Nortel Networks Corporation

Malcolm Edward Carter

Otakar Fojt

Michael Maurice Dodson

Kevin John Warbrick

Peter G Hamer

David Ireland

Christopher Hobbs

Jon Weil

Richard Clegg

Jason Levesley

Arthur Clune

Congestion control in a communications network, e.g. an Internet Protocol (IP) network, is effected by controlling the egress rate of traffic from the output buffer of the system routers or switches. The traffic egressing from an output buffer is sampled at sequential intervals to determine a bit rate at each interval. An autocorrelation function is calculated from these samples and is used to provide an estimated statistical measure or parameter indicative of congestion and related to a long range dependence of the traffic. A calculation is then made as to whether an increase or a decrease in the buffer output rate would result in a decrease in the congestion parameter, and the buffer output rate is adjusted accordingly.

Reducing network traffic congestion

Communications data such as traffic levels in a communications network is analysed using techniques adapted from the study of chaos. Future values of a series of communications data are predicted and an attractor structure is determined from the communications data. This enables the communications processes to be monitored, controlled and analysed. Action can be taken to modify the communications process using the results from the prediction and attractor structure to reduce costs and improve performance and efficiency. These methods may also be used for product data from manufacturing processes. An algorithm bank is compiled containing prediction algorithms suitable for different types of data series, including those exhibiting deterministic behaviour and those exhibiting stochastic behaviour. Recent past values of a data series are taken and assessed or audited in order to determine which of the algorithms in the bank would provide the optimal prediction. The selected algorithm is then used to predict future values of the data series. The assessment or auditing process is carried out in real time and a prediction algorithm selected using a &ldquo;smart switch&rdquo; such that different algorithms are used for different stages in a given series as required. This enables good prediction of data series which change in nature over time to be obtained.

Predicting values of a series of data

Product data from a manufacturing process is analysed using techniques adapted from the study of chaos. Future values of a series of product data are predicted and an attractor structure is determined from the product data. This enables the manufacturing process to be monitored, controlled and analysed. Action can be taken to modify the manufacturing process using the results from the prediction and attractor structure to reduce costs and improve performance and efficiency.

Dynamic prediction for process control

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Herts, United Kingdom

Malcolm Edward Carter