In-memory databases (IMDBs) have been the backbone of modern systems that demand high throughput and low latency. Because of the cost and volatility of DRAM, IMDBs become incompetent when dealing with workloads that require large data volume and strict durability. The emergence of non-volatile memory (NVM) brings new opportunities for IMDBs to tackle this situation. However, it is non-trivial to build an NVM-based IMDB, due to performance degradation, NVM programming complexity, and other challenges. In this paper, we present Tair-PMem , an NVM-based enterprise-strength database atop Redis, the most popular IMDB. Tair-PMem adopts a well-controlled data layout and a log-as-user-data design to mitigate NVM overheads. It eases the NVM programming complexity by providing a hybrid memory programming toolkit. To better leverage the enterprise-strength features and implementations from Redis, Tair-PMem retrofits it in a less intrusive way to achieve full compatibility and stability, while retaining its advanced features. With all of the above techniques elaborately implemented, Tair-PMem satisfies full durability, high throughput, and low latency at the same time. Tair-PMem has now been publicly available as a cloud service on Alibaba Cloud. To the best of our knowledge, Tair-PMem is the first cloud service that makes good use of the persistence capability of NVM.

The past decade has witnessed the rapid development of cloud computing and data-centric applications. While these innovations offer numerous attractive features for data processing, they also bring in new issues about the loss of data ownership. Though some encrypted databases have emerged recently, they can not fully address these concerns for the data owner. In this paper, we propose an ownership-preserving database (OPDB), a new paradigm that characterizes different roles' responsibilities from nowadays applications and preserves data ownership throughout the entire application. We build Operon to follow the OPDB paradigm, which utilizes the trusted execution environment (TEE) and introduces a behavior control list (BCL). Different from access controls that merely handle accessibility permissions, BCL further makes data operation behaviors under control. Besides, we make Operon practical for real-world applications, by extending database capabilities towards flexibility, functionality and ease of use. Operon is the first database framework with which the data owner exclusively controls its data across different roles' subsystems. We have successfully integrated Operon with different TEEs, i.e. , Intel SGX and an FPGA-based implementation, and various database services on Alibaba Cloud, i.e. , PolarDB and RDS PostgreSQL. The evaluation shows that Operon achieves 71% - 97% of the performance of plaintext databases under the TPC-C benchmark while preserving the data ownership.

The ever-increasing demand of storage capacity and system performance leads to the scaling requirement in redundant arrays of independent disks (RAID)-structured storage systems. Existing approaches mainly focus on minimizing data migration in offline scenario, but not consider the user accesses issued by applications. However, in online scenario, the scaling I/Os and user I/Os usually interfere with each other, and result in significant performance degradation. Thus, it is of big significance to develop an efficient online scaling scheme to mitigate the impact of I/O interference. In this paper, we propose popularity-based online scaling (POS), which exploits workload locality by scaling storage zones with high popularity in a higher priority. POS can efficiently alleviate the interference between scaling I/Os and user I/Os, and it is also general enough to scale various RAID systems like RAID-0, RAID-5, and RAID-6. It can also be readily deployed atop various conventional RAID scaling approaches to improve their performance. To demonstrate the effectiveness of POS, we implement various conventional RAID scaling approaches, and also implement POS on top of these approaches for comparison. Extensive simulations with real-world workloads show that POS can efficiently reduce the response time of user requests and scaling I/Os, and also improves the sequentiality of data accesses.

Disk failures are very common in modern storage systems due to the large number of inexpensive disks. As a result, it takes a long time to recover a failed disk due to its large capacity and limited I/O. To speed up the recovery process and maintain a high system reliability, we propose a hierarchical code architecture with erasure codes, OI-RAID, which consists of two layers of codes, outer layer code and inner layer code. Specifically, the outer layer code is deployed with disk grouping technique based on Balanced Incomplete Block Design (BIBD) or complete graph with skewed data layout to provide efficient parallel I/O of all disks for fast failure recovery, and the inner layer code is deployed within each group of disks to provide high reliability. As an example, we deploy RAID5 in both layers to achieve fault tolerance of at least three disk failures, which meets the requirement of data availability in practical systems, as well as much higher speed up ratio for disk failure recovery than existing approaches. Besides, OI-RAID also keeps the optimal data update complexity and incurs low storage overhead in practice.

Parity declustering is widely deployed in erasure coded storage systems so as to provide fast recovery and high data availability. However, to perform scaling on such RAIDs, it is necessary to preserve the parity declustered data layout so as to guarantee the RAID performance after scaling. Unfortunately, existing scaling algorithms fail to achieve this goal so they can not be applied for scaling RAIDs which have deployed parity declustering. To address this challenge, we develop an efficient scaling algorithm called PDS (Parity Declustering Scaling). In particular, we first employ an auxiliary Balanced Incomplete Block Design (BIBD) to define the data migrations during scaling so as to preserve parity declustered data layout, and then define the addressing algorithm in the scaled system based on the migrations. We provide theoretical proofs to show that PDS preserves the parity declustered data layout, which is the basis for scaling RAIDs with parity declustering, and also theoretically prove that PDS achieves the even distribution of data/parity blocks after scaling and requires only the minimal data migrations. To show the performance of PDS, we implement it in MD in Linux Kernel, and conduct experiments with real-world traces. Results show PDS can reduce 89.70% of data migration time and 24.44% of user response time during scaling on average, compared with the round-robin scheme.

It is inevitable to scale RAID systems with the increasing demand of storage capacity and I/O throughput. When scaling RAID systems, we will always need to update parity to maintain the reliability of the storage systems. There are two schemes, read-modify-write (RMW) and read-reconstruct-write (RCW), to update parity. However most existing scaling approaches simply use RMW to update parity. While in many scenarios for existing scaling approaches, RCW performs better in terms of the number of scaling I/Os. In this paper, we propose an algorithm, called EPU, to analyze which of RCW and RMW is better for a scaling scenario and select the more efficient one to save the scaling I/Os. We apply EPU to online scaling scenarios and further use two optimizations, I/O overlap and access aggregation, to enhance the online scaling performance. Using Scale-CRS, one of the existing scaling approaches, as an example, we show via numerical studies that Scale-CRS+EPU reduces the amount of scaling I/Os over the traditional Scale-CRS in many scaling cases. To justify the online efficiency of EPU, we implement both Scale-CRS+EPU and Scale-CRS in a simulator with Disksim as a working module. Through extensive experiments, we show that Scale-CRS+EPU reduces the scaling time and the online response time of user requests over Scale-CRS.