SSDcheck: Timely and Accurate Prediction of Irregular Behaviors in Black-Box SSDs

摘要

Modern servers are actively deploying Solid-State Drives (SSDs). However, rather than just a fast storage device, SSDs are complex devices designed for device-specific goals (e.g., latency, throughput, endurance, cost) with their internal mechanisms undisclosed to users as the proprietary asset, which leads to unpredictable, irregular inter/intra-SSD access latencies. This unpredictable irregular access latency has been a fundamental challenge to server architects aiming to satisfy critical quality-of-service requirements and/or achieve the full performance potential of commodity SSDs. In this paper, we propose SSDcheck, a novel SSD performance model to accurately predict the latency of next access to commodity black-box SSDs. First, after analyzing a wide spectrum of real-world SSDs, we identify key performance-critical features (e.g., garbage collection, write buffering) required to construct a general SSD performance model. Next, SSDcheck runs diagnosis code snippets to extract static feature parameters (e.g., size, threshold) from the target SSD, and constructs its performance model. Finally, during runtime, SSDcheck dynamically manages the performance model to predict the latency of the next access. Our evaluations show that SSDcheck achieves up to 98.96% and 79.96% on-average prediction accuracy for normal-latency and high-latency predictions, respectively. Next, we show the effectiveness of SSDcheck by implementing a new volume manager improving the throughput by up to 4.29x with the tail latency reduction down to 6.53%, and a new I/O request handler improving the throughput by up to 44.0% with the tail latency reduction down to 26.9%. We then show how to further improve the results of scheduling with the help of an emerging Non-Volatile Memory (e.g., PCM). SSDcheck does not require any hardware modifications, which can be harmlessly disabled for any SSDs uncovered by the performance model.