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Enabling Cost-effective Data Processing with Smart SSD

机译：使用SMART SSD实现经济高效的数据处理

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This paper explores the benefits and limitations of in-storage processing on current Solid-State Disk (SSD) architectures. While disk-based in-storage processing has not been widely adopted, due to the characteristics of hard disks, modern SSDs provide high performance on concurrent random writes, and have powerful processors, memory, and multiple I/O channels to flash memory, enabling in-storage processing with almost no hardware changes. In addition, offloading I/O tasks allows a host system to fully utilize devices' internal parallelism without knowing the details of their hardware configurations. To leverage the enhanced data processing capabilities of modern SSDs, we introduce the Smart SSD model, which pairs in-device processing with a powerful host system capable of handling data-oriented tasks without modifying operating system code. By isolating the data traffic within the device, this model promises low energy consumption, high parallelism, low host memory footprint and better performance. To demonstrate these capabilities, we constructed a prototype implementing this model on a real SATA-based SSD. Our system uses an object-based protocol for low-level communication with the host, and extends the Hadoop MapReduce framework to support a Smart SSD. Our experiments show that total energy consumption is reduced by 50% due to the low-power processing inside a Smart SSD. Moreover, a system with a Smart SSD can outperform host-side processing by a factor of two or three by efficiently utilizing internal parallelism when applications have light trafic to the device DRAM under the current architecture.
机译：本文探讨了当前固态磁盘（SSD）架构上储存处理的好处和限制。虽然基于磁盘的储存处理未被广泛采用，但由于硬盘的特性，现代SSD在并发随机写入上提供了高性能，并且具有强大的处理器，内存和多个I / O通道，以闪存，使能存储几乎没有硬件的存储处理。此外，卸载I / O任务允许主机系统充分利用设备的内部并行性，而无需了解其硬件配置的细节。为了利用现代SSD的增强数据处理能力，我们介绍了智能SSD模型，它通过强大的主机系统对设备进行了对，能够在不修改操作系统代码的情况下处理有针对性的任务。通过隔离设备内的数据流量，该模型承诺低能耗，高并行，低主机内存占用和更好的性能。为了展示这些功能，我们在基于SATA的SSD上构建了实现此模型的原型。我们的系统使用基于对象的协议进行低级通信与主机，并扩展Hadoop MapReduce框架以支持智能SSD。我们的实验表明，由于智能SSD内部的低功耗处理，总能耗降低了50％。此外，当应用程序在当前架构下的设备DRAM中有效地利用内部并行性时，具有智能SSD的系统可以通过有效利用内部并行性来倾斜主机侧处理。

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