Operating system level data tiering using online workload characterization
Over the past decade, storage has been the performance bottleneck in I/O-intensive programs such as online transaction processing applications. To alleviate this bottleneck with minimal cost penalty, cost-effective design of a high-performance disk subsystem is of decisive importance in enterprise applications. Data tiering is an efficient way to optimize cost, performance, and reliability in storage servers. With the promising advantages of solid-state drives (SSDs) over hard disk drives (HDDs) such as lower power consumption and higher performance, traditional data tiering techniques should be revisited to use SSDs in a more efficient way. Previously proposed tiering solutions have attempted to enhance performance based on different parameters such as request size or randomness. These solutions, however, are mostly optimized towards one type of I/O workloads and are not applicable to workloads with different characteristics. This paper presents an online data tiering technique at the operating system level with a linear weighted formulation to enhance I/O performance with minimal cost overhead. The proposed technique characterizes the workload access pattern with respect to metadata versus user data, frequency of accesses, random versus sequential accesses, and read versus write accesses. To evaluate the proposed technique, it is implemented on a Linux 3.1.4 equipped with ext2 filesystem. The experimental results over I/O-intensive workloads show that the proposed technique improves performance up to 30% as compared to the previous techniques while imposing negligible memory overhead to the system.
operating system performance solid-state drive filesystem i/o workload characterization