Assessing data deduplication trade-offs from an energy and performance perspective

The energy costs of running computer systems are a growing concern: for large data centers, recent estimates put these costs higher than the cost of hardware itself. As a consequence, energy efficiency has become a pervasive theme for designing, deploying, and operating computer systems. This paper evaluates the energy trade-offs brought by data deduplication in distributed storage systems. Depending on the workload, deduplication can enable a lower storage footprint, reduce the I/O pressure on the storage system, and reduce network traffic, at the cost of increased computational overhead. From an energy perspective, data deduplication enables a trade-off between the energy consumed for additional computation and the energy saved by lower storage and network load. The main point our experiments and model bring home is the following: while for non energy-proportional machines performance- and energy-centric optimizations have break-even points that are relatively close, for the newer generation of energy proportional machines the break-even points are significantly different. An important consequence of this difference is that, with newer systems, there are higher energy inefficiencies when the system is optimized for performance.