The emerging decentralized storage systems (DSSs), such as InterPlanetary File System (IPFS), Storj, and Sia, provide people with a new storage model. Instead of being centrally managed, the data are sliced up and distributed across the nodes of the network. Furthermore, each data object is uniquely identified by a cryptographic hash (ObjectId) and can only be retrieved by ObjectId. Compared with the search functions provided by the existing centralized storage systems, the application scenarios of the DSSs are subject to certain restrictions. In this paper, we first apply decentralized B+Tree and HashMap to the DSSs to provide keyword search. Both indexes are kept in blocks. Since these blocks may be scattered on multiple nodes, we ensure that all operations involve as few blocks as possible to reduce network cost and response time. In addition, the version control and version merging algorithms are designed to effectively organize the indexes and facilitate data integration. The experimental results prove that our indexes have excellent availability and scalability.

The latest CPUs(computer cpu processors) employ multiple cores, massively superscalar pipelines, out-of-order execution of tons of instructions, and advanced SIMD capabilities, which can hide the memory access latency. And most of recent memory-oriented data structures have already benefit from these features. However, due to the complexity of data organization, these CPUs do not always work well in memory resident database systems (MMDBs), particularly regarding storing data in dynamic random-access memory (DRAM). This article studies memory-efficient data structures by analyzing the run time, access latency, cache misses, instructions per cycle (IPC), and DRAM reads (bytes). Then, we design and implement two data organization schemas in the main memory database: dispersing data block organization and clustering data block organization. Using algorithmic engineering and careful attention to internal parallelism, cache alignment can hide the memory access latency. However, we find that these data structures work well in some cases, though they have been eclipsed in the face of complex access paths. To determine the reasons, we study the impact of database techniques on memory access latency, such as data partitioning, storage models, and by processing algorithms. With the specific main memory database system, we estimate the performance of each data organization schema based on DRAM DDR4 and the latest Intel Haswell microarchitecture. In conclusion, this work will make DRAM access applicable in real-world situations by implementing the schema to systems, such as in-memory databases.

In distributed database systems, as cluster scales grow, efficiency and availability become critical considerations. In a cluster, a common approach to high availability is using replication, but this is inefficient due to its low storage utilization. Erasure coding can provide data reliability while ensuring high storage utilization. However, due to the large number of coding and decoding operations required by the CPU, it is not suitable for some frequently updated data. In order to optimize the storage efficiency of the data in the distributed system without affecting the availability of the data, this paper proposes a data temperature recognition algorithm that can distinguish data tablets and divides data tablets into three types, cold, warm, and hot, according to the frequency of access. Combining three replicas and erasure coding technology, ER-store is proposed, a hybrid storage mechanism for different data types. At the same time, we combined the read-write separation architecture of the distributed database system to design the data temperature conversion cycle, which reduces the computational overhead caused by frequent updates of erasure coding technology. We have implemented this design on the CBase database system based on the read-write separation architecture, and the experimental results show that it can save 14.6%–18.3% of the storage space while meeting the efficient access performance of the system.

Many distributed database systems that guarantee high concurrency and scalability adopt read-write separation architecture. Simultaneously, these systems need to store massive amounts of data daily, requiring different mechanisms for storing and accessing data, such as hot and cold data access strategies. Unlike distributed storage systems, the distributed database splits a table into sub-tables or shards, and the request frequency of each sub-table is not the same within a specific time. Therefore, it is not only necessary to design hot-to-cold approaches to reduce storage overhead, but also cold-to-hot methods to ensure high concurrency of those systems. We present a new redundant strategy named CBase-EC, using erasure codes to trade the performances of transaction processing and storage efficiency for CBase database systems developed for financial scenarios of the Bank. Two algorithms are proposed: the hot-cold tablets (shards) recognition algorithm and the hot-cold dynamic conversion algorithm. Then we adopt two optimization approaches to improve CBase-EC performance. In the experiment, we compare CBase-EC with three-replicas in CBase. The experimental results show that although the transaction processing performance declined by no more than 6%, the storage efficiency increased by 18.4%.

Edge computing, as an emerging computing paradigm, aims to reduce network bandwidth transmission overhead while storing and processing data on edge nodes. However, the storage strategies required for edge nodes are different from those for existing data centers. Erasure code (EC) strategies have been applied in some decentralized storage systems to ensure the privacy and security of data storage. Product-matrix (PM) regenerating codes (RGCs) as a state-of-the-art EC family are designed to minimize the repair bandwidth overhead or minimize the storage overhead. Nevertheless, the high complexity of the PM framework contains more finite-domain multiplication operations than classical ECs, which heavily consumes computational resources at the edge nodes. In this paper, a theoretical derivation of each step of the PM minimum storage regeneration (PM-MSR) and PM minimum bandwidth regeneration (PM-MBR) codes is performed and the XOR complexity over finite fields is analyzed. On this basis, a new construct called product bitmatrix (PB) is designed to reduce the complexity of XOR operations in the PM framework, and two heuristics are used to further reduce the XOR numbers of the PB-MSR and PB-MBR codes, respectively. The evaluation results show that the PB construction significantly reduces the XOR number compared to the PM-MSR, PM-MBR, Reed–Solomon (RS), and Cauchy RS codes while retaining optimal performance and reliability.