Abstract-Flash-based solid state drives (SSDs) exhibit potential for solving I/O bottlenecks by offering superior performance over hard disks for several workloads. In this work we design Azor, an SSD-based I/O cache that operates at the block-level and is transparent to existing applications, such as databases. Our design provides various choices for associativity, write policies and cache line size, while maintaining a high degree of I/O concurrency. Our main contribution is that we explore differentiation of HDD blocks according to their expected importance on system performance. We design and analyze a two-level block selection scheme that dynamically differentiates HDD blocks, and selectively places them in the limited space of the SSD cache.We implement Azor in the Linux kernel and evaluate its effectiveness experimentally using a server-type platform and large problem sizes with three I/O intensive workloads: TPC-H, SPECsfs2008, and Hammerora. Our results show that as the cache size increases, Azor enhances I/O performance by up to 14.02×, 1.63×, and 1.55× for each workload respectively. Additionally, our two-level block selection scheme further enhances I/O performance compared to a typical SSD cache by up to 95%, 16%, and 34% for each workload, respectively.

Flash-based solid state drives (SSDs) offer superior performance over hard disks for many workloads. A prominent use of SSDs in modern storage systems is to use these devices as a cache in the I/O path. In this work, we examine how transparent, online I/O compression can be used to increase the capacity of SSD-based caches, thus increasing the costeffectiveness of the system. We present FlaZ, an I/O system that operates at the block-level and is transparent to existing file-systems. To achieve transparent, online compression in the I/O path and maintain high performance, FlaZ provides support for variable-size blocks, mapping of logical to physical blocks, block allocation, and cleanup. FlaZ mitigates compression and decompression overheads that can have a significant impact on performance by leveraging modern multicore CPUs. We implement FlaZ in the Linux kernel and evaluate it on a commodity server with multicore CPUs, using TPC-H, PostMark, and SPECsfs. Our results show that compressed caching trades off CPU cycles for I/O performance and enhances SSD efficiency as a cache by up to 99%, 25%, and 11% for each workload, respectively.

In this work, we examine how transparent block-level compression in the I/O path can improve both the space efficiency and performance of online storage. We present ZBD, a block-layer driver that transparently compresses and decompresses data as they flow between the file-system and storage devices. Our system provides support for variable-size blocks, metadata caching, and persistence, as well as block allocation and cleanup. ZBD targets maintaining high performance, by mitigating compression and decompression overheads that can have a significant impact on performance by leveraging modern multicore CPUs through explicit work scheduling. We present two case-studies for compression. First, we examine how our approach can be used to increase the capacity of SSD-based caches, thus increasing their cost-effectiveness. Then, we examine how ZBD can improve the efficiency of online disk-based storage systems.We evaluate our approach in the Linux kernel on a commodity server with multicore CPUs, using PostMark, SPECsfs2008, TPC-C, and TPC-H. Preliminary results show that transparent online block-level compression is a viable option for improving effective storage capacity, it can improve I/O performance up to 80% by reducing I/O traffic and seek distance, and has a negative impact on performance, up to 34%, only when single-thread I/O latency is critical. In particular, for SSD-based caching, our results indicate that, in line with current technology trends, compressed caching trades off CPU utilization for performance and enhances SSD efficiency as a storage cache up to 99%.

In this work we examine how transparent compression in the I/O path can improve space efficiency for online storage. We extend the block layer with the ability to compress and decompress data as they flow between the file-system and the disk. Achieving transparent compression requires extensive metadata management for dealing with variable block sizes, dynamic block mapping, block allocation, explicit work scheduling and I/O optimizations to mitigate the impact of additional I/Os and compression overheads. Preliminary results show that online transparent compression is a viable option for improving effective storage capacity, it can improve I/O performance by reducing I/O traffic and seek distance, and has a negative impact on performance only when single-thread I/O latency is critical.