Persistent memory (PM) file systems have been developed to achieve high performance by exploiting the advanced features of PMs, including nonvolatility, byte addressability, and dynamic random access memory (DRAM) like performance. Unfortunately, these PMs suffer from limited write endurance. Existing space management strategies of PM file systems can induce a severely unbalanced wear problem, which can damage the underlying PMs quickly. In this paper, we propose a Wear-leveling-aware Multi-grained Allocator, called WMAlloc, to achieve the wear leveling of PMs while improving the performance of file systems. WMAlloc adopts multiple min-heaps to manage the unused space of PMs. Each heap represents an allocation granularity. Then, WMAlloc allocates less-worn blocks from the corresponding min-heap for allocation requests. Moreover, to avoid recursive split and inefficient heap locations in WMAlloc, we further propose a bitmap-based multi-heap tree (BMT) to enhance WMAlloc, namely, WMAlloc-BMT. We implement WMAlloc and WMAlloc-BMT in the Linux kernel based on NOVA, a typical PM file system. Experimental results show that, compared with the original NOVA and dynamic wear-aware range management (DWARM), which is the state-of-the-art wear-leveling-aware allocator of PM file systems, WMAlloc can, respectively, achieve 4.11× and 1.81× maximum write number reduction and 1.02× and 1.64× performance with four workloads on average. Furthermore, WMAlloc-BMT outperforms WMAlloc with 1.08× performance and achieves 1.17× maximum write number reduction with four workloads on average.

Block-based file systems, such as Btrfs, utilize the copy-on-write (CoW) mechanism to guarantee data consistency on solid-state drives (SSDs). Open-channel SSD provides opportunities for in-depth optimization of block-based file systems. However, existing systems fail to co-design the two-layer semantics and cannot take full advantage of the open-channel characteristics. Specifically, synchronizing an overwrite in Btrfs will copy-on-write all pages in the update path and induce severe write amplification. In this paper, we propose a hybrid fine-grained copy-on-write and journaling mechanism (HyFiM) to address these problems. We first utilize persistent memories to preserve the address mapping table of open-channel SSD. Then, we design an intra-FTL copy-on-write mechanism (IFCoW) that eliminates the recursive updates caused by overwrites. Finally, we devise fine-grained metadata journals (FGMJ) to guarantee the consistency of metadata with minimum overhead. We prototype HyFiM based on Btrfs in the Linux kernel. Comprehensive evaluations demonstrate that HyFiM can outperform over Btrfs by 30.77% and 33.82% for sequential and random overwrites, respectively.

Emerging applications like machine learning in embedded devices (e.g., satellites and vehicles) require huge storage space, which recently stimulates the widespread deployment of large-scale flash memory in IoT devices. However, existing embedded file systems fall short in managing large-capacity storage efficiently for two reasons. First, prior arts store data structures of file systems either in flash or in main memory, which severely magnifies the scarcity of computing and memory resources. Moreover, the fine-grained metadata management in the existing embedded file systems induces significant energy consumption for large-capacity storage. In this paper, we propose a novel embedded file system, ELOFS, to tackle the above issues and manage large-capacity NAND flash on resource-scarce devices. ELOFS is made efficient through three novel techniques. First, we redefine the space management granularity and streamline the metadata to speed up the mounting performance. In addition, we design hybrid file structures to adapt dissimilar access patterns of embedded devices. Furthermore, ELOFS provides opportunities for in-depth cooperation with application-specific systems. We implement ELOFS with Memory Technology Device (MTD) interfaces, and the experimental results show that ELOFS outperforms YAFFS and UBIFS in terms of write, read, and deletions with orders of magnitude reductions on memory footprint and mounting time.

Solid-state drives (SSDs) have been widely deployed in high-performance data center environments, where multiple tenants usually share the same hardware. However, traditional SSDs distribute the users’ incoming data uniformly across all SSD channels, which leads to numerous access conflicts. Meanwhile, SSDs that blindly allocate one or several channels to one tenant sacrifice device parallelism and capacity. When SSDs are shared by tenants with different access patterns, inappropriate channel allocation results in SSD performance degradation. In this article, we propose a self-adapting channel allocation mechanism, named SSDKeeper, for multiple tenants that share one SSD. SSDKeeper employs a machine learning-assisted algorithm to take full advantage of SSD parallelism while providing performance isolation. By collecting multitenant access patterns, SSDKeeper predicts an optimal channel allocation strategy for multiple tenants using the well-trained model. To further consume the blocks in different channels evenly, SSDKeeper equips with a novel channel swap scheme to prolong the SSD lifespan. Comparing with traditional SSDs, SSDKeeper reduces the overall latency of read and write by 12.6% and the lifespan is prolonged up to$3.7\times $.

The mismatch between logical and physical I/O granularity inhibits the deployment of embedded file systems. Most existing embedded file systems manage logical space with a small unit, which is no longer the case of the flash operation granularity. Manually enlarging the logical I/O granularity of file systems requires enormous transplanting efforts. Moreover, large logical pages signify the write amplification problem, which turns to severe space consumption and performance collapse. This article designs a novel storage middleware, NV-middle, for legacy-embedded file systems with large-capacity flash memories. Legacy-embedded storage schemes can be smoothly transplanted into new platforms with different hardware read/write granularity. Moreover, the legacy optimization schemes can be maximally reserved, without inducing write amplification problems. We implement NV-middle with the state-of-the-art embedded file system, YAFFS2. Comprehensive evaluations show that NV-middle can achieve times of performance improvement over manually transplanted YAFFS2 with various workloads.

Existing persistent memory file systems usually store inodes in fixed locations, which ignores the external and internal imbalanced wears of inodes on the persistent memory (PM). Therefore, the PM for storing inodes can be easily damaged. Existing solutions achieve low accuracy of wear-leveling with high-overhead data migrations. In this paper, we propose a Lightweight and Multi-grained Wear-leveling Mechanism, called LMWM, to solve these problems. We implement the proposed LMWM in Linux kernel based on NOVA, a typical persistent memory file system. Compared with MARCH, the state-of-theart wear-leveling mechanism for inode table, experimental results show that LMWM can improve 2.5× lifetime of PM and 1.12× performance, respectively.

Emerging applications like machine learning in embedded devices (e.g., satellite and vehicles) require huge storage space, which recently stimulates the widespread deployment of large-capacity flash memory in IoT devices. However, existing embedded file systems fall short in managing large-capacity storage efficiently for excessive memory consumption and poor booting performance. In this paper, we propose a novel embedded file system, LOFFS, to tackle the above issues and manage large-capacity NAND flash on resource-limited embedded devices. We redesign the space management mechanisms and construct hybrid file structures to achieve high performance with minimum resource occupation. We have implemented LOFFS in Linux, and the experimental results show that LOFFS outperforms YAFFS by 55.8% on average with orders of magnitude reductions on memory footprint.

Existing persistent memory file systems can significantly improve the performance by utilizing the advantages of emerging Persistent Memories (PMs). Especially, they can employ superpages (e.g., 2MB a page) of PMs to alleviate the overhead of locating file data and reduce TLB misses. Unfortunately, superpage also induces two critical problems. First, the data consistency of file systems using superpages causes severe write amplification during overwrite of file data. Second, existing management of superpages may lead to large waste of PM space. In this paper, we propose a Virtual Superpage Mechanism (VSM) to solve the problems by taking advantages of virtual address space. On one hand, VSM adopts multi-grained copy-on-write mechanism to reduce the write amplification while ensuring data consistency. On the other hand, VSM presents zero-copy file data migration mechanism to eliminate the loss of space utilization efficiency caused by superpages. We implement the proposed VSM mechanism in Linux kernel based on PMFS. Compared with the original PMFS and NOVA, the experimental results show that VSM improves 36% and 14% on average for write and read performance, respectively. Meanwhile, VSM can achieve the same space utilization efficiency of file system that uses the normal 4KB pages to organize files.

Emerging Persistent Memories (PMs) are promised to revolutionize the storage systems by providing fast, persistent data access on the memory bus. Therefore, persistent memory file systems are developed to achieve high performance by exploiting the advanced features of PMs. Unfortunately, the PMs have the problem of limited write endurance. Furthermore, the existing space management strategies of persistent memory file systems usually ignore this problem, which can cause that the write operations concentrate on a few cells of PM. Then, the unbalanced writes can damage the underlying PMs quickly, which seriously damages the data reliability of the file systems. However, existing wear-leveling-aware space management techniques mainly focus on improving the wear-leveling accuracy of PMs rather than reducing the overhead, which can seriously reduce the performance of persistent memory file systems. In this paper, we propose a Wear-Leveling-Aware Multi-Grained Allocator, called WMAlloc, to achieve the wear-leveling of PM while improving the performance for persistent memory file systems. WMAlloc adopts multiple heap trees to manage the unused space of PM, and each heap tree represents an allocation granularity. Then, WMAlloc allocates less-worn required blocks from the heap tree for each allocation. We implement the proposed WMAlloc in Linux kernel based on NOVA, a typical persistent memory file system. Compared with DWARM, the state-of-the-art and wear-leveling-aware space management technique, experimental results show that WMAlloc can achieve 1.52× lifetime of PM and 1.44× performance improvement on average.

The persistent memory file systems can significantly improve the performance by utilizing the advanced features of emerging Persistent Memories (PMs). Unfortunately, the PMs have the problem of limited write endurance. However, the design of persistent memory file systems usually ignores this problem. Accordingly, the write-intensive applications, especially for the malicious wear attack virus, can damage underlying PMs quickly by calling the common interfaces of persistent memory file systems to write a few cells of PM continuously. Which seriously threat to the data reliability of file systems. However, existing solutions to solve this problem based on persistent memory file systems are not systematic and ignore the unlimited write endurance of DRAM. In this paper, we propose a malicious wear detection and defense mechanism for persistent memory file systems, called Themis, to solve this problem. The proposed Themis identifies the malicious wear attack according to the write traffic and the set lifespan of PM. Then, we design a wear-leveling scheme and migrate the writes of malicious wear attackers into DRAM to improve the lifespan of PMs. We implement the proposed Themis in Linux kernel based on NOVA, a state-of-the-art persistent memory file system. Compared with DWARM, the state-of-the-art and wear-aware memory management technique, experimental results show that Themis can improve 5774× lifetime of PM and 1.13× performance, respectively.

Solid state drives (SSDs) have been widely deployed in high performance data center environments, where multiple tenants usually share the same hardware. However, traditional SSDs distribute the users' incoming data uniformly across all SSD channels, which leads to numerous access conflicts. Meanwhile, SSDs that statically allocate one or several channels to one tenant sacrifice device parallelism and capacity. When SSDs are shared by tenants with different access patterns, inappropriate channel allocation results in SSDs performance degradation. In this paper, we propose a self-adapting channel allocation mechanism, named SSDKeeper, for multiple tenants to share one SSD. SSDKeeper employs a machine learning assisted algorithm to take full advantage of SSD parallelism while providing performance isolation. By collecting multi-tenant access patterns and training a model, SSDKeeper selects an optimal channel allocation strategy for multiple tenants with the lowest overall response latency. Experimental results show that SSDKeeper improves the overall performance by 24% with negligible overhead.

We have witnessed the tremendous success of deep neural networks. However, this success comes with the considerable memory and computational costs which make it difficult to deploy these networks directly on resource-constrained embedded systems. To address this problem, we propose TaijiNet, a separable binary network, to reduce the storage and computational overhead while maintaining a comparable accuracy. Furthermore, we also introduce a strategy called partial binarized convolution which binarizes only unimportant kernels to efficiently balance network performance and accuracy. Our approach is evaluated on the CIFAR-10 and ImageNet datasets. The experimental results show that with the proposed TaijiNet, the separable binary versions of AlexNet and ResNet-18 can achieve 26× and 6.4× compression rates with comparable accuracy when comparing with the full-precision versions respectively. In addition, by adjusting the PCA threshold, the xnor version of Taiji-AlexNet improves accuracy by 4-8 percent comparing with other state-of-the-art methods.

Conventional journaling file systems, such as Ext4, guarantee data consistency by writing in-memory dirty inodes to block devices twice. The write back of inodes may contain up to 80% clean inode that is unnecessary to be written back, which caused severe write amplification problem and largely reduce performance since the size of an inode is several times less than the size of a basic unit for updating the block device. Emerging persistent memories (PMs), such as phase change memory, provide the possibility for storing the offset of inodes in memory persistently. In this paper, we propose an efficient scheme, Updating Frequency based Inode Aggregation (UFIA), to reduce the write amplification of dirty inodes using PM. The main idea of UFIA is to identify the frequently-updated inodes and reorganize them in adjacent physical locations on block device. Firstly, UFIA adopts PM as an inode mapping table for remapping logical inodes to any physical inodes. Secondly, we design an efficient algorithm for UFIA to identify and reorganize the frequently-updated inodes. We implement UFIA and integrate it into Ext4 (denoted by UFIA-Ext4) in Linux kernel 4.4.4. The experiments are conducted with widely-used benchmark Filebench. Compared with original Ext4, the experimental results show that UFIA significantly reduces the write amplification of inodes and improves 54% of the performance on average.

Index structures can significantly accelerate the data retrieval operations in data intensive systems, such as databases. Tree structures, such as B+-tree alike, are commonly employed as index structures; however, we found that the tree structure may not be appropriate for Non-Volatile Memory (NVM) in terms of the requirements for high-performance and high-endurance. This paper studies what is the best index structure for NVM-based systems and how to design such index structures. The design of an NVM-friendly index structure faces a lot of challenges. First, in order to prolong the lifetime of NVM, the write activities on NVM should be minimized. To this end, the index structure should be as simple as possible. The index proposed in this paper is based on the simplest data structure, i.e., linked list. Second, the simple structure brings challenges to achieve high-performance data retrieval operations. To overcome this challenge, we design a novel technique by explicitly building up a contiguous virtual address space on the linked list, such that efficient search algorithms can be performed. Third, we need to carefully consider data consistency issues in NVM-based systems, because the order of memory writes may be changed and the data content in NVM may be inconsistent due to write-back effects of CPU cache. This paper devises a novel indexing scheme, called “Virtual Linear Addressable Buckets” (VLAB). We implement VLAB in a storage engine and plug it into MySQL. Evaluations are conducted on an NVDIMM workstation using YCSB workloads and real-world traces. Results show that write activities of the state-of-the-art indexes are 6.98 times more than ours; meanwhile, VLAB achieves 2.53 times speedup.

With the development of embedded devices, elevator group systems that manage elevators can be designed in an intelligent way. In the design of elevator group systems, one of the most important problems is to determine the “range assignment” for each elevator, which indicates the floors that an elevator will serve. In reality, the traffic loads of a building are different in terms of time periods, called fluctuant traffic loads, which makes the above problem much more challenging. The objective of this paper is to determine the optimal range assignment that can maximize the number of passengers served in a certain amount of time. The elevator group system can adapt to varying traffic loads and achieve fault tolerance by conducting range reassignment. In this paper, we build a Mixed Integer Linear Programming (MILP) to find the optimal range assignment. However, MILP suffers from large computational complexities and it is impractical since the elevator group system needs to response to fluctuant traffic loads in real-time. Therefore, we devise efficient algorithms to obtain near optimal solutions. Experimental results show that we can achieve 48% and 25% improvements on average in the completion time and the average waiting time, respectively.