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An Empirical Evaluation of How The Network Impacts The Performance and Energy Efficiency in RAMCloud

Abstract : 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 trade-offs induced by the use of high-performance networks. This paper reports on work in progress aiming to contribute to a better understanding of the main factors impacting performance 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.
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https://hal.inria.fr/hal-01376923
Contributor : Yacine Taleb <>
Submitted on : Wednesday, February 15, 2017 - 2:40:58 PM
Last modification on : Saturday, July 11, 2020 - 3:15:30 AM
Long-term archiving on: : Tuesday, May 16, 2017 - 3:25:53 PM

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  • HAL Id : hal-01376923, version 2

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Yacine Taleb, Shadi Ibrahim, Gabriel Antoniu, Toni Cortes. An Empirical Evaluation of How The Network Impacts The Performance and Energy Efficiency in RAMCloud. Workshop on the Integration of Extreme Scale Computing and Big Data Management and Analytics in conjunction with IEEE/ACM CCGrid 2017 , May 2017, Madrid, Spain. ⟨hal-01376923v2⟩

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