An empirical evaluation of how the network impacts the performance and energy efficiency in RAMCloud
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Cita com:
hdl:2117/107503
Tipus de documentText en actes de congrés
Data publicació2017
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
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ProjecteCOMPUTACION DE ALTAS PRESTACIONES VII (MINECO-TIN2015-65316-P)
BigStorage - BigStorage: Storage-based Convergence between HPC and Cloud to handle Big Data (EC-H2020-642963)
COMPUTACION DE ALTAS PRESTACIONES VII (MINECO-TIN2015-65316-P)
BigStorage - BigStorage: Storage-based Convergence between HPC and Cloud to handle Big Data (EC-H2020-642963)
COMPUTACION DE ALTAS PRESTACIONES VII (MINECO-TIN2015-65316-P)
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 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.
CitacióTaleb, Y., Ibrahim, S., Antoniu, G., Cortés, A. An empirical evaluation of how the network impacts the performance and energy efficiency in RAMCloud. A: IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. "2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing: 14-17 May 2017, Madrid, Spain: proceedings". Madrid: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1027-1034.
ISBN978-1-5090-6610-0
Versió de l'editorhttp://ieeexplore.ieee.org/document/7973811/
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