ACM Transactions on Storage最新文献

Predictable latency on flash storage is a long-pursuit goal, yet unpredictability stays due to the unavoidable disturbance from many well-known SSD internal activities. To combat this issue, the recent NVMe IO Determinism (IOD) interface advocates host-level controls to SSD internal management tasks. Although promising, challenges remain on how to exploit it for truly predictable performance.

We present IODA,¹ an I/O deterministic flash array design built on top of small but powerful extensions to the IOD interface for easy deployment. IODA exploits data redundancy in the context of IOD for a strong latency predictability contract. In IODA, SSDs are expected to quickly fail an I/O on purpose to allow predictable I/Os through proactive data reconstruction. In the case of concurrent internal operations, IODA introduces busy remaining time exposure and predictable-latency-window formulation to guarantee predictable data reconstructions. Overall, IODA only adds five new fields to the NVMe interface and a small modification in the flash firmware while keeping most of the complexity in the host OS. Our evaluation shows that IODA improves the 95–99.99^th latencies by up to 75×. IODA is also the nearest to the ideal, no disturbance case compared to seven state-of-the-art preemption, suspension, GC coordination, partitioning, tiny-tail flash controller, prediction, and proactive approaches.

This paper offers a solution to overcome the complexities of production system I/O performance monitoring. We present Beacon, an end-to-end I/O resource monitoring and diagnosis system for the 40960-node Sunway TaihuLight supercomputer, currently the fourth-ranked supercomputer in the world. Beacon simultaneously collects and correlates I/O tracing/profiling data from all the compute nodes, forwarding nodes, storage nodes, and metadata servers. With mechanisms such as aggressive online and offline trace compression and distributed caching/storage, it delivers scalable, low-overhead, and sustainable I/O diagnosis under production use. With Beacon’s deployment on TaihuLight for more than three years, we demonstrate Beacon’s effectiveness with real-world use cases for I/O performance issue identification and diagnosis. It has already successfully helped center administrators identify obscure design or configuration flaws, system anomaly occurrences, I/O performance interference, and resource under- or over-provisioning problems. Several of the exposed problems have already been fixed, with others being currently addressed. Encouraged by Beacon’s success in I/O monitoring, we extend it to monitor interconnection networks, which is another contention point on supercomputers. In addition, we demonstrate Beacon’s generality by extending it to other supercomputers. Both Beacon codes and part of collected monitoring data are released.¹

Large-scale storage systems employ erasure-coding redundancy schemes to protect against device failures. The adverse effect of latent sector errors on the Mean Time to Data Loss (MTTDL) and the Expected Annual Fraction of Data Loss (EAFDL) reliability metrics is evaluated. A theoretical model capturing the effect of latent errors and device failures is developed, and closed-form expressions for the metrics of interest are derived. The MTTDL and EAFDL of erasure-coded systems are obtained analytically for (i) the entire range of bit error rates; (ii) the symmetric, clustered, and declustered data placement schemes; and (iii) arbitrary device failure and rebuild time distributions under network rebuild bandwidth constraints. The range of error rates that deteriorate system reliability is derived analytically. For realistic values of sector error rates, the results obtained demonstrate that MTTDL degrades, whereas, for moderate erasure codes, EAFDL remains practically unaffected. It is demonstrated that, in the range of typical sector error rates and for very powerful erasure codes, EAFDL degrades as well. It is also shown that the declustered data placement scheme offers superior reliability.

Persistent byte-addressable memory (PM) is poised to become prevalent in future computer systems. PMs are significantly faster than disk storage, and accesses to PMs are governed by the Memory Management Unit (MMU) just as accesses with volatile RAM. These unique characteristics shift the bottleneck from I/O to operations such as block address lookup—for example, in write workloads, up to 45% of the overhead in ext4-DAX is due to building and searching extent trees to translate file offsets to addresses on persistent memory.

We propose a novel contiguous file system, ctFS, that eliminates most of the overhead associated with indexing structures such as extent trees in the file system. ctFS represents each file as a contiguous region of virtual memory, hence a lookup from the file offset to the address is simply an offset operation, which can be efficiently performed by the hardware MMU at a fraction of the cost of software-maintained indexes. Evaluating ctFS on real-world workloads such as LevelDB shows it outperforms ext4-DAX and SplitFS by 3.6× and 1.8×, respectively.

High density Solid State Drives, such as QLC drives, offer increased storage capacity, but a magnitude lower Program and Erase (P/E) cycles, limiting their endurance and hence usability. We present the design and implementation of non-binary, Voltage-Based Write-Once-Memory (WOM-v) Codes to improve the lifetime of QLC drives. First, we develop a FEMU based simulator test-bed to evaluate the gains of WOM-v codes on real world workloads. Second, we propose and implement two optimizations, an efficient garbage collection mechanism and an encoding optimization to drastically improve WOM-v code endurance without compromising performance. Third, we propose analytical approaches to obtain estimates of the endurance gains under WOM-v codes. We analyze the Greedy garbage collection technique with uniform page access distribution and the Least Recently Written (LRW) garbage collection technique with skewed page access distribution in the context of WOM-v codes. We find that although both approaches overestimate the number of required erase operations, the model based on greedy garbage collection with uniform page access distribution provides tighter bounds. A careful evaluation, including microbenchmarks and trace-driven evaluation, demonstrates that WOM-v codes can reduce Erase cycles for QLC drives by 4.4×–11.1× for real world workloads with minimal performance overheads resulting in improved QLC SSD lifetime.

High-performance computing scientists are producing unprecedented volumes of data that take a long time to load for analysis. However, many analyses only require loading in the data containing particular features of interest and scientists have many approaches for identifying these features. Therefore, if scientists store information (descriptive metadata) about these identified features, then for subsequent analyses they can use this information to only read in the data containing these features. This can greatly reduce the amount of data that scientists have to read in, thereby accelerating analysis. Despite the potential benefits of descriptive metadata management, no prior work has created a descriptive metadata system that can help scientists working with a wide range of applications and analyses to restrict their reads to data containing features of interest. In this article, we present EMPRESS, the first such solution. EMPRESS offers all of the features needed to help accelerate discovery: It can accelerate analysis by up to 300 ×, supports a wide range of applications and analyses, is high-performing, is highly scalable, and requires minimal storage space. In addition, EMPRESS offers features required for a production-oriented system: scalable metadata consistency techniques, flexible system configurations, fault tolerance as a service, and portability.