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

TL;DR

This paper introduces PS-WL, a novel wear leveling scheme for SSD arrays that directly optimizes for failure risk reduction, leading to more reliable and efficient storage systems.

Contribution

It presents a probability-sensitive wear leveling method that considers failure risk, improving upon traditional wear balancing approaches.

Findings

Reduces array failure risk across various workloads.

Lowers performance overhead compared to existing methods.

Enhances system reliability and stability.

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.