The first commercial Non-Volatile Memory (NVM) (i.e., Intel Optane DC Persistent Memory) exhibits limited parallelism, especially for write operations, which is generally neglected by existing NVM-aware file systems. Besides, the concurrent control of file systems also limits their scalability on high-performance NVMs under mainstream multi-core architectures. To effectively exploit full parallelism inherent in both NVMs and multi-core processors to enhance the overall performance, this paper proposes a novel scalable persistent memory file system, called SPMFS. SPMFS first partitions global metadata structures of the file system into per-core structures to distribute load and relieve contention. Second, SPMFS presents a fine-grained range lock to support concurrent accesses upon a file. Finally, SPMFS designs a dedicated I/O thread pool to offer optimal parallelism inherent in underlying NVM regardless of varying user-threads. We implement an SPMFS prototype and evaluate it under a variety of workloads generated by IOtest, Filebench, FIO, and production traces from Alibaba Pangu. The experiments show that SPMFS provides better scalability, and achieves up to 2.37 × write throughput improvement over state-of-the-art kernel NVM-aware file systems (Ext4-DAX, NOVA, and PMFS) and user-space file systems (Strata and Libnvmmio), without sacrificing the read performance.

Emerging byte-addressable Non-volatile memories (NVM) are promising techniques as memory-like storage. Researchers have developed many NVM-aware file systems to exploit the benefits of NVM. However, many early file systems are usually evaluated based on DRAM-based simulations or emulations. Their experimental results cannot present the actual behaviors upon real NVM devices, since the devices do not perform like slow DRAMs as expected. In this paper, we provide a comprehensive empirical study of NVM-aware file systems on the first commercially available byte-addressable NVM (i.e., the Intel Optane DC Persistent Memory Module (PMM)). We evaluate and analyze the performance of the kernel-level file systems (XFS-DAX, Ext4-DAX, PMFS, and NOVA) and the user-space file systems (Strata and Libnvmmio) on PMM with various synthetic and real-world benchmarks (FIO, Filebench, FXmark, Redis, etc.). We also employ different file system configurations and different PMM configurations to evaluate their performance impact. We believe that the experimental results and performance analysis will provide implications for the developers of various applications and storage systems to reap the full characteristics of NVMs.

Cloud disk provided to users by cloud providers has been a prevalent form of cloud storage. Assigning disk storage space for cloud disks among available data warehouses remains a challenging task, as the load characteristics of cloud disks are not known at the time of disk creation. Methods considering only subscribed capacity could be prone to result in resource under-utilization and load imbalance among warehouses. We propose a Smart Cloud Disk Allocation (S-CDA) approach, which uses clustering and classifying to predict the load information for new cloud disks, and then realizes multi-dimensional allocation based on Manhattan distance. Experimental results with realistic cloud workloads show that, compared with the existing one-dimensional allocation scheme, S-CDA increases the overall space/IOPS/disk bandwidth utilization, while decreasing the load imbalance.

Existing local file systems, designed to support a typical single-file access mode only, can lead to poor performance when accessing a batch of files, especially small files. This single-file mode essentially serializes accesses to batched files one by one, resulting in a large number of non-sequential, random, and often dependent I/Os between file data and metadata at the storage ends. Such access mode can further worsen the efficiency and performance of applications accessing massive files, such as data migration. We first experimentally analyze the root cause of such inefficiency in batch-file accesses. Then, we propose a novel batch-file access approach, referred to as BFO for its set of optimized Batch-File Operations , by developing novel BFOr and BFOw operations for fundamental read and write processes, respectively, using a two-phase access for metadata and data jointly. The BFO offers dedicated interfaces for batch-file accesses and additional processes integrated into existing file systems without modifying their structures and procedures. In addition, based on BFOr and BFOw, we also propose the novel batch-file migration BFOm to accelerate the data migration for massive small files. We implement a BFO prototype on ext4, one of the most popular file systems. Our evaluation results show that the batch-file read and write performances of BFO are consistently higher than those of the traditional approaches regardless of access patterns, data layouts, and storage media, under synthetic and real-world file sets. BFO improves the read performance by up to 22.4× and 1.8× with HDD and SSD, respectively, and it boosts the write performance by up to 111.4× and 2.9× with HDD and SSD, respectively. BFO also demonstrates consistent performance advantages for data migration in both local and remote situations.

In the era of 'dark silicon', modern data centers have to provision additional hardware resources to guarantee the Quality of Service (QoS) of applications in case of bursty workloads that typically occur in low frequency but high intensity. Fortunately, Computational Sprinting has proven to be an effective approach to boost the computing performance of many-core processor chips, which allows a chip to exceed its power and thermal limits temporarily by turning on all processor cores and absorbing the extra heat dissipation with novel phase-changing materials. Consequently, it offers a promising way to deal with these occasional workload bursts by unleashing the full potentials of hardware, avoiding deploying extra computing resources. In this work, we propose ESprint, a QoS-aware management system based on an effective feedback control mechanism for latency-critical applications in data centers. ESprint can perform computational sprinting by precisely scheduling core count, frequency levels, and sprinting duration, serving bursty workloads without QoS violation under the thermal constraint. Specifically, ESprint effectively predict load intensity in the next time interval, and further dynamically allocates appropriate computing resources to minimize actual power consumption. Our prototype-based evaluation results show that ESprint achieves up to 1.92x improvement on energy efficiency for typical workloads while ensuring QoS, over the non-sprinting strategy. We also explore the design space among energy efficiency, core count/frequency scaling techniques, workload characteristics, burst intensity, and QoS requirements, and draw several key insights to guide the effective use of computational sprinting in data centers.

Existing local file systems, designed to support a typical single-file access pattern only, can lead to poor performance when accessing a batch of files, especially small files. This single-file pattern essentially serializes accesses to batched files one by one, resulting in a large number of non-sequential, random, and often dependent I/Os between file data and metadata at the storage ends. We first experimentally analyze the root cause of such inefficiency in batch-file accesses. Then, we propose a novel batch-file access approach, referred to as BFO for its set of optimized Batch-File Operations, by developing novel BFOr and BFOw operations for fundamental read and write processes respectively, using a two-phase access for metadata and data jointly. The BFO offers dedicated interfaces for batch-file accesses and additional processes integrated into existing file systems without modifying their structures and procedures. We implement a BFO prototype on ext4, one of the most popular file systems. Our evaluation results show that the batch-file read and write performances of BFO are consistently higher than those of the traditional approaches regardless of access patterns, data layouts, and storage media, with synthetic and real-world file sets. BFO improves the read performance by up to 22.4× and 1.8× with HDD and SSD respectively; and boosts the write performance by up to 111.4× and 2.9× with HDD and SSD respectively. BFO also demonstrates consistent performance advantages when applied to four representative applications, Linux cp, Tar, GridFTP, and Hadoop.