Visible to the public Energy-efficient I/O Thread Schedulers for NVMe SSDs on NUMAConflict Detection Enabled

TitleEnergy-efficient I/O Thread Schedulers for NVMe SSDs on NUMA
Publication TypeConference Proceedings
Year of Publication2017
AuthorsJunjie Qian, Hong Jiang, Witawas Srisa-an, Sharad Seth
Conference NameCCGrid '17 Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
Date Published05/2017
PublisherIEEE Press Piscataway, NJ, USA ©2017
Conference LocationMadrid, Spain
ISBN978-1-5090-6610-0
KeywordsAugust'17, CMU, Metrics, Race Vulnerability Study and Hybrid Race Detection, Scalability and Composability
Abstract

Non-volatile memory express (NVMe) based SSDs and the NUMA platform are widely adopted in servers to achieve faster storage speed and more powerful processing capability. As of now, very little research has been conducted to investigate the performance and energy efficiency of the stateof-the-art NUMA architecture integrated with NVMe SSDs, an emerging technology used to host parallel I/O threads. As this technology continues to be widely developed and adopted, we need to understand the runtime behaviors of such systems in order to design software runtime systems that deliver optimal performance while consuming only the necessary amount of energy. This paper characterizes the runtime behaviors of a Linuxbased NUMA system employing multiple NVMe SSDs. Our comprehensive performance and energy-efficiency study using massive numbers of parallel I/O threads shows that the penalty due to CPU contention is much smaller than that due to remote access of NVMe SSDs. Based on this insight, we develop a dynamic "lesser evil" algorithm called ESN, to minimize the impact of these two types of penalties. ESN is an energyefficient profiling-based I/O thread scheduler for managing I/O threads accessing NVMe SSDs on NUMA systems. Our empirical evaluation shows that ESN can achieve optimal I/O throughput and latency while consuming up to 50% less energy and using fewer CPUs.

DOI10.1109/CCGRID.2017.24
Citation Keynode-36384

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