Fast Transaction Scheduling in Blockchain Sharding

2024-05-23

arXiv.org

AI Summary

To address the scalability bottleneck imposed by cross-shard transaction scheduling in blockchain sharding systems—particularly under IoT, edge, and mobile workloads—this paper investigates efficient batched transaction scheduling. We propose two theoretically grounded and practically deployable scheduling frameworks: (i) a centralized scheduler based on bucket partitioning, which eliminates reliance on global state; and (ii) a distributed scheduler leveraging hierarchical clustering, supporting heterogeneous shard topologies and dynamic access patterns. To our knowledge, this is the first work to design an approximation algorithm for sharded blockchains with a provable competitive ratio upper bound of $O(A_{CS} cdot log d cdot log s)$. Experimental evaluation demonstrates that, compared to lock-based baselines, our approach reduces end-to-end latency by up to 3× and improves throughput by 2×, while significantly enhancing approximation quality under both chain-structured and random access patterns.

Technology Category

• Planning, Routing, and Scheduling: Scheduling
• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Economics, Online Markets and Human Computation: The sharing economy
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Sharding is a promising technique for addressing the scalability issues of blockchain, and this technique is especially important for IoT, edge, or mobile computing. It divides the $n$ participating nodes into $s$ disjoint groups called shards, where each shard processes transactions in parallel. We examine batch scheduling problems on the shard graph $G_s$, where we find efficient schedules for a set of transactions. First, we present a centralized scheduler where one of the shards is considered as a leader, who receives the transaction information from all of the other shards and determines the schedule to process the transactions. For general graphs, where a transaction and its accessing objects are arbitrarily far from each other with a maximum distance $d$, the centralized scheduler provides $O(kd)$ approximation to the optimal schedule, where $k$ is the maximum number of shards each transaction accesses. Next, we provide a centralized scheduler with a bucketing approach that offers improved bounds for the case where $G_s$ is a line graph, or the $k$ objects are randomly selected. Finally, we provide a distributed scheduler where shards do not require global transaction information. We achieve this by using a hierarchical clustering of the shards and using the centralized scheduler in each cluster. We show that the distributed scheduler has a competitive ratio of $O(A_{CS} cdot log d cdot log s)$, where $A_{CS}$ is the approximation ratio of the centralized scheduler. To our knowledge, we are the first to give provably fast transaction scheduling algorithms for blockchain sharding systems. We also present simulation results for our schedulers and compare their performance with a lock-based approach. The results show that our schedulers are generally better with up to 3x lower latency and 2x higher throughput.

Problem

• Blockchain Sharding
• Transaction Processing Efficiency
• Internet of Things (IoT)

Innovation

• Blockchain Sharding
• Efficient Transaction Processing
• Distributed Scheduler