Ilya Gluhovsky, 49Burlingame, CA

7054874, May 30, 2006

Mar 5, 2003

10/382823

Ilya Gluhovsky - Mountain View CA, US
Brian W. O'Krafka - Austin TX, US

Sun Microsystems, Inc. - Santa Clara CA

G06F 17/00

707100, 711118

One embodiment of the present invention provides a system that facilitates modeling the effects of overlapping of memory references in a queueing system model. The system receives a memory reference during execution of a queueing system model. Upon receiving the memory reference, the system determines if the memory reference generates a cache miss. If so, the system models the cache miss in a manner that accounts for possible overlapping of the cache miss with other memory references and other processor operations.

7081823, Jul 25, 2006

Oct 31, 2003

10/697273

Ilya Gluhovsky - Mountain View CA, US
Alan J. Hoffman - Greenwich CT, US
Herbert M. Lee - New Fairfield CT, US
Emmanuel Yashchin - Yorktown Heights NY, US

International Business Machines Corporation - Armonk NY

G08B 21/00

3406361, 34063612, 34063613, 34063615, 340514

A diagnostic system in which, at every point in time, a forecast is made of the future response times of each EPP (nd-to-end robe latform) 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.

7096178, Aug 22, 2006

Jul 8, 2002

10/190958

Ilya Gluhovsky - Mountain View CA, US

Sun Microsystems, Inc. - Santa Clara CA

G06F 9/45

703 22

A method for modeling a system configuration, including isotonizing an unconstrained additive model of a cache architecture to obtain an isotonic additive model for the cache architecture, wherein the isotonic additive model is of the same functional form as the unconstrained additive model, smoothing the isotonic additive model using a flat spot technique to obtain a characterization of the cache architecture, and modeling a system configuration using the characterization of the cache architecture.

7103517, Sep 5, 2006

Jul 3, 2002

10/188789

Ilya Gluhovsky - Mountain View CA, US
Brian W. O'Krafka - Austin TX, US

Sun Microsystems, Inc. - Santa Clara CA

G06F 17/10
G06F 7/60

703 2

A method for a cache architecture simulation includes obtaining a first sample set for the cache architecture using a non-stationary Gaussian field model, performing a cache architecture simulation using the first sample set to produce a first set of simulation data, and fitting a first multivariate model to the first set of simulation data.

7120567, Oct 10, 2006

Aug 20, 2002

10/224178

Ilya Gluhovsky - Mountain View CA, US

Sun Microsystems, Inc. - Santa Clara CA

G06F 17/50

703 2

A method for generating an uncertainty characterization for a system simulation model, including obtaining system simulation input, wherein the system simulation input comprises a cache simulation output, generating one of the group consisting of a deterministic uncertainty model and a stochastic uncertainty model, using the cache simulation output and the system simulation model for at least one system input configuration, and generating the uncertainty characterization for the system simulation model using one of the group consisting of the deterministic uncertainty model and the stochastic uncertainty model.

7346736, Mar 18, 2008

Oct 3, 2005

11/243353

Ilya Gluhovsky - Mountain View CA, US
David Vengerov - Sunnyvale CA, US
John R. Busch - Cupertino CA, US

Sun Microsystems, Inc. - Santa Clara CA

G06F 12/00

711118, 711133, 711134, 714701, 714702, 714704, 714718, 702108, 702119, 702120

One embodiment of the present invention provides a system that selects bases to form a regression model for cache performance. During operation, the system receives empirical data for a cache rate. The system also receives derivative constraints for the cache rate. Next, the system obtains candidate bases that satisfy the derivative constraints. For each of these candidate bases, the system: (1) computes an aggregate error E incurred using the candidate basis over the empirical data; (2) computes an instability measure I of an extrapolation fit for using the candidate basis over an extrapolation region; and then (3) computes a selection criterion F for the candidate basis, wherein F is a function of E and I. Finally, the system minimizes the selection criterion F across the candidate bases to select the basis used for the regression model.

7398191, Jul 8, 2008

Apr 20, 2005

11/111151

Ilya Gluhovsky - Mountain View CA, US
Jan L. Bonebakker - Mountain View CA, US

Sun Microsystems, Inc. - Santa Clara CA

G06F 17/50
G06F 9/45
G06F 19/00
G06F 15/00

703 2, 703 22, 702179, 702186, 709104, 709105, 709224, 709226, 714 1

One embodiment of the present invention provides a system that computes a distance metric between computer system workloads. During operation, the system receives a dataset containing metrics that have been collected for a number of workloads of interest. Next, the system uses splines to define bases for a regression model which uses a performance indicator y as a response and uses the metrics (represented by a vector x) as predictors. The system then fits the regression model to the dataset using a penalized least squares (PLS) criterion to obtain functions f,. . . , f, which are smooth univariate functions of individual metrics that add up to the regression function f, such that y=f(x)+ε=.

7401012, Jul 15, 2008

Apr 20, 2005

11/111152

Jan L. Bonebakker - Mountain View CA, US
Ilya Gluhovsky - Mountain View CA, US

Sun Microsystems, Inc. - Santa Clara CA

G06F 17/50
G06F 9/45
G06F 15/177
G06F 15/173

703 2, 703 22, 707 2, 709225, 709226, 709238, 709239

One embodiment of the present invention provides a system that characterizes computer system workloads. During operation, the system collects metrics for a number of workloads of interest as the workloads of interest execute on a computer system. Next, the system uses the collected metrics to build a statistical regression model, wherein the statistical regression model uses a performance indicator as a response, and uses the metrics as predictors. The system then defines a distance metric between workloads, wherein the distance between two workloads is a function of the differences between metric values for the two workloads. Furthermore, these differences are weighted by corresponding coefficients for the metric values in the statistical regression model.