Deciding On Which Modelling Approach To Adopt

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Deciding On Which Modelling Approach To Adopt

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Deciding on which modelling approach to adopt can be difficult. In this article we lay down our point of view to help you decide which modelling approach might be appropriate. Please note that where you sit in the development or support life cycle might not necessarily align with the phases we have mentioned below so exercise your judgement when deciding on a particular course of action.

  • During the Design Phase – Your choice of Modelling approaches is limited by the lack of empirical data (data collected from production environments e.g. cpu utilization, memory utilization, transactions per hour, etc.). At the design phase you are just starting to form a view of what the system should look like or possibly even high a high level design available which illustrates the different components of the system along with potential high level capacity plans. At this point of the System Development or Support Life Cycle you could use either the Analytical Modelling techniques or the Simulation Modelling techniques to model the system and understand the change in behavior of the system for change in various input parameters.
  • During the Build Phase – Your choice of Modelling approaches is limited by the lack of empirical data (data collected from production environments e.g. cpu utilization, memory utilization, transactions per hour, etc.). At this point of the System Development or Support Life Cycle you could use either the Analytical Modelling techniques or the Simulation Modelling techniques. If you have the ability to perform small scale tests in your development or sandpit environment there is the potential to use the Statistical Modelling techniques provided by VisualizeIT to model the system and understand the change in behavior of the system for change in various input parameters.
  • During the Test Phase – At this point of the Development or Support Life Cycle you have access to empirical data (data collected from production environments e.g. cpu utilization, memory utilization, transactions per hour, etc.) generated from running various performance tests. You should be able to use the Statistical Modelling techniques feeding into it the empirical data to understand the behavior of the system and the constraints that impact overall system scalability. There’s no reason why one can’t use the Analytical or Simulation Modelling techniques as well to model the system and understand the change in behavior of the system for change in various input parameters.
  • Post Go-Live – At this point of the Development or Support Life Cycle you have access to empirical data (data collected from production environments e.g. cpu utilization, memory utilization, transactions per hour, etc.) generated by the application running in production. You should be able to use the Statistical Modelling techniques feeding into it the empirical data to understand the behavior of the system and the constraints that impact overall system scalability. There’s no reason why one can’t use the Analytical or Simulation Modelling techniques as well to model the system and understand the change in behavior of the system for change in various input parameters.

We hope this article has helped you understand your choice of modelling approaches and what your potential options are for designing performance models depending on which part of the System Development and Support Life Cycle you are currently in.

Modelling Solution: VisualizeIT offers access to a bunch of Analytical Models, Statistical Models and Simulation Mcropped-visualize_it_logo__transparent_090415.pngodels. Access to all the Analytical (Mathematical) models is free. We recommend you try out the Analytical models at VisualizeIT which are free to use and drop us a note with your suggestions, input and comments. You can access the VisualizeIT website here and the VisualizeIT modelling solution here –VisualizeIT.

 

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