International Business Machines Corporation

Alan Jerome Hoffman

Emmanuel Yashchin

John Anthony Tomlin

Ilya Gluhovsky

Herbert M Lee

William Robert Pulleyblank

A diagnostic system in which, at every point in time, a forecast is made of the future response times of each EPP (nd-to-endrobelatform) probe in a battery of probes. Thresholds are established in terms of the distribution of future EPP values. The theory of Generalized Additive Models is used to build a predictive model based on a combination of a) data normally generated by network nodes, b) results of a battery of probes and c) profile curves reflecting expected response times (i.e. based on recent history) corresponding to this battery for various times of day, days of week, month of year, etc. The model is pre-computed, and does not have to be dynamically adjusted. The model produces, at regular intervals, forecasts for outcomes of various EPP probes for various horizons of interest; also, it produces thresholds for the respective forecasts based on a number of factors, including acceptable rate of false alarms, forecast variance and EPP values that are expected based on the recorded history. The system is capable of maintaining a pre-specified low rate of false alarms that could otherwise cause a substantial disturbance in network operation.

System and method of predicting future behavior of a battery of end-to-end probes to anticipate and prevent computer network performance degradation

A method, system, and data structure are provided which facilitate matrix multiplication with advantageous computational efficiency. The invention, as variously implemented as a processing system, method, or data structure in a recording medium such as a memory, has applicability to numerous fields, including linear programming, where a great deal of multiplication of large, sparse matrices is performed. The method of the invention includes the steps of creating a first submatrix block from non-zero terms of a sparse matrix, such that all of the terms within a given column of the submatrix block are form a respective column of the sparse matrix, creating a corresponding second index submatrix block of the same dimensions as the first block, such that each term of the second block identifies the position of the corresponding term of the first block within the sparse matrix, in terms of a row and column index. Finally, the method includes reordering terms of the first and second blocks correspondingly, as necessary to produce a final configuration within the first and second blocks such that all of the row indices within any given row of the second block are distinct.

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Greenwich, CT, United States of America

Alan Jerome Hoffman