Blockchain storage platforms reward storage nodes for keeping user-uploaded data for a certain amount of time. These storage nodes are unstable and can go online or offline unpredictably at any time, leading to potential data loss. To prevent data loss, blockchain storage platforms adopt erasure codes on user-uploaded encrypted data. Data repair processes will be performed to recover the lost data. However, the data repair processes heavily rely on time-consuming erasure coding algorithms, mainly consisting of vector-matrix multiplications. The emerging processing-in-memory technique can efficiently speed up the processing of vector-matrix multiplications. It can be integrated into blockchain storage platforms to solve the data repair issue. This paper presents Rapper, a parameter-aware repair-inmemory accelerator for blockchain storage platforms. Rapper utilizes the computing power of emerging processing-in-memory architecture so that data repair processes can be processed in a parallel manner and the overall efficiency can be improved significantly. Specifically, at the hardware level, the ReRAM memory is reorganized into our proposed double bank, XRU, XGroup, and ReRAM crossbars structure. At the software level, a parallel decoding/encoding strategy is proposed to fully exploit the internal parallelism of ReRAM. We also propose an adaptive parameter-aware mapping to handle various sizes of stripes. To demonstrate the viability of the proposed technique, a representative blockchain storage project Storj is adopted as the default storage infrastructure. Experimental results show that Rapper can achieve a 1.96 × speedup on average compared to the representative scheme.

Decentralized storage systems such as blockchain storage applications adopt the distributed storage technology and use distributed storage nodes to store the persistent data. For each off-chain storage node, key-value (KV) stores are normally used to manage data. As the most common data structure for KV store, Log Structured Merge Tree (LSM-Tree) eliminates random write operations and keeps acceptable read performance. Although LSM-Tree-based decentralized storage system can provide a secure and reliable storage platform, the unique feature of blockchain applications is not fully exploited. In blockchain storage applications, the generation of keys for KV stores is based on the encrypted data, and the key determines the allocation of data. Since the granularity for a read/write request at the level of blockchain storage platform is much smaller than that at the level of LSM-Tree or flash memory, a physical block in a solid-state drive (SSD) could be filled with data from different system users. This mixture of workloads will lead to the inefficient usage of physical spaces in the SSD and cause extra compaction operations for LSM-Tree. This paper presentsMeta-Block, a cross-layer and efficient storage management strategy for decentralized blockchain storage applications. Meta-Block utilizes rich functionalities provided by the system infrastructure of open-channel SSD to provide direct storage accesses for the off-chain storage node. The objective is to capture the features of blockchain storage applications and reduce unnecessary read and write operations across different storage layers. As a cross-layer design, Meta-Block redesigns the organization of LSM-Tree, which can effectively reduce the write amplification. We also design a data prefetching strategy to speed up the indexing and enable direct storage access. We demonstrate the viability of the proposed technique using a set of extensive experiments. Experimental results show that Meta-Block can effectively reduce the write amplification and extend the lifetime of SSDs in comparison with representative schemes.