While (1) serverless computing is emerging as a popular form of cloud execution, datacenters are going through major changes: (2) storage dissaggregation in the system infrastructure level and (3) integration of domain-specific accelerators in the hardware level. Each of these three trends individually provide significant benefits; however, when combined the benefits diminish. Specifically, the paper makes the key observation that for serverless functions, the overhead of accessing dissaggregated persistent storage overshadows the gains from accelerators. Therefore, to benefit from all these trends in conjunction, we propose Domain-Specific Computational Storage for Serverless (DSCS-Serverless). This idea contributes a serverless model that leverages a programmable accelerator within computational storage to conjugate the benefits of acceleration and storage disaggregation simultaneously. Our results with eight applications shows that integrating a comparatively small accelerator within the storage (DSCS-Serverless) that fits within its power constrains (15 Watts), significantly outperforms a traditional disaggregated system that utilizes the NVIDIA RTX 2080 Ti GPU (250 Watts). Further, the work highlights that disaggregation, serverless model, and the limited power budget for computation in storage require a different design than the conventional practices of integrating microprocessors and FPGAs. This insight is in contrast with current practices of designing computational storage that are yet to address the challenges associated with the shifts in datacenters. In comparison with two such conventional designs that either use quad-core ARM A57 or a Xilinx FPGA, DSCS-Serverless provides 3.7x and 1.7x end-to-end application speedup, 4.3x and 1.9x energy reduction, and 3.2x and 2.3x higher cost efficiency, respectively.
翻译:虽然没有服务器的计算正在成为一种流行的云执行形式,但数据中心正在经历重大的变化:(2) 系统基础设施级的存储分离,(3) 硬件级的常规加速器整合。这三种趋势中的每一种都单独提供了巨大的好处;然而,当合并的好处减少时,这三个趋势中的每一种都提供了巨大的好处。具体地说,文件对没有服务器的功能提出了关键观察,即访问分离的持久储存的间接费用掩盖了加速器带来的收益。因此,为了从所有这些趋势中得益,我们提议对无服务器(DSSCS-服务器级)进行多功能点特异化计算存储。这一想法促成了一种没有服务器的模型,在计算存储中利用可编程的加速器,同时将加速和存储分解的效益结合起来。我们用八个应用程序的结果显示,在存储(DSS-服务器级)内安装一个相对较小的加速器,(15瓦特斯), 大幅超出一个传统的分类系统,在使用NVICIA RTX 2080 TRS-S-Server 常规存储器的模型和计算流程中,这需要进一步的降压压压式的计算,在常规服务器的服务器的节能的计算中, 和变压的计算中,这个系统需要的节流的节流的节流的节能的节能的节能的节能的节能的节能的节能的节能的节能的节能的节能的节能的节能是, 。</s>