PS-WL: A Probability-Sensitive Wear Leveling scheme for SSD array scaling

2025-06-24

AI Summary

To address the premature failure of aging SSDs in SSD arrays—caused by conventional wear leveling (WL) schemes ignoring the nonlinear relationship between wear and failure probability—this paper proposes PS-WL, a probability-sensitive wear leveling scheme. Methodologically, PS-WL innovatively integrates a data-driven failure probability model into the WL framework, establishes a risk metric based on effective lifetime, and employs a PID controller for dynamic, risk-aware wear distribution. Additionally, a conservative zone mechanism is introduced to suppress excessive migration of hot data, thereby reducing performance overhead. Experimental results across diverse workloads and configurations demonstrate that PS-WL significantly reduces overall array failure risk compared to state-of-the-art baselines, while simultaneously decreasing average I/O latency and throughput overhead by 18.7%.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Distributed Search
• Multiagent Systems: Distributed Problem Solving

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

As flash-based Solid State Drive (SSD) arrays become essential to modern data centers, scaling these arrays to meet explosive data growth is a frequent and critical operation. However, the conventional wear-leveling (WL) paradigm applied during scaling suffers from a fundamental flaw: it ignores the non-linear relationship between wear and failure probability, potentially pushing the most vulnerable, aged disks towards premature failure. To address this critical issue at its root, we propose the Probability-Sensitive Wear Leveling (PS-WL) scheme, which shifts the optimization goal from balancing wear to directly balancing failure risk. At its core, PS-WL introduces an "effective lifetime" model derived from a realistic failure probability to more accurately assess disk lifetime. This model guides a PID controller for wear leveling operation, with a conservative zone minimizes performance overhead by restricting warm data migration. Comprehensive simulations validate the superiority of PS-WL over state-of-the-art methods. The results demonstrate that our approach significantly reduces performance overhead while, most critically, consistently and effectively lowering the aggregated array failure risk across diverse system configurations and workloads. This proves that by directly optimizing for reliability, PS-WL builds a scalable storage system that is, by design, fundamentally safer, more efficient, and more stable.

Problem

• Balancing failure risk in SSD arrays during scaling
• Addressing non-linear wear-failure relationship in disks
• Reducing performance overhead while improving reliability

Innovation

• Balances failure risk not wear
• Uses effective lifetime model
• PID controller for wear leveling