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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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Mobile edge computing (MEC) offers low-latency and flexible computing services for mobile devices (MDs) in Industrial Internet of Things (IIoT). Edge servers (ESs) in general belong to different subjects and will focus on their own interests. They may be reluctant to provide computation resources to MDs without appropriate incentives. Meanwhile, there is a trust issue in trading computation resources between ESs and MDs. Due to the complex interaction between ESs and MDs, it is a challenge for ESs to gain satisfactory revenue through reasonable resource pricing strategies, and for MDs to improve their Quality of Experience (QoE) through efficient computation offloading strategies. This article proposes a Stackelberg game-based computation offloading and resource pricing scheme (SGCS) in blockchain-enable MEC for IIoT. First, a blockchain-based resource trading framework is designed to enable trusted resource transactions. Second, a multileader multifollower Stackelberg game is presented to analyze the complex interactions in the multi-ES and multi-MD environments. Finally, the iterative proximal algorithm (IPA) for MDs' offloading decision and the subgradient-based iterative pricing algorithm (SIPA) for ESs' pricing decision are proposed, respectively, which guarantees that the game converges to a Stackelberg equilibrium (SE). Compared with multiagent deep deterministic policy gradient, genetic algorithm and PSO-GA (i.e., benchmark strategies), the average disutility of MDs with our proposed scheme is reduced by 6.95%-13.07%, 3.09%-20.41%, and 2.36%-17.22%, respectively. Moreover, with the increase of the number of MDs, our proposed scheme has better robustness, which can effectively deal with large-scale scenarios.
Keyword ：
Computation offloading Computation offloading Industrial Internet of Things (IIoT) Industrial Internet of Things (IIoT) mobile edge computing (MEC) mobile edge computing (MEC) Stackelberg game Stackelberg game
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| GB/T 7714 | Lin, Bing , Chen, Xuzhan , Chen, Xing et al. SGCS: An Intelligent Stackelberg-Game-Based Computation Offloading and Resource Pricing Scheme in Blockchain-Enabled MEC for IIoT [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (16) : 26727-26740 . |
| MLA | Lin, Bing et al. "SGCS: An Intelligent Stackelberg-Game-Based Computation Offloading and Resource Pricing Scheme in Blockchain-Enabled MEC for IIoT" . | IEEE INTERNET OF THINGS JOURNAL 11 . 16 (2024) : 26727-26740 . |
| APA | Lin, Bing , Chen, Xuzhan , Chen, Xing , Ma, Yun , Xiong, Neal N. . SGCS: An Intelligent Stackelberg-Game-Based Computation Offloading and Resource Pricing Scheme in Blockchain-Enabled MEC for IIoT . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (16) , 26727-26740 . |
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Vehicular Edge Computing (VEC) is a feasible solution for autonomous driving as it can offload latency-sensitive and computation-intensive tasks from vehicle terminals to roadside units (RSUs) for real-time processing. Due to the high computational resource the workflow applications (i.e., autonomous driving) required, the small vehicle-to-RSUs communication range, and the scarce available resources for each vehicle, it might be difficult to accomplish complex workflow applications through the single-hop offloading paradigm in the Internet of Vehicles (IoV). In this paper, we propose a reinforcement learning (RL) based multi-hop computation offloading scheme for workflow applications to reduce their execution latency in the VEC networks, which considers the data dependency in workflow applications as well as the trust relationship and communication interference among RSUs. Firstly, a preprocessing method is employed to merge the cut-edges in a workflow to reduce the offloading scale of tasks and compress the encoding dimension of offloading solutions. Then, a Deep Q-network algorithm using Phase-optimal State update (DQPS) is proposed to update the offloading policy distribution, in which the deviation of the phase-optimal state from the current state is estimated as the reward of RL to promote the algorithm's convergence to adapt the dynamic VEC networks. Simulation results show that DQPS has the best performance compared to other benchmark schemes. Moreover, the latency of the identical applications can be reduced by 2.5%-25.3% through our offloading scheme with a multi-hop model compared to that with the single-hop paradigm in IoV. © 1975-2011 IEEE.
Keyword ：
Artificial intelligence (AI) Artificial intelligence (AI) Deep reinforcement learning Deep reinforcement learning Multi-hop offloading Multi-hop offloading Vehicle Edge Computing (VEC) Vehicle Edge Computing (VEC) Workflow application Workflow application
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| GB/T 7714 | Lin, B. , Chen, Q. , Chen, X. et al. DQPS: An Intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge Computing Networks [J]. | IEEE Transactions on Consumer Electronics , 2024 . |
| MLA | Lin, B. et al. "DQPS: An Intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge Computing Networks" . | IEEE Transactions on Consumer Electronics (2024) . |
| APA | Lin, B. , Chen, Q. , Chen, X. , Jia, W.-K. , Lu, Y. , Xiong, N.N. . DQPS: An Intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge Computing Networks . | IEEE Transactions on Consumer Electronics , 2024 . |
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Mobile edge computing (MEC) offers a promising technology that deploys computing resources closer to mobile devices for improving performance. Most of the existing studies support on-demand remote execution of the computing tasks in applications through program transformation, but they commonly assume that mobile devices merely resort to a single server for computation offloading, which cannot make full use of the scattered and changeable computing resources. Thus, for object-oriented applications, we propose a novel approach, called FUNOff, to support the dynamic offloading of applications in MEC at the function granularity. First, we extract a call tree via code analysis and locate the function invocations that are suitable for offloading. Next, we refactor the code of related object functions according to a specific program structure. Finally, we make offloading decisions referring to the context at runtime and send function invocations to multiple remote servers for execution. We evaluate the proposed FUNOff on two real-world applications. The results show that, compared with other approaches, FUNOff better supports the computation offloading of object-oriented applications in MEC, which reduces the response time by 10.7%-58.2%.
Keyword ：
code analysis code analysis computation offloading computation offloading Mobile edge computing Mobile edge computing object-oriented application object-oriented application software adaptation software adaptation
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| GB/T 7714 | Chen, Xing , Li, Ming , Zhong, Hao et al. FUNOff: Offloading Applications at Function Granularity for Mobile Edge Computing [J]. | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2024 , 23 (2) : 1717-1734 . |
| MLA | Chen, Xing et al. "FUNOff: Offloading Applications at Function Granularity for Mobile Edge Computing" . | IEEE TRANSACTIONS ON MOBILE COMPUTING 23 . 2 (2024) : 1717-1734 . |
| APA | Chen, Xing , Li, Ming , Zhong, Hao , Chen, Xiaona , Ma, Yun , Hsu, Ching-Hsien . FUNOff: Offloading Applications at Function Granularity for Mobile Edge Computing . | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2024 , 23 (2) , 1717-1734 . |
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随着无线网络中的移动数据流量爆炸式增长，支持高速缓存的无人机被应用于移动计算领域充当边缘服务器，为网络中的用户提供按需服务。为了在满足其他资源约束的条件下，给用户带来更好的体验，通过联合优化无人机部署、缓存放置和用户关联以实现最小化所有用户的内容访问时延，并为用户提供质量不同的内容缓存服务。针对多无人机和地面基站协同提供缓存服务的场景，提出了一种基于迭代优化的联合优化算法。该算法通过迭代求解由目标问题分解得到的三个子问题的方式来获得具有收敛性保证的次优解决方案。首先，采用基于连续凸近似的算法求解无人机部署子问题；其次，采用基于贪心的算法求解内容缓存子问题；然后，利用基于罚函数的连续凸近似算法求解用户关联子问题；最后，对上述过程重复迭代，得到目标问题的一个次优解。多次仿真实验验证了所提算法的有效性和可行性。仿真结果表明，与基准算法相比，所提联合优化算法在平均内容访问时延、缓存命中率两方面均具有更好的性能。
Keyword ：
内容缓存 内容缓存 凸优化 凸优化 无人机三维部署 无人机三维部署 用户关联 用户关联 移动边缘计算 移动边缘计算
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| GB/T 7714 | 唐焕博 , 陈星 , 张建山 . 移动边缘计算中的无人机三维部署和内容缓存优化方法 [J]. | 计算机应用研究 , 2024 , 41 (04) : 1143-1149 . |
| MLA | 唐焕博 et al. "移动边缘计算中的无人机三维部署和内容缓存优化方法" . | 计算机应用研究 41 . 04 (2024) : 1143-1149 . |
| APA | 唐焕博 , 陈星 , 张建山 . 移动边缘计算中的无人机三维部署和内容缓存优化方法 . | 计算机应用研究 , 2024 , 41 (04) , 1143-1149 . |
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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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Abstract ：
移动边缘计算(Mobile Edge Computing, MEC)将计算与存储资源部署到网络边缘，用户可将移动设备上的任务卸载到附近的边缘服务器，得到一种低延迟、高可靠的服务体验.然而，由于动态的系统状态和多变的用户需求，MEC环境下的计算卸载与资源分配面临着巨大的挑战.现有解决方案通常依赖于系统先验知识，无法适应多约束条件下动态的MEC环境，导致了过度的时延与能耗.为解决上述重要挑战，本文提出了一种新型的基于深度强化学习的计算卸载与资源分配联合优化方法(Joint computation Offloading and resource Allocation with deep Reinforcement Learning, JOA-RL).针对多用户时序任务，JOA-RL方法能够根据计算资源与网络状况，生成合适的计算卸载与资源分配方案，提高执行任务成功率并降低执行任务的时延与能耗.同时，JOA-RL方法融入了任务优先级预处理机制，能够根据任务数据量与移动设备性能为任务分配优先级.大量仿真实验验证了JOA-RL方法的可行性和有效性.与其他基准方法相比，JOA-RL方法在任务最大容忍时延与设备电量约束下能够在时延与能耗之间取得更好的平衡，且展现出了更高的任务执行成功率.
Keyword ：
多约束优化 多约束优化 深度强化学习 深度强化学习 移动边缘计算 移动边缘计算 计算卸载 计算卸载 资源分配 资源分配
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| GB/T 7714 | 熊兵 , 张俊杰 , 黄思进 et al. 多约束边环境下计算卸载与资源分配联合优化 [J]. | 小型微型计算机系统 , 2024 , 45 (02) : 405-412 . |
| MLA | 熊兵 et al. "多约束边环境下计算卸载与资源分配联合优化" . | 小型微型计算机系统 45 . 02 (2024) : 405-412 . |
| APA | 熊兵 , 张俊杰 , 黄思进 , 陈哲毅 , 于正欣 , 陈星 . 多约束边环境下计算卸载与资源分配联合优化 . | 小型微型计算机系统 , 2024 , 45 (02) , 405-412 . |
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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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为了更好地支持边缘计算服务提供商进行资源的提前配置与合理分配，负载预测被认为是边缘计算中的一项重要的技术支撑.传统的负载预测方法在面对具有明显趋势或规律性的负载时能取得良好的预测效果，但是它们无法有效地对边缘环境中高度变化的负载取得精确的预测.此外，这些方法通常将预测模型拟合到独立的时间序列上，进而进行单点负载实值预测.但是在实际边缘计算场景中，得到未来负载变化的概率分布情况会比直接预测未来负载的实值更具应用价值.为了解决上述问题，本文提出了一种基于深度自回归循环神经网络的边缘负载预测方法(Edge Load Prediction with Deep Auto-regressive Recurrent networks, ELP-DAR).所提出的ELP-DAR方法利用边缘负载时序数据训练深度自回归循环神经网络，将LSTM集成至S2S框架中，进而直接预测下一时间点负载概率分布的所有参数.因此，ELP-DAR方法能够高效地提取边缘负载的重要表征，学习复杂的边缘负载模式进而实现对高度变化的边缘负载精确的概率分布预测.基于真实的边缘负载数据集，通过大量仿真实验对所提出ELP-DAR方法的有效性进行了验证与分析.实验结果表明，相比于其他基准方法，所提出的ELP-DAR方法可以取得更高的预测精度，并且在不同预测长度下均展现出了优越的性能表现.
Keyword ：
循环神经网络 循环神经网络 概率分布 概率分布 深度自回归 深度自回归 负载预测 负载预测 边缘计算 边缘计算
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| GB/T 7714 | 陈礼贤 , 梁杰 , 黄一帆 et al. 基于深度自回归循环神经网络的边缘负载预测 [J]. | 小型微型计算机系统 , 2024 , 45 (02) : 359-366 . |
| MLA | 陈礼贤 et al. "基于深度自回归循环神经网络的边缘负载预测" . | 小型微型计算机系统 45 . 02 (2024) : 359-366 . |
| APA | 陈礼贤 , 梁杰 , 黄一帆 , 陈哲毅 , 于正欣 , 陈星 . 基于深度自回归循环神经网络的边缘负载预测 . | 小型微型计算机系统 , 2024 , 45 (02) , 359-366 . |
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针对不确定性云边协同环境下工作流应用调度问题，考虑服务器的负载压力、网络拥塞等计算环境因素造成计算性能和传输带宽的不稳定性，采用三角模糊数表示模糊云边协同环境中服务器的计算性能和传输带宽.对于泊松到达的多工作流应用，提出一种基于局部关键路径的多工作流应用调度策略，将局部关键路径作为调度单元进行统一调度，充分避免任务之间的数据传输，旨在满足多工作流应用截止日期约束的前提下，降低其模糊执行代价.仿真结果表明，与其他基准策略相比，在不同的截止时间约束下，该策略都能获得多工作流应用最优的可行调度方案，同时实现了模糊执行代价的有效优化.
Keyword ：
云边协同计算 云边协同计算 局部关键路径 局部关键路径 工作流应用调度 工作流应用调度 模糊不确定性 模糊不确定性
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| GB/T 7714 | 林潮伟 , 林兵 , 陈星 . 云边协同环境下基于局部关键路径的工作流应用调度策略 [J]. | 小型微型计算机系统 , 2024 , 45 (02) : 335-344 . |
| MLA | 林潮伟 et al. "云边协同环境下基于局部关键路径的工作流应用调度策略" . | 小型微型计算机系统 45 . 02 (2024) : 335-344 . |
| APA | 林潮伟 , 林兵 , 陈星 . 云边协同环境下基于局部关键路径的工作流应用调度策略 . | 小型微型计算机系统 , 2024 , 45 (02) , 335-344 . |
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