IT budgeting has always been shaky. This is because IT is considered to be a long term investment, and IT Administrators must provision for the present and the future. While present requirements may be fairly quantifiable and controllable, future requirements are based on estimates, expectations, and predictions that seem deceptive and rendered useless in the face of rapid technological changes that are shaping the world today. It creates an argument for acquisition of tools that will cut through the deceptions (perpetuated by predictions) and to arrive at a meaningful, grounded, realistic view of computing costs.

Cloud computing and virtualization shift the budgeting focus from acquisition of hardware and software to use of hardware and software, CAPEX to OPEX. Cost sheets are drawn up on the actual costs of hiring / using infrastructure, storage space or application instances. Savings are seen to arise from:

  1. Reduced spend on hardware,
  2. Lower power demands for server room,
  3. Reduced floor and rack space use
  4. Improved server utilization,
  5. No investment in backup software, operating systems and database licensing,
  6. Improve application performance on state of the art systems, and
  7. Improved application availability.

However, users who approach virtualization with unrealistic expectations are in for some nasty surprises. A detailed reporting on specific costs of virtualization will help avoid this problem.  But, many IT managers do not know how to track and report costs of virtualization projects. This is compounded by the fact that many organizations do not chargeback for these projects and do not have the wherewithal or knowledge to do so.

Those who are in the know of things suggest four ways of arriving at virtualization costs.

  1. IT managers can use Activity based costing to arrive at a consumption based reporting model. For instance, the unit with the maximum consumption pattern would be assigned the highest proportion of the resource cost.
  2. A tiered pricing model can be used to arrive at the resource requirements and usage within the organization. For instance, the main office may require high bandwidth and additional support while the branches may require a lower bandwidth and minimal support.
  3. The service costs model can be used to arrive at support service density requirements based on levels of usage per location till all locations are covered.  For instance, location “A” uses the service intensely vis-à-vis location “B”, the costs allocated to location “A” will be higher than costs allocated to location “B”.
  4. The weighting model can be used to divide costs according to an external calculation of demands from a business unit.