In-memory Key-Value (KV) stores are widely deployed in modern data centers. These systems generally use DRAM as their storage medium, causing huge hardware costs. The emerging persistent memory (PMEM) is a potential substitute for DRAM, which has a lower price and larger capacity, but lower access speed and bandwidth. Many prior studies strive to build hybrid memory systems to retain both the advantages of DRAM and PMEM. However, they are either application agnostic or simply take DRAM as a cache, which are both not efficient for in-memory KV stores. In this paper, we propose SwapKV, a well-designed in-memory KV store for hybrid DRAM-PMEM system. SwapKV has several promising properties. First, SwapKV combines DRAM and PMEM to a uniform memory pool and only stores one copy of data, which maximizes capacity utilization. Second, SwapKV maps all writing operations to DRAM and migrates data to PMEM with large blocks asynchronously, which mitigates the intrinsic inefficiency of PMEM for writing operations. Third, SwapKV maintains the hot data in DRAM through an efficient hotness filtering and data swapping mechanism, which ensures high system throughput and responsiveness. We implement SwapKV and evaluate it under various workload patterns. The results demonstrate that SwapKV improves the throughput by 11\%$\sim$41\% compared to the state-of-the-art alternatives.

As cloud storage systems increase in scale, hard drive failures are becoming more frequent, which raises reliability issues. In addition to traditional reactive fault tolerance, proactive fault tolerance is used to improve a system's reliability. However, there are few studies which analyze the reliability of proactive cloud storage systems, and they typically assume an exponential distribution for drive failures. This paper presents closed-form equations for estimating the number of data-loss events in proactive cloud storage systems using RAID-5, RAID-6, 2-way replication, and 3-way replication mechanisms, within a given time period. The equations model the impact of proactive fault tolerance, operational failures, failure restorations, latent block defects, and drive scrubbing on the systems reliability, and use time-based Weibull distributions to represent processes (instead of homogeneous Poisson processes). We also design a Monte-Carlo simulation method to simulate the running of proactive cloud storage systems. The proposed equations closely match time-consuming Monte-Carlo simulations, using parameters obtained from the analysis of field data. These equations allow designers to efficiently estimate system reliability under varying parameters, facilitating cloud storage system design.

Persistent memory's (PM) byte-addressability and high capacity will also make it emerging for virtualized environment. Modern virtual machine monitors virtualize PM using either I/O virtualization or memory virtualization. However, I/O virtualization will sacrifice PM's byte-addressability, and memory virtualization does not get the chance of PM image management. In this paper, we enhance QEMU's memory virtualization mechanism. The enhanced system can achieve both PM's byte-addressability inside virtual machines and PM image management outside the virtual machines. We also design pcow, a virtual machine image format for PM, which is compatible with our enhanced memory virtualization and supports storage virtualization features including thin-provision, base image and snapshot. Address translation is performed with the help of Extended Page Table (EPT), thus much faster than image formats implemented in I/O virtualization. We also optimize pcow considering PM's characteristics. The evaluation demonstrates that our scheme boosts the overall performance by up to 50x compared with qcow2, an image format implemented in I/O virtualization, and brings almost no performance overhead compared with the native memory virtualization.

Intel's Optane SSD recently came to the market as the pioneer of 3D XPoint based commercial devices. They have much lower latency (about 14 μs) and better parallelism properties than traditional SSDs, and as such are set to replace NAND flash SSD in commercial settings. To best serve cloud and enterprise data centers' higher performing storage demands, it is necessary to know the performance characteristics of the new devices in both virtualized cloud environments and traditional non-virtualized environments. In this paper, we present an analysis of Optane SSDs based on a large number of experiments. We use several micro-benchmarks to gain knowledge of Optane's basic performance metrics. We also discuss the impact of state-of-the-art storage stacks on the performance of Optane SSDs. By analyzing the test results, we provide configuration suggestions for storage I/O applications using Optane SSDs. Lastly, we evaluate the real-world performance of Optane SDDs by running MySQL database based experiments. All the experiments are performed in non-virtualized and virtualized environments (Linux and QEMU) with a comparison study between the Optane SSD and a SATA NAND flash-based SSD.

In cloud storage systems, the use of erasure coding results in high read latency and long recovery time when drive or node failure happens. In this paper, we design a parity independent array codes (PIT), a variation of STAR code, which is triple fault tolerant and nearly space-optimal, and also propose an efficient single-failure recovery scheme (PITR) for them to mitigate the problem. In addition, we present a "shortened" version of PIT (SPIT) to further reduce the recovery cost. In this way, less disk I/O and network resources are used, thereby reducing the recovery time and achieving a high system reliability and availability.

Common distributed storage systems use data replication to improve system reliability and maintain data availability, but at the cost of disk storage. In order to lower storage costs, data may instead be stored according to erasure codes, but this results in greater network and disk traffic when data blocks are reconstructed following an erasure. These methods are also passive, i.e., they only reconstruct data after failures occur. In this paper, we present a proactive erasure coding scheme (ProCode). We monitor the health of disks via drive failure prediction and automatically adjust the replication factor of data blocks on at-risk disks to ensure data safety. In this way, we achieve fast recovery after disk failures without significantly increasing the storage overhead. ProCode is implemented as an extension to HDFS-RAID used by Facebook. Compared with replication storage and erasure coding, ProCode improves system reliability and availability. Specifically, experimental results show 2 or more orders of magnitude reduction in the average number of data loss events over a 10- year period, a 63% or greater drop in degraded read latency, and a 78% drop in recovery time.