Successfully Modeling and Simulating Systems, Part 1 Page 4


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Modeling and Simulating Future Systems

Not long ago, as part of a response to a European customer's ITT (Invitation To Tender), which is like a U.S. Government RFP, we were asked to provide a model showing that the proposed tape architecture would meet the requirements for response time. The number of tape drives in this system we estimated to be around 30-50, and even our response team was unsure of how many were going to be required. Adding to the complexity, we were bidding a tape drive that had not been announced yet, so all we had when we started were spec sheets from the vendor. The required model provided a good way for us to understand what to bid and for the customer to ensure that we based our bid on something better than a dart throw from a bunch of engineers in a room.

The first step was a visit to the hardware vendor's engineering staff for an in-depth discussion of how this tape drive worked. We needed to understand, among other details:

  • Load time
  • Position time
  • Error recovery and performance
  • Performance in general
  • Data compression algorithm
  • Hardware interface issues with Fibre Channel and Ultra-SCSI
  • Robot pick issues
  • Robot database issues
After a great deal of model development and engineering time, we created a model showing how many tape drives of what type and the number of robots needed to meet the response time lines. The model included information such as:

  • Software access request times within the application
  • Database lookup time for where the tape was in the robot
  • Pick time and movement time for the robot
  • Load and average position for the tape
  • Transfer rate of the tape drive -- including transfer rate based on compression
  • The amount of time the tape would be loaded into the drive before it was removed
Without visiting the tape vendor's engineering staff to gather the data for the creation of the simulation, it would not have been possible to justify the number of tape drives and robots. The customer provided us with much of the data needed for the simulation effort and clearly understood every aspect of the process.

The site was a weather forecast center that had done atmospheric modeling for many years, so not surprisingly, they clearly understood the scientific process of modeling and its value for predicting the future.

Page 5: So Why Doesn't Everyone Model Their Systems?

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