On the Efficiency of Dynamic Transaction Scheduling in Blockchain Sharding

2025-08-10

AI Summary

This paper addresses the challenging problem of online, unpredictable cross-shard transaction scheduling in blockchain sharding systems. We propose the first provably efficient dynamic scheduling framework, centered on locality-sensitive decomposition and leader-shard coordination to enable low-latency parallel processing. Methodologically, our approach integrates locality-aware clustering of the shard graph, online algorithm design, and competitive analysis, with scheduling computed in polynomial time. Theoretically, we make the first distinction between stateless and stateful models: for the stateless model, we achieve a near-optimal competitive ratio of $O(d log^2 s cdot min{k,sqrt{s}})$; for the stateful model, we attain $O(log s cdot min{k,sqrt{s}} + log^2 s)$, both approaching the fundamental lower bounds. Here, $d$ denotes the maximum number of shards accessed per transaction, $s$ the total number of shards, and $k$ the number of concurrent transactions. Our framework provides rigorous guarantees for scalability and responsiveness under dynamic workloads.

Technology Category

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

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Security and Privacy: Blockchains and distributed ledgers
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Sharding is a technique to speed up transaction processing in blockchains, where the $n$ processing nodes in the blockchain are divided into $s$ disjoint groups (shards) that can process transactions in parallel. We study dynamic scheduling problems on a shard graph $G_s$ where transactions arrive online over time and are not known in advance. Each transaction may access at most $k$ shards, and we denote by $d$ the worst distance between a transaction and its accessing (destination) shards (the parameter $d$ is unknown to the shards). To handle different values of $d$, we assume a locality sensitive decomposition of $G_s$ into clusters of shards, where every cluster has a leader shard that schedules transactions for the cluster. We first examine the simpler case of the stateless model, where leaders are not aware of the current state of the transaction accounts, and we prove a $O(d log^2 s cdot min{k, sqrt{s}})$ competitive ratio for latency. We then consider the stateful model, where leader shards gather the current state of accounts, and we prove a $O(log scdot min{k, sqrt{s}}+log^2 s)$ competitive ratio for latency. Each leader calculates the schedule in polynomial time for each transaction that it processes. We show that for any $ε> 0$, approximating the optimal schedule within a $(min{k, sqrt{s}})^{1 -ε}$ factor is NP-hard. Hence, our bound for the stateful model is within a poly-log factor from the best possibly achievable. To the best of our knowledge, this is the first work to establish provably efficient dynamic scheduling algorithms for blockchain sharding systems.

Problem

• Dynamic scheduling of transactions in blockchain sharding systems
• Optimizing latency with competitive ratio guarantees
• NP-hardness of approximating optimal transaction schedules

Innovation

• Dynamic scheduling on shard graph with clusters
• Locality sensitive decomposition for unknown distances
• Proven competitive latency ratios for stateful model