To satisfy the enormous storage capacities required for big data, data centers have been adopting high-density shingled magnetic recording (SMR) disks. However, the weak fine-grained random write performance of SMR disks caused by their inherent write amplification and unbalanced read–write performance poses a severe challenge. Many studies have proposed solid-state drive (SSD) cache systems to address this issue. However, existing cache algorithms, such as the least recently used (LRU) algorithm, which is used to optimize cache popularity, and the MOST algorithm, which is used to optimize the write amplification factor, cannot exploit the full performance of the proposed cache systems because of their inappropriate optimization objectives. This article proposes a new SMR-aware cache framework called SAC+ to improve SMR-based hybrid storage. SAC+ integrates the two dominant types of SMR drives—namely, drive-managed and host-managed SMR drives—and provides a universal framework implementation. In addition, SAC+ integrally combines the drive characteristics to optimize I/O performance. The results of evaluations conducted using real-world traces indicate that SAC+ reduces the I/O time by 36–93% compared with state-of-the-art algorithms.

Big data applications have differentiated requirements for I/O resources in cloud environments. For instance, data analytic and AI/ML applications usually have periodical burst I/O traffic, and data stream processing and database applications often introduce fluctuating I/O loads based on a guaranteed I/O bandwidth. However, the existing resource isolation model (i.e., RLW) and methods (e.g., Token-bucket, mClock, and cgroup) cannot support the fluctuating I/O load and differentiated I/O demands well, and thus cannot achieve fairness, high resource utilization, and high performance for applications at the same time. In this paper, we propose a novel efficient and fair I/O resource isolation model and method called R2B, which can adapt to the differentiated I/O characteristics and requirements of different applications in a shared resource environment. R2B can simultaneously satisfy the fairness and achieve both high application efficiency and high bandwidth utilization.

Raw flash file systems are essential in today's cost-sensitive embedded systems and legacy embedded devices. The performance of raw flash file systems is often limited by their inefficient garbage collection (GC) due to the tight capacity of the onboard flash memory and CPU computing power. However, in real-world scenarios, we observed that the GC overhead can account for more than 85% of the total I/O time, which not only degrades the performance of the file system but also shortens the endurance of the flash chip. However, existing GC optimization used in high-end SSD or server-side file systems usually leads to high CPU and memory usage, which is not suitable for the embedded environment. In this paper, we propose LightGC, a low-complexity and high-efficiency GC scheme. We design an optimized clustering algorithm for page hotness measurement and a hot-delay GC victim selection strategy. LightGC can also be adapted to locality changes in workloads to maximize the effect of cold and heat separation. Our experimental results show that LightGC offers a relative improvement of up to 19% in GC efficiency, a 5%-15% reduction in write amplification, and a 10%-17% increase in flash memory endurance compared to other existing GC methods. In addition, driven by LightGC, we implement the UBIFS2 file system, an upgraded version of the UBIFS flash file system. We have done careful engineering design so that UBIFS2 and the original UBIFS are compatible with each other and users can switch between them freely.

To satisfy the huge storage capacity requirements of big data, the emerging high-density disks gradually adopt the Shingled Magnetic Recording (SMR) technique. However, the most serious challenge of SMR disks lies in their weak fine-grained random write performance caused by the write amplification inner SMRs and its extremely unbalanced read and write latencies. Although fast storage devices like Flash-based SSDscan be used to boost SMR disks in SMR-based hybrid storage, the optimization targets of existing cache algorithms (e.g., higher popularity for LRU, lower SMR write amplification ratio for MOST) are NOT the crucial factor for the performance of the SMR-based hybrid storage. In this paper, we propose a new SMR-Aware Co-design cache algorithm called SAC to accelerate the SMR-based hybrid storage. SAC adopts a hardware/software co-design method to fit the characteristics of SMR disks and to optimize the crucial factor, i.e., RMW operations inner SMR disks, effectively. Furthermore, SAC also makes a good balance between some conflicting factors, e.g., the data popularity vs. the SMR write amplification and clean cache space vs. dirty cache space. In our evaluations under real-world traces, SAC achieves a 7.5× performance speedup compared with LRU in the write-only mode, and a 2.9× speedup in the read-write mixed mode.

Hybrid storage devices have been widely deployed in cloud platforms to provide cost-efficient storage services. So the performance isolation on hybrid storage devices is important and necessary to ensure the quality-of-service (QoS) of cloud tenants. However, existing performance isolation techniques (e.g., the widely used cgroup) cannot provide accurate performance isolation on hybrid devices, usually leading to an unexpected throughput shrink than the user-specified value. In this paper, we propose a new Self-Adaptive Accurate Throttling (SAAT) method to achieve accurate performance isolation on hybrid storage devices. SAAT can give an accurate estimation of the throughput shift of existing solutions periodically according to the latest status of hybrid storage devices, and then adjust the assigned throughput of cgroup to achieve the accurate performance isolation. The experimental results exhibit that SAAT outperform the original cgroup method for 3.42 times on the difference of average throughput (DAT) between the target and the measured one, i.e., reducing DAT from 6.56% to 1.94%; in the meanwhile, SAAT reduces the Average Jitter of practical throughput from 6.62% to 5.25%.