There have been tools available to inject traffic into a system and monitor that data for a while. There have also been tools available to allow such monitoring on a one-off basis when a problem is known or suspected. This doesn’t, however, really go far enough in overall system monitoring since both approaches would usually be used retrospectively. It would seem, however, that the tool manufacturers have started to fill this gap in the performance monitoring market.

The Compuware “Vantage” toolset is a good example of this class of tool. This suite has included a strong range of monitoring, investigation and analysis tools so some time. They have now added a “passive monitoring” facility that starts to meet the requirement to be able to monitor the performance of all client transactions. If you would like to know more about this particular toolset then Compuware have published a fact sheet about it here.

If you take the first path then good luck to you, and I would be very interested in knowing how it turns out for you. The issue is that a business user, and in fact most technical people, just do not wish to work with an “average” user and then work out variations. A business is structure as departments with functions. If the business has measurements at all, and thankfully most do, it will be in terms of departmental sizes and throughputs. There are also likely to monitor the volume
of business that is done, and if you are lucky have documented processes for how this business is performed. Thankfully the tools needed to convert these figures into what you need are already built into a performance model. The following discussion, therefore, assume that what you will have been given is user levels and business volume projections./p>

1. Start with the year-on-year departmental sizes, with each department modelled as one or more sections. If you only have past figures then provide a simple basis for projecting this forward.
2. Fill in the likely levels of variation in system usage, based on either you own knowledge or on discussion with people who know the likely behaviour of the business.
3. Fill in the data volumes generated by the different functions, and there needs to be a relationship between the data or function volumes and the figures supplied by the business.
4. Once the model calculates, this will provide a projection of the numbers of functions and items of data that will be produced year-on-year. Compare this to the volumes that you have been provided by the business. If there is an approximate correlation then adjust the figures to improve the match and use this as the behaviour profile.
5. If there is a large mismatch then you may have a misunderstanding about the figures that you have been provided with, or it is possible the business estimates need refinement. This will need to be discussed with the people who own the business figures. The process can in itself add value for the business. If they are trying to do levels of business that there people and business processes can’t support then this may manifest as complaints about system performance. (“I can’t meet my targets because the system is too slow.” may actually be “I can’t meet my targets because they are unrealistic.”)

The process of generating performance model figures from business or systems figures is one of approximation. The target is not a precise and accurate model, but one which is of the correct order and believable. The idea with this sort of modelling is to 1% accuracy, but order of magnitude estimation. If you need help in starting a performance modelling exercise then call me on +44 (0)7887 53608, or e-mail me. I will be happy to have a talk about the most appropriate approach for finding a usable set of figures, and the levels of accuracy needed.