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This article presents crash-consistent Non-Volatile Memory Express (ccNVMe), a novel extension of the NVMe that defines how host software communicates with the non-volatile memory (e.g., solid-state drive) across a PCI Express bus and RDMA-capable networks with both crash consistency and performance efficiency. Existing storage systems pay a huge tax on crash consistency, and thus cannot fully exploit the multi-queue parallelism and low latency of the NVMe and RDMA interfaces. ccNVMe alleviates this major bottleneck by coupling the crash consistency to the data dissemination. This new idea allows the storage system to achieve crash consistency by taking the free rides of the data dissemination mechanism of NVMe, using only two lightweight memory-mapped I/Os (MMIOs), unlike traditional systems that use complex update protocol and synchronized block I/Os. ccNVMe introduces a series of techniques including transaction-aware MMIO/doorbell and I/O command coalescing to reduce the PCIe traffic as well as to provide atomicity. We present how to build a high-performance and crash-consistent file system named MQFS atop ccNVMe. We experimentally show that MQFS increases the IOPS of RocksDB by 36% and 28% compared to a state-of-the-art file system and Ext4 without journaling, respectively. [ABSTRACT FROM AUTHOR]

Non-volatile memory and remote direct memory access (RDMA) provide extremely high performance in storage and network hardware. However, existing distributed file systems strictly isolate file system and network layers, and the heavy layered software designs leave high-speed hardware under-exploited. In this article, we propose an RDMA-enabled distributed persistent memory file system, Octopus+, to redesign file system internal mechanisms by closely coupling non-volatile memory and RDMA features. For data operations, Octopus+ directly accesses a shared persistent memory pool to reduce memory copying overhead, and actively fetches and pushes data all in clients to rebalance the load between the server and network. For metadata operations, Octopus+ introduces self-identified remote procedure calls for immediate notification between file systems and networking, and an efficient distributed transaction mechanism for consistency. Octopus+ is enabled with replication feature to provide better availability. Evaluations on Intel Optane DC Persistent Memory Modules show that Octopus+ achieves nearly the raw bandwidth for large I/Os and orders of magnitude better performance than existing distributed file systems. [ABSTRACT FROM AUTHOR]

The rapidly increasing data in recent years requires the datacenter infrastructure to store and process data with extremely high throughput and low latency. Fortunately, persistent memory (PM) and RDMA technologies bring new opportunities towards this goal. Both of them are capable of delivering more than 10 GB/s of bandwidth and sub-microsecond latency. However, our past experiences and recent studies show that it is non-trivial to build an efficient and distributed storage system with such new hardware. In this article, we design and implement TH-DPMS (TsingHua Distributed Persistent Memory System) based on persistent memory and RDMA, which unifies the memory, file system, and key-value interface in a single system. THDPMS is designed based on a unified distributed persistent memory abstract, pDSM. pDSM acts as a generic layer to connect the PMs of different storage nodes via high-speed RDMA network and organizes them into a global shared address space. It provides the fundamental functionalities, including global address management, space management, fault tolerance, and crash consistency guarantees. Applications are enabled to access pDSM with a group of flexible and easy-to-use APIs by using either raw read/write interfaces or the transactional ones with ACID guarantees. Based on pDSM, we implement a distributed file system and a keyvalue store named pDFS and pDKVS, respectively. Together, they uphold TH-DPMS with high-performance, low-latency, and fault-tolerant data storage. We evaluate TH-DPMS with both micro-benchmarks and realworld memory-intensive workloads. Experimental results show that TH-DPMS is capable of delivering an aggregated bandwidth of 120 GB/s with 6 nodes. When processing memory-intensive workloads such as YCSB and Graph500, TH-DPMS improves the performance by one order of magnitude compared to existing systems and keeps consistent high efficiency when the workload size grows to multiple terabytes. [ABSTRACT FROM AUTHOR]

Data encryption and authentication are essential for secure non-volatile memory (NVM). However, the introduced security metadata needs to be atomically written back to NVM along with data, so as to provide crash consistency, which unfortunately incurs high overhead. To support fine-grained data protection and fast recovery for a secure NVM system without compromising the performance, we propose ShieldNVM. It first proposes an epoch-based mechanism to aggressively cache the security metadata in the metadata cache while retaining the consistency of them in NVM. Deferred spreading is also introduced to reduce the calculating overhead for data authentication. Leveraging the ability of data hash message authentication codes, we can always recover the consistent but old security metadata to its newest version. By recording a limited number of dirty addresses of the security metadata, ShieldNVMachieves fast recovering the secure NVM system after crashes. Compared to Osiris, a state-of-the-art secure NVM, ShieldNVM reduces system runtime by 39.1% and hash message authentication code computation overhead by 80.5% on average over NVM workloads. When system crashes happen, ShieldNVM's recovery time is orders of magnitude faster than Osiris. In addition, ShieldNVM also recovers faster than AGIT, which is the Osiris-based state-of-the-art mechanism addressing the recovery time of the secure NVM system. Once the recovery process fails, instead of dropping all data due to malicious attacks, ShieldNVM is able to detect and locate the area of the tampered data with the help of the tracked addresses. [ABSTRACT FROM AUTHOR]

As the cost-per-bit of solid state disks is decreasing quickly, SSDs are supplanting HDDs in many cases, including the primary storage of key-value stores. However, simply deploying LSM-tree-based key-value stores on commercial SSDs is inefficient and induces heavy write amplification and severe garbage collection overhead under write-intensive conditions. The main cause of these critical issues comes from the triple redundant management functionalities lying in the LSM-tree, file system and flash translation layer, which block the awareness between key-value stores and flash devices. Furthermore, we observe that the performance of LSM-tree-based key-value stores is improved little by only eliminating these redundant layers, as the I/O stacks, including the cache and scheduler, are not optimized for LSM-tree’s unique I/O patterns. To address the issues above, we propose FlashKV, an LSM-tree based key-value store running on openchannel SSDs. FlashKV eliminates the redundant management and semantic isolation by directly managing the raw flash devices in the application layer.With the domain knowledge of LSM-tree and the open-channel information, FlashKV employs a parallel data layout to exploit the internal parallelism of the flash device, and optimizes the compaction, caching and I/O scheduling mechanisms specifically. Evaluations show that FlashKV effectively improves system performance by 1.5× to 4.5× and decreases up to 50% write traffic under heavy write conditions, compared to LevelDB. [ABSTRACT FROM AUTHOR]

As the cost-per-bit of solid state disks is decreasing quickly, SSDs are supplanting HDDs in many cases, including the primary storage of key-value stores. However, simply deploying LSM-tree-based key-value stores on commercial SSDs is inefficient and induces heavy write amplification and severe garbage collection overhead under write-intensive conditions. The main cause of these critical issues comes from the triple redundant management functionalities lying in the LSM-tree, file system and flash translation layer, which block the awareness between key-value stores and flash devices. Furthermore, we observe that the performance of LSM-tree-based key-value stores is improved little by only eliminating these redundant layers, as the I/O stacks, including the cache and scheduler, are not optimized for LSM-tree's unique I/O patterns. To address the issues above, we propose FlashKV, an LSM-tree based key-value store running on openchannel SSDs. FlashKV eliminates the redundant management and semantic isolation by directly managing the raw flash devices in the application layer. With the domain knowledge of LSM-tree and the open-channel information, FlashKV employs a parallel data layout to exploit the internal parallelism of the flash device, and optimizes the compaction, caching and I/O scheduling mechanisms specifically. Evaluations show that FlashKV effectively improves system performance by 1.5× to 4.5× and decreases up to 50% write traffic under heavy write conditions, compared to LevelDB. [ABSTRACT FROM AUTHOR]

Persistent memory provides data durability in main memory and enables memory-level storage systems. To ensure consistency of such storage systems, memory writes need to be transactional and are carefully moved across the boundary between the volatile CPU cache and the persistent main memory. Unfortunately, cache management in the CPU cache is hardware-controlled. Legacy transaction mechanisms, which are designed for disk-based storage systems, are inefficient in ordered data persistence of transactions in persistent memory. In this article, we propose the Blurred Persistence mechanism to reduce the transaction overhead of persistent memory by blurring the volatility-persistence boundary. Blurred Persistence consists of two techniques. First, Execution in Log executes a transaction in the log to eliminate duplicated data copies for execution. It allows persistence of the volatile uncommitted data, which are detectable with reorganized log structure. Second, Volatile Checkpoint with Bulk Persistence allows the committed data to aggressively stay volatile by leveraging the data durability in the log, as long as the commit order across threads is kept. By doing so, it reduces the frequency of forced persistence and improves cache efficiency. Evaluations show that our mechanism improves system performance by 56.3% to 143.7% for a variety of workloads. [ABSTRACT FROM AUTHOR]

Carbon emissions from energy consumption of Shandong province from 1995 to 2012 are calculated. Three zero-residual decomposition models (LMDI,MRCI and Shapley value models) are introduced for decomposing carbon emissions. Based on the results, Kendall coordination coefficient method is employed for testing their compatibility, and an optimal weighted combination decomposition model is constructed for improving the objectivity of decomposition. STIRPAT model is applied to evaluate the impact of each factor on carbon emissions.The results show that, using 1995 as the base year, the cumulative effects of population, per capita GDP, energy consumption intensity, and energy consumption structure of Shandong province in 2012 are positive, while the cumulative effect of industrial structure is negative. Per capita GDP is the largest driver of the increasing carbon emissions and has a great impact on carbon emissions; energy consumption intensity is a weak driver and has certain impact on carbon emissions; population plays a weak driving role, but it has themost significant impact on carbon emissions; energy consumption structure is a weak driver of the increasing carbon emissions and has a weak impact on carbon emissions; industrial structure has played a weak inhibitory role, and its impact on carbon emissions is great. [ABSTRACT FROM AUTHOR]

We present Nap, a black-box approach that converts concurrent persistent memory (PM) indexes into nonuniform memory access (NUMA)-aware counterparts. Based on the observation that real-world workloads always feature skewed access patterns, Nap introduces a NUMA-aware layer (NAL) on the top of existing concurrent PM indexes, and steers accesses to hot items to this layer. The NAL maintains (1) per-node partial views in PM for serving insert/update/delete operations with failure atomicity and (2) a global view in DRAM for serving lookup operations. The NAL eliminates remote PM accesses to hot items without inducing extra local PM accesses. Moreover, to handle dynamic workloads, Nap adopts a fast NAL switch mechanism. We convert five state-of-the-art PM indexes using Nap. Evaluation on a four-node machine with Optane DC Persistent Memory shows that Nap can improve the throughput by up to 2.3× and 1.56× under write-intensive and read-intensive workloads, respectively. [ABSTRACT FROM AUTHOR]