Hyperformix, Inc.

Ca, Inc.

Paul T Barnett

Allan Drew Clarke

Timothy E Wise

Richard Gimarc

James Reynolds

Amy Carolyn Spellmann

Kenneth Zink

Annette Palmer

Theodore Lehr

Daniel M Braddock

Rajinikala Ramachandran

Carolyn West

Tom Zauli

David Leigh DuPré´

James Renyolds

Amy Carolyn Spellman

A method and apparatus for merging data acquired by two or more capture devices from two or more points in a computer system, duplicate frames are analyzed to determine the time difference between the timestamp of a first capture device and a second capture device. The disclosure compares the frames for duplicates. If the duplicate frames are the first set of duplicate frames discovered, then all previous timestamps and all subsequent timestamps from the second capture device are adjusted by the calculated time difference. If duplicate frames are again discovered, the time difference is recalculated and all subsequent frames from the second capture device are adjusted by the calculated time difference. After all the frames have been analyzed and the timestamps adjusted, the frames are merged together and put into chronological order to simulate a single capture of data encompassing all of the points where the data was collected.

System and method for aligning data frames in time

A method and apparatus for merging data acquired by two or more capture devices from two or more points in a computer system, frames are compared for duplicates, then duplicate frames are analyzed to determine the time difference between the timestamps of a first capture device and a second capture device. If the duplicate frames are the first set of duplicate frames discovered, then all previous timestamps and all subsequent timestamps from the second capture device are adjusted by the time difference. If duplicate frames are again discovered, the time difference is recalculated and all subsequent frames from the second capture device are adjusted by the recalculated time difference. After all the frames have been analyzed and the timestamps adjusted, the frames are merged together and put into chronological order to simulate a single capture of data encompassing all of the points where the data was collected.

The invention provides a method for preparing a discrete event simulation model of a production network including a client and a plurality of servers on a plurality of network tiers connected by a network comprising the steps of executing a business function from the client on the production network, gathering resource data from the plurality of servers, normalizing the resource data, calculating a consumption of resources used by the plurality of servers, estimating a transaction cost associated with the consumption of resources and building a discrete event simulation model from the transaction cost.

Method for building enterprise scalability models from production data

A method and apparatus for merging data acquired by two or more capture devices from two or more points in a computer system, duplicate frames are analyzed to determine the time difference between the timestamp of a first capture device and the timestamp of a second capture device. The invention compares the frames for duplicates. If the duplicate frames are the first set of duplicate frames discovered, then all previous timestamps and all subsequent timestamps from the second capture device are adjusted by the calculated time difference. If duplicate frames are again discovered, the time difference is recalculated and all subsequent frames from the second capture device are adjusted by the calculated time difference. After all the frames have been analyzed and the timestamps adjusted, the frames are merged together and put into chronological order to simulate a single capture of data encompassing all of the points where the data was collected.

A method is disclosed that preparing a discrete event simulation model of a production network is provided including a client and a plurality of servers connected by a network comprising the steps of initiating a single business function trace test on the production network; gathering network flow trace data from the network; deriving a transaction summary from the network flow trace data; initiating a single business function load test on the production network; gathering resource data from the plurality of servers; normalizing the resource data; gathering web log data from at least one server from the plurality of servers; calculating a number of business functions running on the production network; calculating a consumption of resources used by the plurality of servers; calculating a business function cost associated with the consumption of resources; creating a business function profile from the transaction summary and the business function cost; and building a discrete event simulation model from the business function profile.

Method for building enterprise scalability models from load test and trace test data

A method of semi-automatic data collection, data analysis and model generation for performance analysis of computer networks and software is provided. The invention provides a graphical user interface which indicates the overall data flow in the performance analysis process and guides the user through the proper sequence of steps in that process. Underneath the graphical user interface, the invention provides software tools which include novel data pipeline for transformation of network traces, resource data and application data into organized hash tables, and further into spreadsheets for introduction into computer network simulation programs. The invention also provides novel algorithms for recognizing transaction and parentage between transactions from low level network trace data. The invention further provides novel methods of visualization of trace data and transaction and parentage associations.

Method of semi-automatic data collection, data analysis, and model generation for the performance analysis of enterprise applications

Method and apparatus for separating background utilization from transaction resource consumption to simulate resource costs on system servers including a computer application server is taught. Transaction throughput combined with resource consumption data. Regression analysis on the resulting data set provides information useful for determining performance and for creating discrete event simulations. In a first embodiment, data from an application server is combined with the application transaction log to find a background utilization on the application servers and to simulate transaction resource cost with a background load. In a second embodiment, data from servers other than the application server are combined with the application transaction log to estimate the resource cost per transaction for a given resource in the system. In a third embodiment, data from servers other than the application server are combined with the application transaction log to simulate resource cost for a given resource based on transaction throughput.

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Collin County, TX, United States of America

Paul T Barnett