Blockchain has now become a critical infrastructure in Metaverse for storing and managing the digital resources of users, bridging the real and virtual worlds. However, consensus protocols in blockchains constrain the performance of their applications. While existing voting-based consensus protocols such as HotStuff and other Byzantine Fault Tolerance (BFT) protocols have optimized efficiency and scalability, they simply adopt a one-person-one-vote rule that is not aligned with the human-centric values of most blockchain applications, including Metaverse. Therefore, we propose Stake Voting Protocol (SVP), a secure and scalable consensus protocol, whose design philosophy is to consider validators’ stakes in the BFT protocol and introduce flexibility through a sliding window. We also propose a certification rule within the pipelined two-chain consensus process to enhance security. Furthermore, our epoch change and incentive mechanisms ensure dynamics and liveness, respectively. Finally, our analytical and experimental results demonstrate that the proposed SVP satisfies correctness and can resist specific attacks with low latency and high throughput.

Log-Structured Merge Tree (LSM-Tree) is widely employed in key-value (kv) store owing to its exceptional performance in write-intensive workloads. LSMTree appends kv pairs to memory initially, and only when the memory capacity reaches a threshold does it flush the data to disk. However, in comparison to write, read operations often entail more disk access, resulting in relatively a slower response speed. Although LSM-Tree utilizes an index structure to expedite read operations, its performance enhancement remains limited. In addition, LSM-Tree fails to consider the read hotness of each sorted string table (SSTable) during compaction, leading to significant fluctuations in performance. This paper introduces HL-LSM, a novel LSM-Tree. HL-LSM utilizes learned index to replace original index to save system overhead and improve read performance. Moreover, a lightweight read hotness statistics is devised to optimize the compaction and assess the feasibility of each SSTable in constructing a learned index. In order to evaluate the performance of HL-LSM, we constructed a system prototype, HLDB, based on WiscKey. Experiment demonstrates that in read-intensive scenarios, HLDB is able to reduce the overall system latency by nearly 10% compared to WiscKey. Furthermore, it was observed that the optimization effect of HL-LSM improves as the variance of hotness value distribution increases.