No abstract

Read disturb is a circuit-level noise in solid-state drives (SSDs), which may corrupt existing data in SSD blocks and then cause high read error rate and longer read latency. The approach of read refresh is commonly used to avoid read disturb errors by periodically migrating the hot read data to other free blocks, but it places considerable negative impacts on I/O (Input/Output) responsiveness. This article proposes scheduling approaches on write data and read refresh operations, to mitigate the negative effects caused by read disturb. To be specific, we first construct a model to classify SSD blocks into two categories according to the estimated read error rate by referring to the factors of block’s P/E (Program/Erase) cycle and the accumulated read count to the block. Then, the data being intensively read will be redirected to the block having a small read error rate, as it is not sensitive to read disturb even though the data will be heavily requested. Moreover, we take advantage of reinforcement learning to predict the idle interval between two I/O requests for purposely conducting (partial) read refresh operations. As a result, it is able to minimize negative impacts toward subsequent incoming I/O requests and to ensure I/O responsiveness. Through a series of emulation tests on several realistic disk traces, we demonstrate that the proposed mechanisms can noticeably yield performance improvements on the metrics of read error rate and I/O latency.

This article proposes a low I/O intensity-aware scheduling scheme on garbage collection (GC) in SSDs for minimizing the I/O long-tail latency to ensure I/O responsiveness. The basic idea is to assemble partial GC operations by referring to several determinable factors (e.g., I/O characteristics) and dispatch them to be processed together in idle time slots of I/O processing. To this end, it first makes use of Fourier transform to explore the time slots having relative sparse I/O requests for conducting time-consuming GC operations, as the number of affected I/O requests can be limited. After that, it constructs a mathematical model to further figure out the types and quantities of partial GC operations, which are supposed to be dealt with in the explored idle time slots, by taking the factors of I/O intensity, read/write ratio, and the SSD use state into consideration. Through a series of simulation experiments based on several realistic disk traces, we illustrate that the proposed GC scheduling mechanism can noticeably reduce the long-tail latency by between 5.5% and 232.3% at the 99.99th percentile, in contrast to state-of-the-art methods.

RAID-enabled SSDs commonly have unbalanced I/O workloads on their components (e.g. SSD channels), as the data/parity chunks in the same stripe may have varied access frequency, which greatly impacts I/O responsiveness. This paper proposes a I/O scheduling scheme by resorting to the degraded read mode and the read-modify-write mode, to reduce the long-tail latency of I/O requests in RAID-enabled SSDs. The basic idea is to avoid scheduling read or update requests to the heavily congested but targeted RAID components. Such requests are satisfied by accessing other relevant RAID components by certain XOR computations (we call the degraded modes ). Specially, we build a queuing overhead assessment model on the top of factors of data redundancy and the current blocked I/O traffics on SSD channels, to precisely dispatch incoming I/O requests to be fulfilled with the degraded mode or not. The trace-driven experiments illustrate that the proposed scheme can reduce the long-tail latency of read requests by 23.1% on average at the 99.99th percentile, in contrast to state-of-the-art scheduling methods.

To ensure better I/O performance of solid-state drivers (SSDs), a dynamic random access memory (DRAM) is commonly equipped as a cache to absorb overwrites or writes, instead of directly flushing them onto underlying SSD cells. This paper focuses on the management of the small amount cache inside SSDs. First, we propose to unify both factors of temporal and spatial locality of user applications by employing the visibility graph technique, for directing cache management. Next, we propose to support batch adjustment of adjacent or nearby (hot) cached data pages by referring to the connection situations in the visibility graph of all cached pages. At last, we propose to evict the buffered data pages in batches, to maximize the internal flushing parallelism of SSD devices, without worsening I/O congestion. The trace-driven simulation experiments show that our proposal can yield improvements on cache hits by more than 2.8%, and the overall I/O latency by 20.2% on average, in contrast to conventional cache schemes inside SSDs.