Enhancing the endurance, performance, and energy efficiency of encrypted Non-Volatile Main Memory (NVMM) can be achieved by minimizing written data through inline deduplication. However, existing approaches applying inline deduplication to encrypted NVMM suffer from substantial performance degradation due to high computing, memory footprint, and index-lookup overhead to generate, store, and query the cryptographic hash (fingerprint). In the preliminary ESD [ 14 ], we proposed the Error Correcting Code (ECC) assisted selective deduplication scheme, utilizing the ECC information as a fingerprint to identify similar data effectively and then leveraging the selective deduplication technique to eliminate a large amount of redundant data with high reference counts. In this article, we proposed FSDedup. Compared with ESD, FSDedup could leverage the prefetch cache to reduce the read overhead during similarity comparison and utilize the cache refresh mechanism to identify further and eliminate more redundant data. Extensive experimental evaluations demonstrate that FSDedup can enhance the performance of the NVMM system further than the ESD. Experimental results show that FSDedup can improve both write and read speed by up to 1.8×, enhance Instructions Per Cycle by up to 1.5×, and reduce energy consumption by up to 2.0×, compared to ESD.

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.

Erasure coding has been widely deployed in modern large-scale storage systems for storage-efficient fault tolerance by storing stripes of data and parity chunks. Recently, enterprises explore the notion of wide stripes to suppress the fraction of parity chunks in each stripe to achieve extreme storage savings. However, how to efficiently generate wide stripes remains a non-trivial issue. In particular, re-encoding the currently stored stripes (termed narrow stripes) into wide stripes triggers substantial bandwidth overhead in relocating and regenerating the chunks for wide stripes. We propose StripeMerge, a wide-stripe generation mechanism that selects and merges narrow stripes into wide stripes, with the primary objective of minimizing the wide-stripe generation bandwidth. We prove the existence of an optimal scheme that does not incur any data transfer for wide-stripe generation, yet the optimal scheme is computationally expensive. To this end, we propose two heuristics that can be efficiently executed with only limited wide-stripe generation bandwidth overhead. We prototype StripeMerge and show via both simulations and Amazon EC2 experiments that the wide-stripe generation time can be reduced by up to 87.8% over a state-of-the-art storage scaling approach.

In-memory key-value stores are often used to speed up Big Data workloads on modern HPC clusters. To maintain their high availability, erasure coding has been recently adopted as a low-cost redundancy scheme instead of replication. Existing erasure-coded update schemes, however, have either low performance or high memory overhead. In this paper, we propose a novel parity logging-based architecture, HybridPL, which creates a hybrid of in-place update (for data and XOR parity chunks) and log-based update (for the remaining parity chunks), so as to balance the update performance and memory cost, while maintaining efficient single-failure repairs. We realize HybridPL as an in-memory key-value store called LogECMem, and further design efficient repair schemes for multiple failures. We prototype LogECMem and conduct experiments on different workloads. We show that LogECMem achieves better update performance over existing erasure-coded update schemes with low memory overhead, while maintaining high basic I/O and repair performance.