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1 KerData - Scalable Storage for Clouds and Beyond Inria Rennes – Bretagne Atlantique , IRISA D1 - SYSTÈMES LARGE ÉCHELLE 2 ASCOLA - Aspect and Composition Languages Inria Rennes – Bretagne Atlantique , LS2N - Laboratoire des Sciences du Numérique de Nantes 3 UPC - Technical University of Catalonia 4 BSC - Barcelona Supercomputing Center - Facultat Barcelona

Abstract : 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 RAM-Cloud — 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.

Keywords : RAMCloud In-memory storage Performance eval- uation Energy efficiency





Author: Yacine Taleb - Shadi Ibrahim - Gabriel Antoniu - Toni Cortes -

Source: https://hal.archives-ouvertes.fr/



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