An empirical evaluation of how the network impacts the performance and energy efficiency in RAMCloud

In-memory storage systems emerged as a de-facto building block for today's large scale Web architectures and Big Data processing frameworks. Many research and engineering efforts have been dedicated to improve their performance and memory efficiency. More recently, such systems can leverage high-performance networks, e.g., Infiniband. To be able to leverage these systems, it is essential to understand the tradeoffs induced by the use of high-performance networks. This paper aims to provide empirical evidence of the impact of client's location on the performance and energy consumption of in-memory storage systems. Through a study carried on RAMCloud, we focus on two settings: 1) clients are collocated within the same network as the storage servers (with Infiniband interconnects); 2) clients access the servers from a remote network, through TCP/IP. We compare and discuss aspects related to scalability and power consumption for these two scenarios which correspond to different deployment models for applications making use of in-memory cloud storage systems.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), and by Generalitat de Catalunya (contract 2014-SGR-1051).Peer Reviewe