In the era of big data, ensuring both data confidentiality and reliability has become a critical concern for data owners. While encryption algorithms and erasure coding techniques can independently guarantee confidentiality and reliability, respectively, traditional methods that apply encryption before or after erasure coding often lead to significant performance degradation and increased storage overhead. To address these limitations, this paper proposes DERAID, a novel RAID-based technology that integrates encryption directly into the coding process. DERAID is designed to improve the efficiency of encoding, decoding, updating and reconstruction operations, while simultaneously ensuring data confidentiality and reliability and minimizing the data expansion rate. The scheme employs a unified coding structure based on the ECB encryption mode, where conventional parity blocks are replaced by a combination of cryptographic keys and verification blocks. Experimental results demonstrate that, compared to traditional methods and QS-code, DERAID improves encoding performance by 44.7–95.3%, updating performance by 48.2–99.6% and reconstruction performance by 63.0–99.9%, while reducing the data expansion rate by 3.3–36.9%. These results indicate that DERAID achieves a practical balance between security, reliability and performance.

In cloud storage services, burst I/O workloads from data analytics and artificial intelligence/machine learning (AI/ML) applications present significant challenges to Quality of Service (QoS) management. Existing scheduling models like dmClock ensure fair and stable I/O bandwidth allocation in typical scenarios. However, they falter under frequent burst traffic, leading to lower resource utilization and higher task latency. To address this, we propose BAQoS, a Burst I/O Aware Quality of Service Optimization for Cloud Storage Service. BAQoS employs refined request classification, a burst-aware hierarchical scheduling algorithm, and a high-performance scheduler architecture (HPSA). These features enable dynamic resource allocation and efficient scheduling for both burst and regular requests. Experiments show that BAQoS markedly enhances performance under burst workloads, accelerating burst request processing by up to 7.09 and improving overall system performance by 48.86%. Furthermore, BAQoS ensures superior performance for non-burst users, achieving a 51.15% performance boost for high-priority users, a 5.34% increase in system throughput, and an over 50% reduction in IOPS standard deviation among same-priority users.

Erasure codes have been widely applied to in-memory key-value storage systems for high reliability and low redundancy. In distributed in-memory key-value storage systems, update operations are relatively frequent, especially the partial-stripe update, which makes data update more challenging. Recently, existing research has been based on appending logs to accelerate parity data write. However, its logs are stored on disks, which decreases the system performance significantly. Therefore, we propose a novel in-memory key-value storage architecture, DNVPL, which utilizes NVRAM to log parity data. Our main idea is to design an appending-only update scheme to tradeoff the memory cost and the update overhead. We implement DNVPL with an in-memory key-value storage prototype, called LogKV. We evaluate it with different workloads. The experiments show that our scheme achieves high update performance from different metrics. Our scheme can reduce update latency by up to 49% and save storage space by 48% compared to the state-of-the-art schemes.

Due to cloud security concerns, there is an increasing interest in information retrieval systems that can support private queries over public documents. It is desirable for oblivious document ranking and retrieval in public cloud at lower cost and faster speed without revealing query-related information. Currently, the term frequency-inverse document frequency (TF-IDF) and private information retrieval (PIR) techniques are used to solve this problem, but the encryption operation time is over dominant. Motivated by the observation of the sparsity of the TF-IDF matrix, we propose an efficient approach for oblivious document ranking and retrieval, called E-Coeus. It takes advantage of the high sparsity of the TF-IDF matrix to rearrange the matrix. Our method accelerates the speed of PIR inadvertently retrieving documents and reduces the user retrieval delay time. In a stand-alone experiment for a TF-IDF matrix of 1.2M rows and 64K columns with the sparsity of 10%, E-Coeus improves the document ranking and retrieval performance by 23% over the state-of-the-art approach, Coeus. With cluster of 64 machines, E-Coeus improves the performance by 34% over Coeus when the TF-IDF matrix sparsity is 30%.

In data centers, hard disks are the most prone to failure of IT equipment. Although there is data backup, data reliability still faces challenges due to hard disks failure. In recent years, many hard disk failure prediction approaches based on SMART data have been proposed. In this paper, we proposed a novel disk failure prediction approach based on Lightgbm algorithm with CID (complexity invariant distance). Our failure prediction model has been built and evaluated on SMART data of about 80,000 hard disks from two manufacturers. The experimental result shows that by adding CID features, the TPR is increased from 0.28 to 0.96, and the number of days that the model can predict failures in advance is extended by 1.2 days. Compared with the several existing failure prediction models, our model has better performance on AUC score, f1-score and TPR.

Flash and RDMA (Remote Direct Memory Access) provide extremely high performance in storage and network hardware. However, a gap between distributed system and new hardware exists. Although RDMA speeds up memory access between two nodes, there are many serious problems to be solved when sending data or command from one flash to another flash. In this paper, we propose a distributed system on flash and RDMA, and implement a User-space Sendfile verb based on it. Experimental results show that the RPC in DSFR outperforms the traditional RPC mechanism by dozens of times, and the uSendfile reduces time overhead significantly.