Most large popular web applications, like Facebook and Twitter, have been relying on large amounts of in-memory storage to cache data and offer a low response time. As the main memory capacity of clusters and clouds increases, it becomes possible to keep most of the data in the main memory. This motivates the introduction of in-memory storage systems. While prior work has focused on how to exploit the low-latency of in-memory access at scale, there is very little visibility into the energy-efficiency of in-memory storage systems. Even though it is known that main memory is a fundamental energy bottleneck in computing systems (i.e., DRAM consumes up to 40% of a server's power). In this paper, by the means of experimental evaluation, we have studied the performance and energy-efficiency of RAMCloud - a well-known in-memory storage system. We reveal that although RAMCloud is scalable for read-only applications, it exhibits non-proportional power consumption. We also find that the current replication scheme implemented in RAMCloud limits the performance and results in high energy consumption. Surprisingly, we show that replication can also play a negative role in crash-recovery.

This work has been supported by the BigStorage project, funded by the European Union under the Marie Sklodowska-Curie Actions (H2020-MSCA-ITN-2014-642963), by the Spanish Government (grant SEV2015-1305 0493 of the Severo Ochoa Program), by the Spanish Ministry of Science and Innovation (contract TIN2015-65316), by Generalitat de Catalunya (contract 2014-SGR-1051), and by the ANR KerStream project (ANR-16- CE25-0014-01). The experiments presented in this paper were carried out using the Grid5000/ALADDIN-G5K experimental testbed, an initiative from the French Ministry of Research through the ACI GRID incentive action, INRIA, CNRS and RENATER and other contributing partners (see http://www.grid5000.fr/ for details).

Peer Reviewed