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学者姓名：陈星
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Abstract ：
现今社会上数据的规模和种类变得越来越庞大和多样化,如何安全可信地共享异构数据资源成为了亟待解决的问题.为实现大数据的可信互联,提出基于Hyperledger Fabric的数据可信共享平台.首先,针对数据异源异构的问题,定义了数据架构的转换规则;然后,以数据提供方和数据需求方之间的数据共享全过程为导向,提出了数据可信追溯机制,保证了数据共享的真实性和完整性;此外,文中设计了一种数据处理即服务的数据共享框架,在确保数据可信的前提下,支撑数据调用、数据训练和数据匹配操作.通过对执行效率和智能合约性能进行验证分析,证明了本平台的有效性和实用性.
Keyword ：
Hyperledger Fabric Hyperledger Fabric 区块链 区块链 可信凭证 可信凭证 数据共享 数据共享 智能合约 智能合约
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| GB/T 7714 | 林哲旭 , 陈汉林 , 刘漳辉 et al. 基于Hyperledger Fabric的数据可信共享平台 [J]. | 小型微型计算机系统 , 2025 , 46 (1) : 189-199 . |
| MLA | 林哲旭 et al. "基于Hyperledger Fabric的数据可信共享平台" . | 小型微型计算机系统 46 . 1 (2025) : 189-199 . |
| APA | 林哲旭 , 陈汉林 , 刘漳辉 , 陈星 , 莫毓昌 . 基于Hyperledger Fabric的数据可信共享平台 . | 小型微型计算机系统 , 2025 , 46 (1) , 189-199 . |
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随着物联网和5G的不断发展,用户对流畅体验感的需求日益迫切,对于数据传输速率、响应延迟和服务质量的要求不断提高.边缘计算范式能够使服务器更加接近用户和设备,更快地响应数据请求,提高网络的效率和可扩展性,从而提供更好的用户体验.在此基础上了,本文提出了一种多边缘计算服务器协同提供计算服务的网络系统模型,并定义了服务部署和计算任务卸载联合优化问题.针对该问题,提出了一种基于蚁群优化算法(Ant Colony Optimization,ACO)的服务部署和计算任务卸载联合优化问题解决策略.实验结果表明,相较于基准策略,所提出的策略能够显著降低任务完成时延和能耗,并有效提高网络的效率和可扩展性.
Keyword ：
任务卸载 任务卸载 服务部署 服务部署 蚁群优化算法 蚁群优化算法 边缘计算 边缘计算
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| GB/T 7714 | 邓福康 , 许英豪 , 张建山 et al. ACO使能的边缘计算系统服务部署和计算任务卸载方法 [J]. | 小型微型计算机系统 , 2025 , 46 (2) : 314-320 . |
| MLA | 邓福康 et al. "ACO使能的边缘计算系统服务部署和计算任务卸载方法" . | 小型微型计算机系统 46 . 2 (2025) : 314-320 . |
| APA | 邓福康 , 许英豪 , 张建山 , 陈星 . ACO使能的边缘计算系统服务部署和计算任务卸载方法 . | 小型微型计算机系统 , 2025 , 46 (2) , 314-320 . |
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Mobile Edge Computing (MEC) can augment the capability of Internet of Things (IoT) mobile devices (MDs) through offloading the computation-intensive tasks to their adjacent servers. Synergistic computation offloading among MEC servers is one possible solution to reduce the completion time of system during peak hours. However, due to the large number of servers and the long distance between base stations (BSs), synchronizing the information of all servers takes a long time, which is not applicable to the fluctuant environments. Meanwhile, each server from different BSs is typically selfish and rational, and can only obtain the imperfect information from its adjacent servers, which is a challenge for computation offloading among servers from a global perspective. This article proposes a game-based computation offloading scheme with imperfect information in multi-edge environments. First, a non-cooperative game with imperfect information is designed to analyze the complex interactions during synergistic computation offloading among MEC servers. Second, a Synergistic Balancing Offloading Algorithm (SBOA) through distributed decision-making manner to obtain the optimal offloading decision is proposed, which guarantees that the game converges to a Nash Equilibrium (NE) point. Extensive simulation results reveal the fast convergence of SBOA. As the percentage of high-load servers rises and the number of heavy tasks increases, SBOA performs better than other benchmark algorithms in terms of timeliness, effectiveness, and system completion time.
Keyword ：
Cloud computing Cloud computing computation offloading computation offloading Decision making Decision making Delays Delays Games Games imperfect information imperfect information Internet of Things Internet of Things Internet of Things (IoT) Internet of Things (IoT) Load management Load management mobile edge computing (MEC) mobile edge computing (MEC) non-cooperative game non-cooperative game Performance evaluation Performance evaluation Servers Servers Simulation Simulation Technological innovation Technological innovation
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| GB/T 7714 | Lin, Bing , Weng, Jie , Chen, Xing et al. A Game-Based Computation Offloading With Imperfect Information in Multi-Edge Environments [J]. | IEEE TRANSACTIONS ON SERVICES COMPUTING , 2025 , 18 (1) : 1-14 . |
| MLA | Lin, Bing et al. "A Game-Based Computation Offloading With Imperfect Information in Multi-Edge Environments" . | IEEE TRANSACTIONS ON SERVICES COMPUTING 18 . 1 (2025) : 1-14 . |
| APA | Lin, Bing , Weng, Jie , Chen, Xing , Ma, Yun , Hsu, Ching-Hsien . A Game-Based Computation Offloading With Imperfect Information in Multi-Edge Environments . | IEEE TRANSACTIONS ON SERVICES COMPUTING , 2025 , 18 (1) , 1-14 . |
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The emerging load prediction techniques support up-front and rational resource provisioning in edge systems to enhance system efficiency and Quality-of-Service (QoS). Classic prediction methods may handle loads with apparent trends, but they cannot achieve accurate prediction for highly-variable edge loads. With the advantage of sequential data analysis, recurrent neural networks (RNNs) are often used for load prediction but reveal limited generalization ability and low training efficiency. Moreover, it is hard to obtain a well-performed prediction model by discrete single-edge training with insufficient historical data. To address these important challenges, we propose a novel Multi-edge Cooperative universal framework for load Prediction with Personalized Federated deep learning (MC-2PF), enabling multi-edge cooperative training of load prediction models. Specifically, to solve the client-drift issue in federated learning (FL) caused by distinct data distribution, we customize personalized models for each edge by independent control parameters and theoretically analyze the model convergence improvement. Meanwhile, we prove the generalization bound of the MC-2PF and its universality to RNN-based prediction models through a practical example. Using the real-world testbed and load datasets, extensive experiments verify the effectiveness and practicality of the MC-2PF for different RNN-based prediction models. Compared to state-of-the-art frameworks, the MC-2PF achieves higher prediction accuracy, faster convergence, and stronger adaptiveness.
Keyword ：
Accuracy Accuracy Adaptation models Adaptation models Cloud computing Cloud computing Computational modeling Computational modeling Data models Data models Load modeling Load modeling Load prediction Load prediction multi-edge cooperation multi-edge cooperation personalized federated learning personalized federated learning Predictive models Predictive models Quality of service Quality of service sequential data analysis sequential data analysis Servers Servers Training Training
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| GB/T 7714 | Chen, Zheyi , Jiang, Qingnan , Chen, Lixian et al. MC-2PF: A Multi-Edge Cooperative Universal Framework for Load Prediction With Personalized Federated Deep Learning [J]. | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2025 , 24 (6) : 5138-5154 . |
| MLA | Chen, Zheyi et al. "MC-2PF: A Multi-Edge Cooperative Universal Framework for Load Prediction With Personalized Federated Deep Learning" . | IEEE TRANSACTIONS ON MOBILE COMPUTING 24 . 6 (2025) : 5138-5154 . |
| APA | Chen, Zheyi , Jiang, Qingnan , Chen, Lixian , Chen, Xing , Li, Jie , Min, Geyong . MC-2PF: A Multi-Edge Cooperative Universal Framework for Load Prediction With Personalized Federated Deep Learning . | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2025 , 24 (6) , 5138-5154 . |
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GUI testing ensures the software quality and user experience in the ever-changing mobile application development. Using test scripts is one of the main GUI testing manner, but it might be obsolete when the GUI changes with the app's evolution. Current studies often rely on textual or visual similarity to perform test repair, but may be less effective when the interacted event sequence changes dramatically. In the interaction design, practitioners often provide multiple entry points to access the same function to gain higher openness and flexibility, which indicates that there may be multiple routes for reference in test repair. To evaluate the feasibility, we first conducted an exploratory study on 37 tests from 18 apps. The result showed that over 81% tests could be represented with alternative event paths, and using the extended paths could help enhance the test replay rate. Based on this finding, we propose a test-extension-based test repair algorithm named ExtRep. The method first uses test-extension to find alternative paths with similar test objectives based on feature coverage, and then finds repaired result with the help of sequence transduction probability proposed in NLP area. Experiments conducted on 40 popular applications demonstrate that ExtRep can achieve a success rate of 73.68% in repairing 97 tests, which significantly outperforms current approaches Water, Meter, and Guider. Moreover, the test-extension approach displays immense potential for optimizing test repairs. A tool that implements the ExtRep is available for practical use and future research.
Keyword ：
Android testing Android testing Black-box testing Black-box testing GUI functional testing GUI functional testing GUI test repair GUI test repair Test-extension Test-extension
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| GB/T 7714 | Long, Yonghao , Chen, Yuanyuan , Zeng, Chu et al. ExtRep: a GUI test repair method for mobile applications based on test-extension [J]. | AUTOMATED SOFTWARE ENGINEERING , 2025 , 32 (2) . |
| MLA | Long, Yonghao et al. "ExtRep: a GUI test repair method for mobile applications based on test-extension" . | AUTOMATED SOFTWARE ENGINEERING 32 . 2 (2025) . |
| APA | Long, Yonghao , Chen, Yuanyuan , Zeng, Chu , Chen, Xiangping , Chen, Xing , Zhou, Xiaocong et al. ExtRep: a GUI test repair method for mobile applications based on test-extension . | AUTOMATED SOFTWARE ENGINEERING , 2025 , 32 (2) . |
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Due to the complicated data dependencies between the tasks in a workflow application and the heterogeneous resources in edge-cloud environments, it is difficult to select an optimal tasks-servers solution for scheduling workflow applications in the complex environments. Current research on workflow applications scheduling is mainly concentrated on certain conditions, ignoring the fact that the scheduling environments usually fluctuate. In this article, we deal with reducing the execution cost of multiple workflow applications within the corresponding deadline constraints and improving the network robustness in fuzzy edge-cloud environments. Triangular Fuzzy Numbers (TFNs) are employed to describe the computing capacity of servers and the bandwidth between them in uncertain environments. Specially, a novel Scheduling Strategy based on Particle Swarm Optimization algorithm employing the Quadratic Penalty Function (SSPSO_QPF) is proposed for scheduling multiple workflow applications. Compared with other classic scheduling strategies, simulation results demonstrate that the proposed strategy can generate feasible scheduling schemes even with the strict deadline constraints, and significantly reduce the fuzzy execution cost of multiple workflow applications.
Keyword ：
Cloud computing Cloud computing Complex network robustness Complex network robustness Costs Costs Data communication Data communication Edge-cloud environments Edge-cloud environments Multi-constraint combinatorial optimization Multi-constraint combinatorial optimization Processor scheduling Processor scheduling Scheduling Scheduling Servers Servers Task analysis Task analysis Workflow scheduling Workflow scheduling
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| GB/T 7714 | Chen, Xing , Lin, Chaowei , Lin, Bing . An Intelligent Workflow Scheduling Scheme for Complex Network Robustness in Fuzzy Edge-Cloud Environments [J]. | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2024 , 11 (1) : 1106-1123 . |
| MLA | Chen, Xing et al. "An Intelligent Workflow Scheduling Scheme for Complex Network Robustness in Fuzzy Edge-Cloud Environments" . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 11 . 1 (2024) : 1106-1123 . |
| APA | Chen, Xing , Lin, Chaowei , Lin, Bing . An Intelligent Workflow Scheduling Scheme for Complex Network Robustness in Fuzzy Edge-Cloud Environments . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2024 , 11 (1) , 1106-1123 . |
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移动边缘计算(Mobile Edge Computing,MEC)是一种利用靠近移动设备的边缘节点提供的计算能力,来提升性能的前沿技术.现有的一些先进的计算卸载方法,已能够支持在MEC环境中基于函数粒度进行动态卸载.函数即服务(Function as a Service,FaaS)作为无服务架构的一种经典范式,提供了一种在函数粒度上构建和拓展应用程序的新方式.相比传统的方式,FaaS提供了理想的资源弹性.OpenFaaS作为当下流行的开源FaaS项目,为FaaS平台的搭建提供了良好的基础.将先进的计算卸载方法与FaaS解决方案(OpenFaaS)进行整合,是有意义且具有挑战的.为此,文中设计并实现了一个基于Open-FaaS的多边缘管理框架,该框架实现了对多个边缘上OpenFaaS的搭建与状态管理.同时,对于需要部署的函数,将其重构并部署到OpenFaaS上,在运行时能够灵活地在多个OpenFaaS间调度函数执行.针对5个实际的Java智能应用对该框架进行了评估,结果表明该框架可以有效管理多个边缘,且与本地运行相比,该框架平均可节省10.49％～49.36％的响应时间.
Keyword ：
OpenFaaS OpenFaaS 函数即服务(FaaS) 函数即服务(FaaS) 无服务架构 无服务架构 移动边缘计算 移动边缘计算 计算卸载 计算卸载
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| GB/T 7714 | 林璟峰 , 李鸣 , 陈星 et al. 基于OpenFaaS的多边缘管理框架 [J]. | 计算机科学 , 2024 , 51 (10) : 362-371 . |
| MLA | 林璟峰 et al. "基于OpenFaaS的多边缘管理框架" . | 计算机科学 51 . 10 (2024) : 362-371 . |
| APA | 林璟峰 , 李鸣 , 陈星 , 莫毓昌 . 基于OpenFaaS的多边缘管理框架 . | 计算机科学 , 2024 , 51 (10) , 362-371 . |
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在这篇文章中,针对分片区块链(Sharded Blockchain)系统性能优化问题,提出了一种结合粒子群和遗传算法的系统性能优化方法(PSO-GA),目的是为了在尽可能满足当前网络环境情况下,提升其系统吞吐量.该方法考虑分片区块链中节点的计算能力、恶意节点的概率以及节点之间的传输速率等不同网络环境下,找到响应网络状态的最佳分片区块链系统参数;为了避免传统粒子群优化算法陷入局部最优的问题,引入遗传算法中的交叉操作和变异操作,有效提高方法的准确性.通过大量仿真实验对方法的有效性进行验证分析.实验结果表明,相比于其他的方法,本文所提出的方法可以在更短的时间取得更高的系统吞吐量.
Keyword ：
分片区块链 分片区块链 可扩展性 可扩展性 粒子群算法 粒子群算法 遗传算法 遗传算法
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| GB/T 7714 | 蒋腾聪 , 张建山 , 郑鸿强 et al. 基于PSO-GA的分片区块链系统性能优化方法 [J]. | 小型微型计算机系统 , 2024 , 45 (7) : 1756-1762 . |
| MLA | 蒋腾聪 et al. "基于PSO-GA的分片区块链系统性能优化方法" . | 小型微型计算机系统 45 . 7 (2024) : 1756-1762 . |
| APA | 蒋腾聪 , 张建山 , 郑鸿强 , 陈星 . 基于PSO-GA的分片区块链系统性能优化方法 . | 小型微型计算机系统 , 2024 , 45 (7) , 1756-1762 . |
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As an effective technique to relieve the problem of resource constraints on mobile devices (MDs), the computation offloading utilizes powerful cloud and edge resources to process the computation-intensive tasks of mobile applications uploaded from MDs. In cloud-edge computing, the resources (e.g., cloud and edge servers) that can be accessed by mobile applications may change dynamically. Meanwhile, the parallel tasks in mobile applications may lead to the huge solution space of offloading decisions. Therefore, it is challenging to determine proper offloading plans in response to such high dynamics and complexity in cloud-edge environments. The existing studies often preset the priority of parallel tasks to simplify the solution space of offloading decisions, and thus the proper offloading plans cannot be found in many cases. To address this challenge, we propose a novel real-time and Dependency-aware task Offloading method with Deep Q-networks (DODQ) in cloud-edge computing. In DODQ, mobile applications are first modeled as Directed Acyclic Graphs (DAGs). Next, the Deep Q-Networks (DQN) is customized to train the decision-making model of task offloading, aiming to quickly complete the decision-making process and generate new offloading plans when the environments change, which considers the parallelism of tasks without presetting the task priority when scheduling tasks. Simulation results show that the DODQ can well adapt to different environments and efficiently make offloading decisions. Moreover, the DODQ outperforms the state-of-art methods and quickly reaches the optimal/near-optimal performance.
Keyword ：
Cloud computing Cloud computing Cloud-edge computing Cloud-edge computing Computational modeling Computational modeling deep reinforcement learning deep reinforcement learning dependent and parallel tasks dependent and parallel tasks Heuristic algorithms Heuristic algorithms Mobile applications Mobile applications real-time offloading real-time offloading Real-time systems Real-time systems Servers Servers Task analysis Task analysis
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| GB/T 7714 | Chen, Xing , Hu, Shengxi , Yu, Chujia et al. Real-Time Offloading for Dependent and Parallel Tasks in Cloud-Edge Environments Using Deep Reinforcement Learning [J]. | IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS , 2024 , 35 (3) : 391-404 . |
| MLA | Chen, Xing et al. "Real-Time Offloading for Dependent and Parallel Tasks in Cloud-Edge Environments Using Deep Reinforcement Learning" . | IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 35 . 3 (2024) : 391-404 . |
| APA | Chen, Xing , Hu, Shengxi , Yu, Chujia , Chen, Zheyi , Min, Geyong . Real-Time Offloading for Dependent and Parallel Tasks in Cloud-Edge Environments Using Deep Reinforcement Learning . | IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS , 2024 , 35 (3) , 391-404 . |
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Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms.
Keyword ：
Edge intelligence Edge intelligence federated graph learning federated graph learning neighbor generation neighbor generation semi-supervised learning semi-supervised learning
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| GB/T 7714 | Zhong, Luying , Pi, Yueyang , Chen, Zheyi et al. SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation [J]. | IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS , 2024 : 1141-1150 . |
| MLA | Zhong, Luying et al. "SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation" . | IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (2024) : 1141-1150 . |
| APA | Zhong, Luying , Pi, Yueyang , Chen, Zheyi , Yu, Zhengxin , Miao, Wang , Chen, Xing et al. SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation . | IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS , 2024 , 1141-1150 . |
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