Emulate Processing of Assorted Database Server Applications on Flash-Based Storage in Datacenter Infrastructures

被引:0
|
作者
Bhimani, Janki [1 ]
Pandurangan, Rajinikanth [3 ]
Mi, Ningfang [2 ]
Balakrishnan, Vijay [3 ]
机构
[1] Florida Int Univ, Sch Comp & Informat Sci, Miami, FL 33199 USA
[2] Northeastern Univ, Dept Elect & Comp Engn, Boston, MA 02115 USA
[3] Samsung Semicond Inc R&D, Memory Solut Lab, San Jose, CA USA
基金
美国国家科学基金会;
关键词
SSD; I/O Generator; I/O Pattern; Characteristics; Performance;
D O I
10.1109/ipccc47392.2019.8958744
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In the era of big data processing, more and more datacenters in cloud storages are now replacing traditional HDDs with enterprise SSDs. Both developers and users of these SSDs require thorough benchmarking to evaluate their performance impacts. I/O performance with synthetic workload or classic benchmark varies drastically from real I/O activities in the datacenter. Thus, we propose a new framework, called Pattern I/O generator (PatIO), to collectively capture the enterprise storage behavior that is prevailing across assorted user workloads and system configurations for different database server applications on flash-based storage. PatIO is designed to emulate the processing of real-world I/O activities easily with less time and resource requirements. Our methodology comprises three main steps: (1) dissect the overall I/O activities of various real workloads and identify the prevailing attributes in distinct visual I/O patterns; (2) construct a pattern warehouse as the collection of unique I/O patterns that are generated through various combinations of multiple I/O jobs; and (3) finally integrate different combinations of these synthetically generated I/O patterns to reproduce the comprehensive characteristics of various real workloads and system setup for the database server applications. To provide an easy-to-use experience, we develop a graphical user interface (GUI). We evaluate our framework by comparing I/O characteristics and I/O performance of generated workloads with those of real-world workloads for multiple database applications such as MySQL, Cassandra, and ForestDB.
引用
收藏
页数:8
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