No abstract

Advanced solid-state disks (SSDs) have been equipped with page-mapping flash translation layers and multichannel architectures. The SSDs employ a RAM-based write buffer, which delays write requests for reducing write traffic, reorders requests for mitigating garbage-collection overhead, and produces parallel page writes for improving channel time utilization. This work presents a novel write buffer algorithm that exploits temporal and spatial correlations among buffer pages. The write-buffer groups temporally or spatially correlate buffer pages and then write the grouped buffer pages to the same flash block. In this way, when the correlated page data are updated in the future, flash blocks will receive bulk page invalidations and become good candidates for garbage collection. With multichannel architectures, the write buffer adaptively disperses read-most sequential data over channels for high page-level parallelism of sequential reads, while clustering write-most sequential data in the same channel for a reduced cost of garbage collection. We evaluated the proposed method and previously proposed buffer algorithms. Our method was shown to outperform the existing methods by up to 134%. We also implemented our buffer design on the OpenSSD platform; the time and space overheads of our design were reported to be very low.

Non-Volatile Memory Express (NVMe) is a specification for next-generation solid-state disks (SSDs). Benefited from the massive internal parallelism and the high-speed PCIe bus, NVMe SSDs achieve extremely high data transfer rates, and they are an ideal solution of shared storage in virtualization environments. Providing virtual machines with Service Level Objective (SLO) compliance on NVMe SSDs is a challenging task, because garbage collection activities inside of NVMe SSDs globally affect the I/O performance of all virtual machines. In this study, we introduce a novel approach, called parallelism reservation, which is inspired by the rich internal parallelism of NVMe SSDs. The degree of parallelism stands for how many flash chips are concurrently active. Our basic idea is to reserve sufficient degrees of parallelism for read, write, and garbage collection operations, making sure that an NVMe SSD delivers stable read and write throughput and reclaims free space at a constant rate. The stable read and write throughput are proportionally distributed among virtual machines for SLO compliance. Our experimental results show that our parallelism reservation approach delivered satisfiable throughput and highly predictable response to virtual machines.

No abstract

Mapping table caching is a promising technique to reduce the RAM footprint of the FTL mapping tables in modern SSDs. The mapping cache can achieve a high hit ratio under the disk workloads of many production systems because there are spatial and temporal localities in the disk workloads. However, the mapping cache suffers from severe miss penalty and degrades the SSD performance under random write patterns, which are common in benchmarks and database applications. Our main result is that optimizing the mapping cache for random-write workloads is completely different from that for non-random workloads. We propose partitioning all flash blocks into a group of user data and a group of mapping information. By strategically shifting free flash blocks between the two groups, the best balance of the garbage collection overhead between the two groups is achieved. We conducted a series of experiments using the disk workloads from industry-standard SSD benchmarks, and the results show that our approach improved the write performance by up to 30% compared to a conventional map caching method.