The shattered and overlapped Level-0 data organization is the primary cause of write stall and read amplification problems in LSM-Tree-based Key-Value (KV) stores: (1) Level-0 to Level-1 compaction involves a large amount of data which induces write stalls, and (2) A point lookup needs to access multiple files in Level-0 which leads to significant read amplification. To address the problem, we propose BL-Tree by replacing the shattered Level-0 in LSM-Tree with a B+-Tree in byte-addressable Persistent Memory (PM). The sorted B+-Tree of Level-0 can accelerate the point lookup speed and reduce read/write amplification. BL-Tree further conducts the locality-aware and parallel compaction from the B+-Tree in PM (Level-0) to the lower levels of LSM-Tree in SSDs by only moving cold data downward, thus alleviating the write stalls and reducing the read/write amplification simultaneously. The extensive experiments on the prototype of BL- Tree show that it definitely avoids the write stalls and significantly reduces the read/write amplification. As a result, BL-Tree reduces the P99 tail latency by 65.2 × than LevelDB-PM and speeds up the throughput by more than 2 × under workloads with spatial locality than other KV stores.

MiRNA-disease associations (MDAs) are particularly insightful for revealing the complex pathology of human diseases. Traditional experimental methods for MDA studies are costly, time-consuming, and low-throughput. Thus, many machine learning and deep learning-based methods have been proposed to predict MDAs. However, due to the limitations of supervised learning, these methods often fall short in predictive performance. To address this issue, we propose a novel MDA prediction method, HeMDAP, which is based on graph contrastive learning using meta-path view and network structure view of heterogeneous graph. The main innovation of HeMDAP lies in designing two complementary graph representations, network structure view and meta-path view, as well as introducing self-supervised contrastive learning and supervised contrastive learning to effectively optimize node embeddings. Additionally, we utilize knowledge-aware enhancement to further improve embedding quality. This multi-view learning and multi-task training strategy can more comprehensively capture the complex relationships among miRNAs, genes, and diseases. Experimental results on public datasets show that HeMDAP outperforms all existing methods in terms of prediction accuracy. In five-fold cross-validation, HeMDAP achieved an AUC of 94.92% and an AUPR of 95.07%. These results demonstrate the effectiveness and superiority of our proposed method in the MDA prediction task.

More and more data are stored in cloud storage, which brings two major challenges. First, the modified files in the cloud should be quickly synchronized to ensure data consistency, e.g., delta synchronization (sync) achieves efficient cloud sync by synchronizing only the updated part of the file. Second, the huge data in the cloud needs to be deduplicated and encrypted, e.g., Message-Locked Encryption (MLE) implements data deduplication by encrypting the content among different users. However, when combined, a few updates in the content can cause large sync traffic amplification for both keys and ciphertext in the MLE-based cloud storage, significantly degrading the cloud sync efficiency. A feature-based encryption sync scheme, FeatureSync, is proposed to address the delta amplification problem. However, with further improvement of the network bandwidth, the performance of FeatureSync stagnates. In our preliminary experimental evaluations, we find that the bottleneck of the computational overhead in the high-bandwidth network environments is the main bottleneck in FeatureSync. In this article, we propose an enhanced feature-based encryption sync scheme FASTSync to optimize the performance of FeatureSync in high-bandwidth network environments. The performance evaluations on a lightweight prototype implementation of FASTSync show that FASTSync reduces the cloud sync time by 70.3% and the encryption time by 37.3%, on average, compared with FeatureSync.

Non-Volatile Memories (NVMs) have shown tremendous potential to be the next generation of main memory, yet they are still seriously hampered by the high write latency and limited endurance. In this paper, we first unveil via realworld benchmark analysis that the words within the same cache line showcase a high degree of similarity. We therefore present SimiEncode, a low-overhead and effective Similarity-based Encoding approach. SimiEncode relieves writes to NVMs by (1) generating a mask word with minimized differences to the words within a cache line, (2) encoding each word with the associated mask word by simple XOR operations, and (3) writing a single tag bit to indicate the resulting zero word after encoding. Our prototype implementation of SimiEncode and extensive evaluations driven by 15 state-of-the-art benchmarks demonstrate that, compared with existing approaches, SimiEncode significantly prolongs the lifetime and improves the performance. Importantly, SimiEncode is orthogonal to and can be easily incorporated into existing bit flipping optimizations.

As increasingly prevalent, more and more data are stored in the cloud storage, which brings us two major challenges. First, the modified files in the cloud should be quickly synchronized (sync) to ensure data consistency, e.g., delta sync achieves efficient cloud sync by synchronizing only the updated part of the file. Second, the huge data in the cloud needs to be deduplicated and encrypted, e.g., message-locked encryption (MLE) implements data deduplication by encrypting the content between different users. However, when both are combined, few updates in the content can cause large sync traffic amplification for both keys and ciphertext in the MLE-based cloud storage, which significantly degrading the cloud sync efficiency. In this paper, we propose an feature-based encryption sync scheme FeatureSync to improve the performance of synchronizing multiple encrypted files by merging several files before synchronizing. The performance evaluations on a lightweight prototype implementation of FeatureSync show that FeatureSync reduces the cloud sync time by 72.6% and the cloud sync traffic by 78.5% on average, compared with the state-of-the-art sync schemes.