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学者姓名：陈星
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Abstract ：
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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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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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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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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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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Workflow decision making is critical to performing many practical applications of scientific principles and data. Scheduling in edge-cloud environments can address the high complexity of workflow applications, while decreasing the data transmission delay between the cloud and end devices. However, due to the heterogeneous resources in edge-cloud environments and the complicated data dependencies between the tasks in a workflow, significant challenges for workflow scheduling remain, including the selection of an optimal tasks-servers solution from the possible numerous combinations. Existing studies are mainly done subject to rigorous conditions without fluctuations, ignoring the fact that workflow scheduling is typically present in uncertain environments. In this study, we focus on reducing the execution cost of multiple workflow applications mainly caused by data transmission and task computation, while satisfying the required deadline constraints. Triangular fuzzy numbers are employed to represent the computing performance of servers and transmission bandwidth in fuzzy edge-cloud environments. A cost-driven scheduling strategy for multiple Poisson-arrived workflow applications using partial critical paths is proposed. It firstly merges cut edges through preprocess to reduce the workflow scale, then uniformly schedules all tasks on each partial critical path to avoid data transmission between dependent tasks and reduce the data transmission cost. The experimental results show that our strategy can obtain the optimal feasible scheduling scheme and have better robustness and real-time performance with different deadline constraints, compared with other benchmark strategies. Note to Practitioners-Vehicle identification is one of the workflow decision making systems in transportation environments, whose core technology is Deep Neural Networks (DNN). Traffic cameras with limited process capacity periodically record the images of on-road vehicles, and usually fail to complete the applications within their deadlines. Workflow decision making is one of the key issues to performance DNNs in vehicle identification applications. The uncertain environments have a great impact on the system latency for such problems, which can easily lead to the misjudgement of the optimal scheduling. In addition, it is difficult to select an optimal layers-servers solution from the numerous combinations. Therefore, we can employ the scheduling strategy (i.e., SWPCP) to make intelligent and faster workflow decisions for vehicle identification applications, which can reduce the execution cost mainly caused by layer computation and data transmission between layers within their deadlines, even in uncertain edge-cloud environments. Complex DNN layers (tasks) in vehicle identification applications can be scheduled to the cloud for execution, while simple ones are processed on the edge. The cloud and edge platforms collaborate with each other and execute the DNN layers with low system cost and latency.
Keyword ：
cost-driven scheduling strategy cost-driven scheduling strategy deadline constraints deadline constraints Fuzzy edge-cloud environments Fuzzy edge-cloud environments workflow decision making systems workflow decision making systems
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| GB/T 7714 | Lin, Bing , Lin, Chaowei , Chen, Xing et al. Cost-Driven Scheduling for Workflow Decision Making Systems in Fuzzy Edge-Cloud Environments [J]. | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2024 , 22 : 3756-3771 . |
| MLA | Lin, Bing et al. "Cost-Driven Scheduling for Workflow Decision Making Systems in Fuzzy Edge-Cloud Environments" . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 22 (2024) : 3756-3771 . |
| APA | Lin, Bing , Lin, Chaowei , Chen, Xing , Lin, Mingwei , Huang, Gang , Xu, Zeshui . Cost-Driven Scheduling for Workflow Decision Making Systems in Fuzzy Edge-Cloud Environments . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2024 , 22 , 3756-3771 . |
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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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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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Through deploying computing resources at the network edge, Mobile Edge Computing (MEC) alleviates the contradiction between the high requirements of intelligent mobile applications and the limited capacities of mobile End Devices (EDs) in smart communities. However, existing solutions of computation offloading and resource allocation commonly rely on prior knowledge or centralized decision-making, which cannot adapt to dynamic MEC environments with changeable system states and personalized user demands, resulting in degraded Quality-of-Service (QoS) and excessive system overheads. To address this important challenge, we propose a novel Personalized Federated deep Reinforcement learning based computation Offloading and resource Allocation method (PFR-OA). This innovative PFR-OA considers the personalized demands in smart communities when generating proper policies of computation offloading and resource allocation. To relieve the negative impact of local updates on global model convergence, we design a new proximal term to improve the manner of only optimizing local Q-value loss functions in classic reinforcement learning. Moreover, we develop a new partial-greedy based participant selection mechanism to reduce the complexity of federated aggregation while endowing sufficient exploration. Using real-world system settings and testbed, extensive experiments demonstrate the effectiveness of the PFR-OA. Compared to benchmark methods, the PFR-OA achieves better trade-offs between delay and energy consumption and higher task execution success rates under different scenarios.
Keyword ：
computation offloading computation offloading deep reinforcement learning deep reinforcement learning Delays Delays Mobile edge computing Mobile edge computing personalized federated learning personalized federated learning Quality of service Quality of service resource allocation resource allocation Resource management Resource management Servers Servers Smart cities Smart cities Task analysis Task analysis Training Training
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| GB/T 7714 | Chen, Zheyi , Xiong, Bing , Chen, Xing et al. Joint Computation Offloading and Resource Allocation in Multi-Edge Smart Communities With Personalized Federated Deep Reinforcement Learning [J]. | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2024 , 23 (12) : 11604-11619 . |
| MLA | Chen, Zheyi et al. "Joint Computation Offloading and Resource Allocation in Multi-Edge Smart Communities With Personalized Federated Deep Reinforcement Learning" . | IEEE TRANSACTIONS ON MOBILE COMPUTING 23 . 12 (2024) : 11604-11619 . |
| APA | Chen, Zheyi , Xiong, Bing , Chen, Xing , Min, Geyong , Li, Jie . Joint Computation Offloading and Resource Allocation in Multi-Edge Smart Communities With Personalized Federated Deep Reinforcement Learning . | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2024 , 23 (12) , 11604-11619 . |
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Spatiotemporal Graph (STG) forecasting is an essential task within the realm of spatiotemporal data mining and urban computing. Over the past few years, Spatiotemporal Graph Neural Networks (STGNNs) have gained significant attention as promising solutions for STG forecasting. However, existing methods often overlook two issues: the dynamic spatial dependencies of urban networks and the heterogeneity of urban spatiotemporal data. In this paper, we propose a novel framework for STG learning called Dynamic Meta-Graph Convolutional Recurrent Network (DMetaGCRN), which effectively tackles both challenges. Specifically, we first build a meta graph generator to dynamically generate graph structures, which integrates various dynamic features, including input sensor signals and their historical trends, periodic information (timestamp embeddings), and meta-node embeddings. Among them, a memory network is used to guide the learning of meta-node embeddings. The meta-graph generation process enables the model to simulate the dynamic spatial dependencies of urban networks and capture data heterogeneity. Then, we design a Dynamic Meta-Graph Convolutional Recurrent Unit (DMetaGCRU) to simultaneously model spatial and temporal dependencies. Finally, we formulate the proposed DMetaGCRN in an encoder-decoder architecture built upon DMetaGCRU and meta-graph generator components. Extensive experiments on four real-world urban spatiotemporal datasets validate that the proposed DMetaGCRN framework outperforms state-of-the-art approaches.
Keyword ：
Dynamic graph generation Dynamic graph generation Heterogeneity Heterogeneity Meta-graph Meta-graph Spatiotemporal graph forecasting Spatiotemporal graph forecasting
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| GB/T 7714 | Guo, Xianwei , Yu, Zhiyong , Huang, Fangwan et al. Dynamic meta-graph convolutional recurrent network for heterogeneous spatiotemporal graph forecasting [J]. | NEURAL NETWORKS , 2024 , 181 . |
| MLA | Guo, Xianwei et al. "Dynamic meta-graph convolutional recurrent network for heterogeneous spatiotemporal graph forecasting" . | NEURAL NETWORKS 181 (2024) . |
| APA | Guo, Xianwei , Yu, Zhiyong , Huang, Fangwan , Chen, Xing , Yang, Dingqi , Wang, Jiangtao . Dynamic meta-graph convolutional recurrent network for heterogeneous spatiotemporal graph forecasting . | NEURAL NETWORKS , 2024 , 181 . |
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