Annual IT operational costs continue to increase, with labor commanding a larger and larger share. For example, an IBM internal study of its own distributed infrastructure showed labor to be over 60% of the total operational cost per year1, while industry analysts estimate labor costs can be as high as 80% of overall data center costs2.
As a result, many customers are turning to private clouds, implementing such technologies as virtualization and consolidation, standardized workloads, and automation by way of self-service provisioning in an effort to reduce these costs. While only 12% of enterprises currently utilize some of these techniques, this number is expected to rise to 50% by 20123. IBM Power Systems™ cloud solutions utilize this three-pronged approach to significantly reduce IT labor costs in a private cloud environment.
Quantifying the impact that private cloud technologies have on various aspects of labor, however, has proven elusive, resulting in slower adoption rates. Customers want to know, for example, just how much these cloud solutions will affect the labor required to set-up and maintain both the physical as well as virtual infrastructure for a given deployment platform before committing resources to their implementation.
This paper describes an approach to help answer this question. We first examined the challenge of IT labor costs. We then introduced the key components of the Power Systems cloud solutions and how each tier of the private cloud stack fits within the virtualization, standardization and automation model. We proceeded to quantify the impact of virtualization by constructing a labor model that calculated the total labor hours required to set-up and maintain the overall infrastructure for both stand-alone x86 environments and virtualized Power Systems private cloud environments. Using actual customer data, the model allowed us to calculate the breakdown between the labor required for the physical and virtual infrastructure for a given number of workloads and specified time period. Next, we looked at the impact of standardization. We adjusted the model with a “clone” factor to reflect the fact that many companies implementing private clouds are using standardized workloads that can be easily copied or cloned to other virtualized servers to reduce labor costs even further. Finally, we took a look at automation by conducting a hands-on study to capture the time it took for an administrator to manually deploy an application onto a single Power Server versus using Tivoli Service Automation Manager in a Power Systems private cloud environment.
Following this overall approach yielded the following observations:
1 IBM Internal Consolidation Project
2 Source: Butler Group 2007 and http://www.itmanagement.com/blog/20070129/report-indicates-mainframe-adoption-continuing-to-grow/ (link resides outside of ibm.com)
3 Internal IBM cloud study 2009