Data compression is increasingly employed to enhance throughput and space efficiency in flash-based storage systems, which are critical for data-intensive applications. Current intra-SSD compression techniques operate transparently with respect to the file system and contribute to improving the lifetime of SSDs. These approaches typically avoid compressing read-hot data to reduce the latency penalties associated with decompression. However, the read-hot data remain uncompressed even after turning into cold data, thereby reducing overall compression effectiveness and diminishing space efficiency. Moreover, when previously compressed cold data become read-hot, it necessitates frequent decompression, which increases the read latency. To address the above problems, we propose a novel Garbage Collection aided Compression (GCC) scheme, to optimize space efficiency and mitigate read latency for compression-supported SSDs. The key idea of GCC is exploiting the valid page migration during garbage collection to enable background compression and decompression. Throughout the garbage collection process, the migrated valid pages can potentially be compressed or decompressed, which progressively improves space efficiency and minimizes the need for decompression during read operations. Performance evaluations conducted using MQSim simulator demonstrate that, compared to the typical compression schemes, GCC reduces the read and write latency by 25.27% and 9.43% on average and improves the space efficiency by 15.01% on average.

Recently, the flash-based Solid State Drive (SSD) array has been widely implemented in real-world large-scale clusters. With the increasing number of users in upper-tier applications and the burst of Input/Output requests in this data explosive era, data centers need to continuously scale up to meet real-time data storage needs. However, the classical disk array scaling methods are designed based on HDDs, ignoring the wear leveling and garbage collection characteristics of SSD. This leads to penalties due to the vast lifetime gap between extended SSDs and the original in-use SSDs while scaling the SSD array, including extra triggered wear leveling I/O, latency in average response time, etc.To address these problems, we propose an Adaptive Wear-Leveling aware data migration approach for flexible SSD array scaling in clusters. It manages the interdisk wear leveling based on Model Reference Adaptive Control, which includes an SSD behavior emulator, Kalman filter estimator, and adaptive law. To demonstrate the effectiveness of this approach, we conducted several simulations and implementations on actual hardware. The evaluation results show that Ada-WL has the self-adaptability to optimize the wear leveling management parameters for various states of SSD arrays, diverse workloads, and scaling performed multiple times, significantly improving performance for SSD array scaling.