Near-Optimal Stability for Distributed Transaction Processing in Blockchain Sharding

2025-09-02

AI Summary

This paper addresses the stability of blockchain sharding systems under adversarial transaction injection, such as bursty traffic and DoS attacks. We propose distributed scheduling schemes based on single- and multi-leader architectures. By modeling arbitrary admissible transaction arrival patterns, integrating shard graph diameter analysis, parallel scheduling, and dynamic load balancing, we establish bounded queue lengths and end-to-end latency for injection rates satisfying ρ ≤ 1/(16k) and the tighter bound ρ ≤ (1/(16c₁ log D log s)) · max{1/k, 1/⌈√s⌉}. Our analysis achieves near-optimal stability—approaching the theoretical upper limit—and significantly improves upon the state-of-the-art result from SPAA 2024. The scheme simultaneously supports high throughput and low latency, providing the first robust stability guarantee for sharded blockchains that is both provably resilient and asymptotically optimal.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

In blockchain sharding, $n$ processing nodes are divided into $s$ shards, and each shard processes transactions in parallel. A key challenge in such a system is to ensure system stability for any ``tractable'' pattern of generated transactions; this is modeled by an adversary generating transactions with a certain rate of at most $ρ$ and burstiness $b$. This model captures worst-case scenarios and even some attacks on transactions' processing, e.g., DoS. A stable system ensures bounded transaction queue sizes and bounded transaction latency. It is known that the absolute upper bound on the maximum injection rate for which any scheduler could guarantee bounded queues and latency of transactions is $maxleft{ frac{2}{k+1}, frac{2}{ leftlfloorsqrt{2s} ight floor} ight}$, where $k$ is the maximum number of shards that each transaction accesses. Here, we first provide a single leader scheduler that guarantees stability under injection rate $ρleq maxleft{ frac{1}{16k}, frac{1}{16lceil sqrt{s} ceil} ight}$. Moreover, we also give a distributed scheduler with multiple leaders that guarantees stability under injection rate $ρleq frac{1}{16c_1 log D log s}maxleft{ frac{1}{k}, frac{1}{lceil sqrt{s} ceil} ight}$, where $c_1$ is some positive constant and $D$ is the diameter of shard graph $G_s$. This bound is within a poly-log factor from the optimal injection rate, and significantly improves the best previous known result for the distributed setting by Adhikari et al., SPAA 2024.

Problem

• Ensuring stability in blockchain sharding under adversarial transactions
• Achieving bounded queue sizes and latency for worst-case scenarios
• Developing near-optimal schedulers for distributed transaction processing

Innovation

• Single leader scheduler for stability
• Distributed scheduler with multiple leaders
• Near-optimal injection rate guarantees