ABSTRACT Cross‐chain interoperability is a focal point in blockchain research. However, existing efforts predominantly concentrate on functional realization, such as asset transfer and message relaying, while often overlooking the critical dimension of performance. The effective implementation of these functions faces significant performance challenges, including data verification overhead, traceability delays, and inflexible contract execution. Precisely addressing this gap, this paper proposes a performance‐optimized, relay‐based architecture. Three dedicated core modules are introduced to address these performance bottlenecks: A Shared Data Life‐cycle Management Module for efficient data governance, a Real‐time Cross‐chain Traceability Module for low‐latency tracking, and a Dynamic Smart Contract Management Module for agile cross‐chain logic execution. Implemented on BitXHub, our system demonstrates superior performance, successfully processing 937 out of 1000 transactions and achieving a latency of 6.7 ms under 800 concurrent requests. The framework's practical effectiveness is further validated through deployments in a cross‐border seafood supply chain and a multi‐party Deoxyribonucleic Acid (DNA) data sharing network, proving its value as a high‐performance solution for complex real‐world applications.

Efficient resource utilization is crucial in real-world applications, especially for balancing loads across machines handling specific job types. This paper introduces a novel batch-ordered job-store scheduling model, where jobs in a batch are scheduled sequentially, with their operations allocated in a round-robin fashion across two scenarios. We establish that this problem is NP-hard and analyze it in both online and offline settings. In the online case, we first examine the exclusive scenario, where operations within the same job must be scheduled on different machines, and show that a load greedy (LG) algorithm achieves a tight competitive ratio of$2 - \frac{1}{m}$, withmrepresenting the number of machines. Next, we consider the circular scenario, which requires maintaining the circular order of operations across ordered machines. In this context, we analyze potential anomalies in load distribution during local optimality achieved by the ordered load greedy (OLG) algorithm and provide bounds on the occurrence of these anomalies and the maximum load in each local scheduling round. In the offline case, we abstract each OLG scheduling process as a generalized circular sequence alignment (CSA) problem and develop a dynamic programming-based matching (DPM) algorithm to solve it. To further enhance load balancing, we develop a dynamic programming-based optimization (DPO) algorithm to schedule multiple jobs simultaneously in both scenarios. Experimental results confirm the efficiency of DPM for the CSA problem, and we validate the load balancing effectiveness of both online and offline algorithms using real traffic datasets. These theoretical findings and algorithmic implementations lay a solid groundwork for future practical advancements.

Traffic congestion at toll plazas is a critical issue in urban infrastructure, which is often exacerbated by surges in vehicle volume during peak hours. The congestion typically arises from imbalances in traffic demand and toll booth efficiency, often resulting in safety hazards and delays. Existing solutions, while addressing efficiency or safety aspects, often lack a comprehensive approach for efficient traffic management at toll plazas. To address this challenge, in this paper, we propose TollHelper, a framework designed to optimize vehicle scheduling and load balancing at toll plazas as well as improve safety. Our approach treats concurrently arriving vehicles as a single scheduling batch, guiding different vehicles from the same batch to different toll booths to enhance safety and reduce congestion. We address this scheduling constraint in both general and heterogeneous toll booth scenarios, introducing effective load balancing algorithms to minimize toll booth service loads and optimize user driving experiences. Based on empirical studies, we demonstrate that our methods achieve improvements in standard deviation compared to the baselines, ranging from 51.6% to 97.0% improvement in terms of load balancing effects.