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学者姓名：陈星
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Abstract ：
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 等. "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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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. IEEE
Keyword ：
computation offloading computation offloading deep reinforcement learning deep reinforcement learning Mobile edge computing Mobile edge computing personalized federated learning personalized federated learning resource allocation resource allocation
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| GB/T 7714 | Chen, Z. , Xiong, B. , Chen, X. 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 : 1-16 . |
| MLA | Chen, Z. et al. "Joint Computation Offloading and Resource Allocation in Multi-edge Smart Communities with Personalized Federated Deep Reinforcement Learning" . | IEEE Transactions on Mobile Computing (2024) : 1-16 . |
| APA | Chen, Z. , Xiong, B. , Chen, X. , Min, G. , Li, J. . Joint Computation Offloading and Resource Allocation in Multi-edge Smart Communities with Personalized Federated Deep Reinforcement Learning . | IEEE Transactions on Mobile Computing , 2024 , 1-16 . |
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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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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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无人机(Unmanned Aerial Vehicles, UAVs)与多接入边缘计算(Multi-access Edge Computing, MEC)技术的结合突破了传统地面通信的局限性，已成为解决MEC中任务卸载问题的重要手段。由于单无人机可提供的计算资源和能量有限，为了应对日益扩大的网络规模，考虑了多无人机辅助MEC环境中的任务卸载问题。基于问题定义，任务卸载过程可以视为一个在平行链路上进行的、具有玩家特定延迟函数的Wardrop路由博弈，目的是得到均衡状态和最优状态下的卸载策略，并量化分析两者间的差距。由于均衡解难以计算，因此构造了一个新的势函数，将均衡问题转换成最小化势函数问题。同时使用Frank-Wolfe算法最终获得均衡和最优卸载策略。算法在每次迭代中将目标函数线性化，通过求解线性规划得到可行方向，进而沿此方向在可行域内作一维搜索。仿真实验表明，相比其他基准测试方法，基于平行链路Wardrop路由博弈的均衡卸载策略能够有效降低模型总成本，且与最优卸载策略下总成本的比值约为1。
Keyword ：
Frank-Wolfe算法 Frank-Wolfe算法 Wardrop路由博弈 Wardrop路由博弈 任务卸载 任务卸载 多接入边缘计算 多接入边缘计算 无人机 无人机
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| GB/T 7714 | 汪昕隆 , 林兵 , 陈星 . 多无人机辅助MEC环境中基于Wardrop路由博弈的计算卸载 [J]. | 计算机科学 , 2024 , 51 (03) : 309-316 . |
| MLA | 汪昕隆 et al. "多无人机辅助MEC环境中基于Wardrop路由博弈的计算卸载" . | 计算机科学 51 . 03 (2024) : 309-316 . |
| APA | 汪昕隆 , 林兵 , 陈星 . 多无人机辅助MEC环境中基于Wardrop路由博弈的计算卸载 . | 计算机科学 , 2024 , 51 (03) , 309-316 . |
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随着智能家居的普及，用户期望通过自然语言指令实现智能设备的控制，并希望获得个性化的智能家居服务。然而，现有的挑战包括智能设备的互操作性和对用户环境的全面理解。针对上述问题，提出一个支持设备端用户智能家居服务推荐个性化的框架。首先，构建智能家居的运行时知识图谱，用于反映特定智能家居中的上下文信息，并生成用例场景语句；其次，利用预先收集的通用场景下，用户的自然语言指令和对应的用例场景语句训练出通用推荐模型；最后，用户在设备端以自然语言管理智能家居设备和服务，并通过反馈微调通用模型的权重得到个人模型。在基本指令集、复述集、场景指令集三个数据集上的实验表明，用户的个人模型相比于词嵌入方法的准确率提升了6.5%～30%,与Sentence-BERT模型相比准确率提升了2.4%～25%,验证了设备端基于深度学习的智能家居服务框架具有较高的服务推荐准确率，能够有效地管理智能家居设备和服务。
Keyword ：
智能家居 智能家居 物联网 物联网 相似度计算 相似度计算 知识图谱 知识图谱 自然语言处理 自然语言处理
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| GB/T 7714 | 陈佳雯 , 黄志明 , 蔡泽卓 et al. 设备端基于深度学习的智能家居服务推荐框架 [J]. | 计算机应用研究 , 2024 , 41 (02) : 533-539 . |
| MLA | 陈佳雯 et al. "设备端基于深度学习的智能家居服务推荐框架" . | 计算机应用研究 41 . 02 (2024) : 533-539 . |
| APA | 陈佳雯 , 黄志明 , 蔡泽卓 , 陈星 . 设备端基于深度学习的智能家居服务推荐框架 . | 计算机应用研究 , 2024 , 41 (02) , 533-539 . |
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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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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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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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