Erasure-coded storage systems adopt multiple redundancy levels to balance reliability and storage efficiency under changing workloads. However, transitioning data across different redundancy configurations incurs high network overhead due to data relocation and parity recomputation, especially under successive transitions. Existing approaches are typically optimized for fixed parameters and lack flexibility and scalability. This article presents FlexRT , a flexible and efficient redundancy transitioning framework for erasure-coded systems. FlexRT employs a linear-hashing–based stripe placement that decouples stripe layout from coding parameters, enabling zero data relocation across successive transitions. To minimize parity update overhead, FlexRT binds encoding coefficients to physical nodes instead of logical stripe positions, allowing parity to be incrementally updated even when data blocks are reorganized. In addition, a greedy sub-stripe decomposition and matching algorithm maximizes parity reuse and reduces the amount of data involved in recomputation, transforming redundancy transitioning into an efficient split-and-merge process. We implement FlexRT in a C++ prototype and evaluate it through large-scale simulations and Alibaba Cloud experiments. Results show that FlexRT reduces transitioning traffic by 86.0%–94.1% and shortens transition time by 79.4%–89.2% compared with state-of-the-art schemes, while completely eliminating data relocation.

Non-volatile memory (NVM) opens up new opportunities to resolve scaling restrictions of main memory, yet it is still hindered by the write disturbance (WD) problem. The WD problem mistakenly transforms the values of NVM cells, hence seriously deteriorating memory reliability and downgrading access performance. Existing studies mainly mitigate the WD problem via encoding WD-prone data patterns under in-place updates, yet we find that when turning to out-of-place updates, they can gain the potential to reduce more WD errors. We present LearnWD, an approach that mitigates the WD problem in NVM via coupling machine learning with out-of-place updates. LearnWD first employs clustering algorithms to classify the stale data based on the error proneness. To perform a write operation, LearnWD carefully examines the aggressivity of new data and the error proneness of stale data, so as to speculatively minimize the resulting WD errors. We conduct extensive experiments using 15 real-world datasets with different data types, showing that LearnWD can assist a variety of data encoding schemes to further reduce 19.5% of WD errors, shorten 10.1% of write latency, and extend 22.2% of write endurance.

In-memory key-value (KV) caching bridges the performance gap between high-performance networks and disk devices. However, prior in-memory KV caching systems either consider large objects or introduce additional memory overhead. In this paper, we conduct a systematic analysis over 56 production traces, and make three observations: (i) small objects dominate the traces and data accesses are highly skewed; (ii) the hotness of objects keeps stable across days; and (iii) the multi-get operation that retrieves multiple objects from the same node incurs much shorter tail latency than purely using the single-get operation.

Erasure coding is an effective technique for guaranteeing data reliability for storage systems, yet it incurs a high repair penalty with amplified repair traffic. The repair becomes more intricate in clustered storage systems with the bandwidth diversity property. We present TPRepair , a T ree-based P ipelined Repair approach, aiming to expedite the overall repair process with the tailored pipelined repair procedure. TPRepair first prioritizes selecting racks with the current minimum load to participate in the repair process. It subsequently formulates tree-based links, tailored to align seamlessly with the pipelined repair procedure. TPRepair further designs an optimization algorithm to reduce the bottleneck load when repairing multiple chunks. Large-scale simulations demonstrate that TPRepair can increase 13.8%–41.3% of the balance ratio without amplifying cross-rack traffic. Meanwhile, Alibaba Cloud ECS experiments indicate that TPRepair can increase repair throughput by 11.3% to 72.9%.

Erasure coding has been demonstrated as a storage-efficient means against failures, yet its tunability remains a challenging issue in data centers, which is prone to induce substantial cross-cluster traffic. In this article, we presentClusterRT, a cluster-aware redundancy transitioning approach that can dynamically tailor the redundancy degree of erasure coding in data centers.ClusterRTformulates the data relocation as the maximum flow problem to reduce cross-cluster data transfers. It then designs a parity-coordinated update algorithm, which gathers the parity chunks within the same cluster and leverages encoding dependency to further decrease the cross-cluster update traffic.ClusterRTfinally rotates the parity chunks to balance the cross-cluster transitioning traffic across the data center. Large-scale simulation and Alibaba Cloud ECS experiments show thatClusterRTreduces 94.0% to 96.2% of transitioning traffic and reduces 70.4% to 88.4% of transitioning time.

Cloud storage, one of the core services of cloud computing, provides an effective way to solve the problems of storage and management caused by high‐speed data growth. Thus, a growing number of organizations and individuals tend to store their data in the cloud. However, due to the separation of data ownership and management, it is difficult for users to check the integrity of data in the traditional way. Therefore, many researchers focus on developing several protocols, which can remotely check the integrity of data in the cloud. In this paper, we propose a novel public auditing protocol based on the adjacency‐hash table, where dynamic auditing and data updating are more efficient than those of the state of the arts. Moreover, with such an authentication structure, computation and communication costs can be reduced effectively. The security analysis and performance evaluation based on comprehensive experiments demonstrate that our protocol can achieve all the desired properties and outperform the state‐of‐the‐art ones in computing overheads for updating and verification.