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ScaleDefrag: Design and implementation of a scalable file defragmentation tool for flash-based SSDs.

Sangjin Lee¹, Sunggon Kim², Yongseok Son¹

¹Department of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of Korea.

|May 6, 2026

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ScaleDefrag significantly speeds up file defragmentation on solid-state drives (SSDs) by using parallel processing. This new tool reduces defragmentation time by up to 3.8×, improving performance on multi-core systems.

Area of Science:

Computer Science
Data Storage Systems

Background:

Flash-based Solid-State Drives (SSDs) are prevalent in cloud and High-Performance Computing (HPC).
File fragmentation on SSDs, caused by intensive file modifications, degrades storage system performance.
Existing defragmentation tools lack scalability due to sequential and synchronous processing.

Purpose of the Study:

To develop a scalable defragmentation tool for flash-based SSDs.
To reduce defragmentation time and improve system performance.

Main Methods:

ScaleDefrag employs parallel and asynchronous processing for defragmentation.
An information collector gathers file metadata prior to parallel execution.
Multiple defraggers work on a one-to-one file basis with asynchronous I/O operations.

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Main Results:

ScaleDefrag reduces defragmentation time by up to 3.8× compared to e4defrag.
The tool demonstrates improved scalability on multi-core systems.
ScaleDefrag increases defragmentation throughput and mitigates performance degradation for co-running applications.

Conclusions:

ScaleDefrag offers a scalable and efficient solution for SSD defragmentation.
The parallel and asynchronous approach overcomes the limitations of traditional defragmentation methods.
This enhances the performance and reliability of large-scale storage systems utilizing SSDs.