the metric stream by applying the ensemble analytical rule, quantized results are fed back for expert analysis. Then, one or more type I or type II errors are identified in the quantized results, and one or more of the parameters of the operators are automatically adjusted to correct the identified errors. The metric stream is further analyzed by applying the ensemble analytical rule with the automatically adjusted parameters.

The technology disclosed relates to building ensemble analytic rules for reusable operators and tuning an operations monitoring system. In particular, it relates to analyzing a metric stream by applying an ensemble analytical rule. After analysis of the metric stream by applying the ensemble analytical rule, quantized results are fed back for expert analysis. Then, one or more type I or type II errors are identified in the quantized results, and one or more of the parameters of the operators are automatically adjusted to correct the identified errors. The metric stream is further analyzed by applying the ensemble analytical rule with the automatically adjusted parameters.

A stream processing system in a first zone of a telecommunication network may obtain at least one policy for processing trace data of virtual network functions (VNFs) in the first zone, and obtain the trace data of the VNFs from a data distribution platform of the telecommunication network, where the trace data is published in accordance with a topic to the data distribution platform by the VNFs, and where the stream processing system comprises a subscriber to the topic. The first stream processing system may additionally forward at least a first portion of the trace data to a second stream processing system of the telecommunication network in accordance with the at least one policy, where the first portion comprises less than all of the trace data, and where the second stream processing system is for a region of the telecommunication network that includes the first zone and a second zone.

Some embodiments provide a method of identifying packet latency in a software defined datacenter (SDDC) that includes a network and multiple host computers executing multiple machines. At a first host computer, the method identifies and stores (i) multiple time values associated with several packet processing operations performed on a particular packet sent by a first machine executing on the first host computer, and (ii) a time value associated with packet transmission through the SDDC network from the first host computer to a second host computer that is a destination of the particular packet. The method provides the stored time values to a set of one or more controllers to process to identify multiple latencies experienced by multiple packets processed in the SDDC.

In one embodiment, a network flow sampling system includes data communication apparatus, which includes packet processing circuitry configured to process data packets of multiple network flows, and an adaptive policer configured to sample respective ones of the data packets of respective ones of the network flows yielding sampled data, while applying sampling fairness among the respective network flows, wherein at least one of the data packets from each of the respective network flows is sampled.

First and second processors that are in communication with each other are disclosed. The first processor includes a sampling controller, a sampling circuit, and a data flow controller. The sampling controller is configured to receive multiple identifiers and corresponding enable signals associated with data that is to be transmitted to or received from the second processor, and generate an identification signal and a sampling signal based on one of the identifiers and the corresponding enable signal. The sampling circuit is configured to sample multiple data counts to generate corresponding sampled counts based on the identification signal and the sampling signal. The data flow controller is configured to generate a control signal based on the identifiers, the corresponding enable signals, the data counts, and the corresponding sampled counts to control data flow between the first and second processors.

There is provided a method for adaptive bitrate (ABR) adjustments in an IP network before making upshift of ABR level of media streams like video for live Over the Top (OTT) distribution. Example methods may include initiating, at a first time interval, probing of the IP network to determine if a first candidate bitrate is applicable, where the first candidate bitrate is greater than a preset bitrate of a client device data stream, determining that the candidate bitrate is applicable, increasing a transfer bitrate of the client device data stream, and initiating, at a second time interval, probing of the IP network to determine if a second candidate bitrate is applicable, where the second candidate bitrate is greater than the first candidate bitrate.

The disclosed subject matter is directed towards a clear channel assessment procedure based on a common preamble, such as for use with 3GPP and IEEE 802.11 technologies, or any other radio technology, including for use in the 6 GHz band. Detection of the common preamble is based on detecting known sequences in signal part, which can be detected without decoding the preamble's payload (channel) part to determine an ongoing transmission's duration. If an ongoing transmission is detected, subsequent energy detection monitoring is performed to determine when transmission ends, which can use a different energy detection threshold from what is used in the initial clear channel assessment's energy detection. The technology facilitates the usage of different sampling rates by different radio technologies that work concurrently in the same unlicensed band, by correlating a received preamble with a stored preamble that accounts for deterministic distortions arising from the different sampling rates.

In one embodiment, a supervisory service for a software-defined wide area network (SD-WAN) obtains telemetry data from one or more edge devices in the SD-WAN. The service trains, using the telemetry data as training data, a machine learning-based model to predict tunnel failures in the SD-WAN. The service receives feedback from the one or more edge devices regarding failure predictions made by the trained machine learning-based model. The service retrains the machine learning-based model, based on the received feedback.