Network Physics, Inc.

Opnet Technologies, Inc.

Tomas J Pavel

Han C Wen

Minh Duong-van

Mark E Crane

Ian Adam

Gilbert Grosdidier

Wei Yue

Richard Blanckenbecler

A network monitoring device configured to collect a new packet from one or more observation points of a network and to compare the new packet with a list of a number of received packets based on a packet arrival rate and to identify a duplicate packet. In particular, the number of received packets in the list is equivalent to a number of packets received within a time period, i.e. the packet arrival rate. Stated differently, the network monitoring device is to compare the new packets with received packets stored in a queue of a buffer and wherein the queue has a size based on a packet arrival rate collected at one or more observation points. In addition, the time period is further adjusted according to a threshold value. The threshold value is a variable parameter that can be adjusted to compensate for different network deployment. In one embodiment, the threshold value is a time value that is not more than a transmission time of a TCP retransmitted packet.

Method and system for identifying duplicate packets in flow-based network monitoring system

A control node of a communication network is operated at a packet bandwidth determined according to observations of performance metrics of the network at the control point. These performance metrics may be one or more of throughput, average fetch time and packet loss. The control node is operated so as to set a control bandwidth to corresponding resonance points of the performance metrics. The resonance points are determined by scanning across a range of control bandwidths, until one or more of the performance metrics is/are optimized. The packet bandwidth is set by varying an inter-packet delay time over selected communication links at the control node.

Method of reducing packet loss by resonance identification in communication networks

A baseline for internet traffic duration is established by (i) collecting internet traffic data regarding file size, server response time, packet loss and round trip time, (ii) removing from this data outliers associated with file size, server response time and packet loss per client type, and (iii) organizing any remaining data into round trip time bins according to median values of round trip time per client type. Thereafter, historical or newly collected Internet traffic data is compared against threshold values for each round trip time bin to locate duration outliers. These duration outliers are indicators of congestion and congestion episodes may be identified by the continued presence of such outliers over successive time intervals.

Method for detecting congestion in internet traffic

Predictions of congestion conditions for a traffic stream in a communication network are applied to modify an initial congestion window size for the traffic stream; and dynamic bandwidth control is thereafter applied to the traffic stream. This dynamic bandwidth control may include modulating inter-packet bandwidths of the traffic stream according to a capacity of a bottleneck in a communication path through which the traffic stream passes in the communication network. The predictions of congestion conditions may be based on monitoring packet losses and/or round trip times within the communication network for a selected period of time. The monitoring may be performed on at least one of a traffic stream-by traffic stream basis, a connection-by-connection basis, a link-by-link basis, or a destination-by-destination basis.

Method for accelerating TCP flows in communication networks with dynamic control

A moving window of data is used to determine a local baseline as a moving average of the data weighted by the number of measurements in each time interval. A next measurement associated with a next time interval is compared to a value associated with the baseline to determine an outlier. In some cases, for example where the time series of the data shows small variability around a local mean, the next measurement is compared to a multiple of the weighted moving average to determine an outlier. In other cases, for example where the time series of the data shows significant variability around the local mean, the next measurement is compared to the sum of the weighted moving average and a multiple of a moving root mean square deviation value weighted by the number of measurements in each time interval and in some cases, a damping factor.

Method for real-time auto-detection of outliers

Packet round trip times (RTT) within a communication network are measured and from those measurements information regarding congestion conditions within the network is extracted. The RTT measurements are organized into an invariant distribution (a histogram) and an analytical tool which is designed to reveal periodicity information (such as a Fourier or wavelet transform, etc.) is applied to the distribution to obtain a period plot. From this period plot, bandwidth information (indicative of the congestion conditions and/or link capacities within the network) can be obtained.

Method for determining network congestion and link capacities

Congestion within a communication is controlled by rate limiting packet transmissions over selected communication links within the network and modulating the rate limiting according to buffer occupancies at control nodes within the network. Preferably, though not necessarily, the rate limiting of the packet transmissions is performed at an aggregate level for all traffic streams utilizing the selected communication links. The rate limiting may also be performed dynamically in response to measured network performance metrics; such as the throughput of the selected communication links input to the control points and/or the buffer occupancy level at the control points. The network performance metrics may be measured according to at least one of: a moving average of the measured quantity, a standard average of the measured quantity, or another filtered average of the measured quantity. The rate limiting may be achieved by varying an inter-packet delay time over the selected communication links at the control points. The control points themselves may be located upstream or even downstream (or both) of congested nodes within the network and need only be located on only a few of a number of communication links that are coupled to a congested node within the network. More generally, the control points need only be associated with a fraction of the total number of traffic streams applied to a congested node within the network.

Method for reducing fetch time in a congested communication network

End-to-end packet losses of one or more traffic streams transmitted across a congested network may be reduced by setting the bandwidths of the corresponding traffic streams at critical values thereof at one or more control points along the network topology. The critical value of the bandwidths may be determined by monitoring buffer occupancy at the control point(s). Buffer occupancy may be determined by periodically sweeping down from a maximum bandwidth value according to a monotonically decaying exponential function.

Method for reducing packet loss by phase transition identification in communication networks

End-to-end packet losses of one or more traffic streams transmitted across a congested network may be reduced by modulating the bandwidths of the corresponding traffic streams applied to node(s) of the network from one or more control points along the network topology. This reduction in packet loss results in a reduction in fluctuations or variability of the controlled traffic streams, an increase in bandwidth utilization and a reduction in times to transmit files. The control points can be either upstream or downstream of one or more congestion points along the network. The modulation of the bandwidths are governed by a nonlinear differential equation that involves feedback of the throughput and buffer occupancy level. The control terms involve a relaxation time, coupling constant, control constant and nonlinear feedback constants.

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San Jose, CA, United States of America

Tomas J Pavel