Energy-Efficient I/O Thread Schedulers for NVMe SSDs on NUMA
| Autor: | Junjie Qian, Hong Jiang, Stan Skelton, Sharad C. Seth, Witawas Srisa-an, Joseph Moore |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
010302 applied physics
Hardware_MEMORYSTRUCTURES Computer science NVM Express 020206 networking & telecommunications 02 engineering and technology Thread (computing) Energy consumption computer.software_genre 01 natural sciences Non-volatile memory Server 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Operating system Software design computer Efficient energy use |
| Zdroj: | CCGrid |
| DOI: | 10.1109/ccgrid.2017.24 |
| Popis: | 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 state-of-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 Linux-based 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 energy-efficient 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. |
| Databáze: | OpenAIRE |
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