In the Peer Incite on June 11 2009, the community discussed the installation of in-line compression at two installations. Both used compression technology from IBM Real-time Compression. This financial analysis is based on the overall benefits of compression as discussed in the Peer Incite, together with data center storage metrics that Wikibon has developed. The result is a case study based on a blend between actual field technology benefits applied to a typical storage environment.
Our analysis shows the following:
- For an initial CAPEX investment of $214,027, applying compression on primary storage returns $387,269 over a four-year period.
- This initial CAPEX requirement is lower than CAPEX costs that would have been needed to fund a storage infrastructure without compression.
- The savings represents CAPEX reduction from avoiding incremental storage purchases and the OPEX savings associated with lower maintenance, power, and cooling costs.
- The overall business case for compressing primary data is compelling, with an NPV for the project of $138,690, an IRR of 695%, and a break-even of nine months.
The core assumptions are provided in the footnote for tables 4 and 5. These are based on metrics gathered by the Wikibon Energy Lab, a service that analyzes data center energy consumption for clients. All metrics are normalized to a metric of dollar-cost per terabyte per year ($/TB/Yr).
The case uses Shopzilla's environment as a benchmark and assumes 300 terabytes of storage are installed and the planned requirement is to add an additional 132 TB of storage. Our assumption is this additional storage is needed to accommodate the lag between project kickoff and the time when compression becomes effectively implemented. There is pressure on budgets and space, power, and cooling requirements in the data center.
Table 1 shows the "as-is" case or current storage plan (prior to compression being introduced), with a capex requirement of $380,959, and additional opex of $78,430. The opex covers the maintenance, power and space for an additional 132 terabytes of storage as shown in the #TB column of the table (e.g. CAPEX of $380,859 for 132 TB of storage).
Table 2 shows the revised budget if compression is used. The key assumptions are:
- The implementation will take three months to complete.
- The reduction in data stored achieved will be 50%.
- The data reduction will be achieved over a one year period.
- Because of the three month implementation period, an additional 33 terabytes is assumed to be required.
- The total amount of data that will be compressed over the one-year period is 198 TB of the 333 TB installed.
The assumptions used are conservative in order to challenge the business case for compression.
Table 3 shows the financial analysis of the IT benefits of implementing in-line compression. For an initial CAPEX cost of $214,027, the project returns $387,269 over a four-year period. This savings represents the CAPEX savings (delta between Table 1 and Table 2 CAPEX) and the OPEX savings (delta of Table 1 and Table 2 OPEX over a four-year period). This represents a 48% reduction in the storage budget. The NPV for the project is $138,690, the IRR is 695%, and the breakeven is nine months.
The factors not taken into account in the financial analysis are:
- The improvement in performance expected in the arrays because of the reduced bandwidth (the number of IOs would be the same). This would be reflected in the financial plan by an adjustment to the ratio of Tier1, 2 and 3 storage that would be purchased in the future.
- The additional risk and complexity that introducing an additional layer to the storage system. This would be reflected by additional staff and additional DR precautions.
- The improvement in backup window of 25% that would be expected.
- The improvement in the in-line backup data de-duplication system that is currently installed (would offset future upgrades).
- The risk that the improvement of 50% would not be achieved in this particular installation.
Action Item: The overall experience of early installers of in-line compression have been positive. The business case for in-line compression is very strong. IT executives under budget, space and power pressures should look hard at this new technology. It has the potential to reduce the cost of all file-based primary storage with improved performance and should in many instances be placed higher in the priority queue than data de-duplication for backup.