Characterizing and optimizing TPC-C workloads on large-scale systems using SSD arrays

Abstract

Transaction processing performance council benchmark C (TPC-C) is the de facto standard for evaluating the performance of high-end computers running on-line transaction processing applications. Differing from other standard benchmarks, the transaction processing performance council only defines specifications for the TPC-C benchmark, but does not provide any standard implementation for end-users. Due to the complexity of the TPC-C workload, it is a challenging task to obtain optimal performance for TPC-C evaluation on a large-scale high-end computer. In this paper, we designed and implemented a large-scale TPC-C evaluation system based on the latest TPC-C specification using solid-state drive (SSD) storage devices. By analyzing the characteristics of the TPC-C workload, we propose a series of system-level optimization methods to improve the TPC-C performance. First, we propose an approach based on SmallFile table space to organize the test data in a round-robin method on all of the disk array partitions; this can make full use of the underlying disk arrays. Second, we propose using a NOOP-based disk scheduling algorithm to reduce the utilization rate of processors and improve the average input/output service time. Third, to improve the system translation lookaside buffer hit rate and reduce the processor overhead, we take advantage of the huge page technique to manage a large amount of memory resources. Lastly, we propose a locality-aware interrupt mapping strategy based on the asymmetry characteristic of non-uniform memory access systems to improve the system performance. Using these optimization methods, we performed the TPC-C test on two large-scale high-end computers using SSD arrays. The experimental results show that our methods can effectively improve the TPC-C performance. For example, the performance of the TPC-C test on an Intel Westmere server reached 1.018 million transactions per minute.

摘要

创新点

本文提出一系列面向 TPC-C 评测程序的系统级优化方法: 1) 采用小文件表空间模式组织 TPC-C 测试数据, 充分发挥底层磁盘阵列并发处理能力, 平衡上层 I/O 请求; 2) 采用 NOOP 的磁盘调度策略管理底层的固态磁盘阵列, 降低了平均的 I/O 请求处理时间; 3) 采用大页面方式优化 TPC-C 的内存使用, 提高系统 TLB 命中率并降低处理器开销; 4) 根据中断请求的类型, 把中断绑定到固定的处理器核上, 有效的减少中断请求对处理器性能的干扰。