Abstract The ‘out-of-place’ update nature of Solid State Drives (SSDs) introduces a security risk. Scrubbing, a secure deletion method, mitigates this issue but negatively affects SSD endurance and requires redundant data for recovery due to page errors. Previously, the RAID-5 based scheme was used to manage redundant data. While effective for reading, it causes significant write latency due to the channel blocking issue. In response, we propose FlashFox, which integrates secret sharing and Reed-Solomon coding into SSDs, enabling the application of scrubbing within an encrypted storage environment. This innovation ensures secure deletion by cleaning up sensitive data, thereby reducing wear on SSD endurance. Furthermore, we have developed a RAID-4-based scheme for implementing FlashFox on SSDs. This scheme, by assigning specific channels to manage redundant data, successfully avoids the channel blocking issue prevalent in RAID-5. Experimental results show FlashFox reduces endurance wear by at least 15% compared to traditional scrubbing methods and writing response delay by at least 8$\times $ compared to RAID-5 based scheme.

The massive computational and memory requirements of deep convolutional neural networks (DCNNs) have led to the development of neural network (NN) accelerators. However, as DCNN models grow in size, the demands on NN accelerators in terms of performance, memory bandwidth, and power efficiency continue to increase. We, therefore, present 9Ring , a flexible and efficient DCNN accelerator that takes full advantage of 3D-stacked memory, focusing on its hardware architecture, software scheduling, and optimization strategy. In particular, we first show that the mismatch between DCNN accelerators and DCNN models can lead to increased energy consumption and performance bottlenecks. We then present three flexible dataflow scheduling strategies to mitigate this mismatch. Afterward, we introduce an energy efficiency analysis tool that can automatically search for the optimal scheduling scheme with respect to different DCNN models for energy efficiency. Finally, we conduct an empirical study showing that 9Ring can reduce energy consumption by 31.4% and 43.9% on average, and improve performance by 12% and 10% on average, compared with Tetris and the NN accelerators on conventional low-power DRAM memory systems, respectively.

Over the past four decades, Learning Classifier Systems (LCSs) have faced challenges in producing accurate and interpretable models for domains with continuous features, mainly due to the irrelevance issue caused by genotypic methods. These methods directly modify genotypes (conditions), leading to the creation of irrelevant rules. Phenotypic LCSs, which first modify a rule's phenotype (covered instance set) before altering its genotype, can avoid this issue. However, previous phenotypic LCSs struggle with overfitting, resulting in lower testing performance. In response, we propose a novel phenotypic LCS featuring innovations: 1) a heterogeneous phenotype approach in the rule discovery mechanism to alleviate overfitting, and 2) Informed Mutation leverages the inherent neighbouring of similar instances to enhance rule generalization, thereby improving model interpretability. The proposed LCS demonstrates its success with superior testing performance and more interpretable models in all experiments compared to other LCSs. Notably, in a problem with 2048 features, the proposed LCS model outperformed the genotypic UCS by achieving a 97.4% testing accuracy with just 13 rules, compared to the UCS's 9961 rules but only 49.9% accuracy.

Directory tree walks on parallel file systems are costly operations frequently required by many storage management tasks. Even listing the contents of a single directory can take minutes to hours for huge directories, as the tree walk performance of parallel file systems in Linux is severely throttled by sequentially accessing distributed metadata for each file through the syscall interface.

Oblivious Random Access Machines (ORAMs) allow cloud users to access remote data without leaking access patterns. Current ORAM solutions achieve this goal at expense of either increasing bandwidth consumption by a factor of$O(\log N)$, where$N$is the number of data blocks, or relying on homomorphic encryption for bandwidth amplification reduction to$O(1)$. Furthermore, most ORAMs are only effective for a single user, while the solutions for multi-user scenarios often induce security or performance problems. This article introducesTianji— an asynchronous multi-user Shamir-based ORAM system — which supports asynchronous network access scenarios for multiple users with improved security and performance.Tianjiis implemented on top ofS$^{3}$3ORAM$^+$+—an extension of the state-of-the-art Shamir-based S$^{3}$ORAM with a new non-eviction data write-back scheme to achieve$O(1)$consumption in both bandwidth amplification and storage capacity. Our experimental results show that the proposedTianjiwithS$^{3}$3ORAM$^+$+can significantly outperform the state-of-the-art multi-userTaoStorein terms of access latency and client scalability. Additionally, its average response time is relatively stable when client loads increase.

Dual-mode phase change memory (PCM) allows each PCM cell to operate concurrently in two modes: multi-level cell (MLC) and single-level cell (SLC), each with its own distinct features in transfer speeds and power consumption. This paper describes the design and implementation of a log-structured file system, called DPLFS, which is proposed to provide a storage system with high throughput and low latencies. DPLFS achieves this merit by exploiting the dual-mode PCM. In particular, file data in DPLFS is stored in MLC, while its metadata is maintained in SLC, and will be written back to MLC when the space in SLC is not enough. DPLFS adapts log-structured file system techniques to exploit fast random accesses in SLC and large capacity in MLC and maintains a file directory in SLC to speed up the data retrieval speed. Our experimental results show a 21% in write-intensive workloads and a 11% in read-intensive workloads, compared to the state-of-the-art file systems.

In parallel and distributed file systems, caching can improve data performance and metadata operations. Currently, most distributed file systems adopt a write-back data cache for performance and a write-through metadata cache for simplifying consistency. However, with modern file systems scales and workloads, write-through metadata caching can impact overall file system performance, e.g., through lock contention and heavy RPC loads required for namespace synchronization and transaction serialization. This paper proposes a novel metadata write-back caching (MetaWBC) mechanism to improve the performance of metadata operations in distributed environments. To achieve extreme metadata performance, we developed a fast, lightweight, and POSIXcompatible memory file system as a metadata cache. Further, we designed a file caching state machine and included other performance optimizations. We coupled MetaWbc with Lustre and evaluated that MetaWbc can outperform the native parallel file system by up to 8x for metadata-intensive benchmarks, and up to 7x for realistic workloads in throughput.

Abstract The write performance of multi-level cell (MLC) is several times slower than single-level cell (SLC); however, the cost per bit of MLC is much lower than SLC. Dual-mode flash (the medium can be partially switched to SLC mode by programming only 1 bit in some cells) can combine SLC and MLC to provide trading density opportunity for performance. In this paper, we present Hercules—a hybrid storage system that couples dual-mode flash memory and hard drive disk (HDD)—based on the content locality principle for high storage performance. The data are divided into two types: the reference data for read operation and the delta data for write operation. The reference data are stored in SLC and the delta data in MLC or HDD in sequential orders. Hercules organizes the metadata for the mapping of the physical locations of the reference blocks and the delta data of the original blocks, intelligently identifies hot/cold data and performs the data migration between MLC and disk for performance improvements. To validate our findings, we implemented Hercules and made evaluation to show that Hercules can effectively improve the data access speed and reduce the response time, compared with the Flashcache storage structure, and in particular, with Hercules, we can achieve 10% performance improvement over the system in absence of hot delta data caching.

In high-performance computing (HPC), data and metadata are stored on special server nodes and client applications access the servers’ data and metadata through a network, which induces network latencies and resource contention. These server nodes are typically equipped with (slow) magnetic disks, while the client nodes store temporary data on fast SSDs or even on non-volatile main memory (NVMM). Therefore, the full potential of parallel file systems can only be reached if fast client side storage devices are included into the overall storage architecture. In this article, we propose an NVMM-based hierarchical persistent client cache for the Lustre file system (NVMM-LPCC for short). NVMM-LPCC implements two caching modes: a read and write mode (RW-NVMM-LPCC for short) and a read only mode (RO-NVMM-LPCC for short). NVMM-LPCC integrates with the Lustre Hierarchical Storage Management (HSM) solution and the Lustre layout lock mechanism to provide consistent persistent caching services for I/O applications running on client nodes, meanwhile maintaining a global unified namespace of the entire Lustre file system. The evaluation results presented in this article show that NVMM-LPCC can increase the average read throughput by up to 35.80 times and the average write throughput by up to 9.83 times compared with the native Lustre system, while providing excellent scalability.

Summary LSM‐trie‐based key‐value (KV) store is often used to manage an ultralarge dataset in reality by introducing a number of sublevels at each level, its linear growth pattern can fairly reduce the write amplification in store operations. Although this design is effective for the write operation, the last level holds a large proportion of KV items, leading to the extreme imbalance of data distribution. Therefore, to support efficient read, we need to carefully consider this imbalance. On the other hand, to ensure that acquired data is latest, the LSM‐trie needs to search the dataset at different levels one by one, and this search method may take a lot of unnecessary time. When the number of items is ultralarge, the random lookup performance may be poor due to the imbalance data distribution. To address this issue, we improve the read performance of the LSM‐trie by changing its serial search to parallel search, using two threads to simultaneously search at the last level and other levels, respectively. Our experiment results show that the read performance of the LSM‐trie can be improved up to 98.35% and on average 71.55%.

Most high-performance computing (HPC) clusters use a global parallel file system to enable high data throughput. The parallel file system is typically centralized and its storage media are physically separated from the compute cluster. Compute nodes as clients of the parallel file system are often additionally equipped with SSDs. The node internal storage media are rarely well-integrated into the I/O and compute workflows. How to make full and flexible use of these storage media is therefore a valuable research question.