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学者姓名：方娟
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Abstract ：
In cloud data centers, the exponential growth of data places increasing demands on computing, storage, and network resources, especially in multi-tenant environments. While this growth is crucial for ensuring Quality of Service (QoS), it also introduces challenges such as fluctuating resource requirements and static container configurations, which can lead to resource underutilization and high energy consumption. This article addresses online resource provisioning and efficient scheduling for multi-tenant environments, aiming to minimize energy consumption while balancing elasticity and QoS requirements. To address this, we propose a novel optimization framework that reformulates the resource provisioning problem into a more manageable form. By reducing the original multi-constraint optimization to a container placement problem, we apply the interior-point barrier method to simplify the optimization, integrating constraints directly into the objective function for efficient computation. We also introduce elasticity as a key parameter to balance energy consumption with autonomous resource scaling, ensuring that resource consolidation does not compromise system flexibility. The proposed Energy-Efficient and Elastic Resource Provisioning (EEP) framework comprises three main modules: a distributed resource management module that employs vertical partitioning and dynamic leader election for adaptive resource allocation; a prediction module using omega-step prediction for accurate resource demand forecasting; and an elastic scheduling module that dynamically adjusts to tenant scaling needs, optimizing resource allocation and minimizing energy consumption. Extensive experiments across diverse cloud scenarios demonstrate that the EEP framework significantly improves energy efficiency and resource utilization compared to established baselines, supporting sustainable cloud management practices.
Keyword ：
Data centers Data centers Quality of service Quality of service Ions Ions Data center network Data center network Containers Containers energy-efficient energy-efficient resource provisioning resource provisioning Dynamic scheduling Dynamic scheduling QoS QoS Energy consumption Energy consumption Servers Servers Resource management Resource management elastic scheduling elastic scheduling Cloud computing Cloud computing Elasticity Elasticity
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| GB/T 7714 | Lu, Shuaibing , Yan, Ran , Wu, Jie et al. Online Elastic Resource Provisioning With QoS Guarantee in Container-Based Cloud Computing [J]. | IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS , 2025 , 36 (3) : 361-376 . |
| MLA | Lu, Shuaibing et al. "Online Elastic Resource Provisioning With QoS Guarantee in Container-Based Cloud Computing" . | IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 36 . 3 (2025) : 361-376 . |
| APA | Lu, Shuaibing , Yan, Ran , Wu, Jie , Yang, Jackson , Deng, Xinyu , Wu, Shen et al. Online Elastic Resource Provisioning With QoS Guarantee in Container-Based Cloud Computing . | IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS , 2025 , 36 (3) , 361-376 . |
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Abstract ：
Mobile edge computing enhances cloud computing by providing low-latency and real-time response services at the network edge. However, the limited and heterogeneous resources of edge servers pose challenges for efficient service placement in multi-user scenarios. To address this issue, we propose a mobility-aware service placement strategy based on service priority (PriMSP), which leverages a priority mechanism based on service sensitivity to mitigate migration conflicts and minimize service response time. Specifically, we formulate an optimization model aimed at minimizing the service response time while considering user mobility and constraining service migration costs. To reduce frequent service migrations and conflicts caused by user mobility, we introduce multi-step trajectory prediction and a priority-based processing strategy to optimize service migration and mitigate migration conflicts, respectively. Furthermore, by integrating service priority and trajectory prediction, we propose a proactive service placement method based on deep reinforcement learning (DRL). We conduct experiments in a simulated environment using a real dataset, and the results demonstrate that our method effectively reduces the service latency of latency-sensitive applications while considering migration costs.
Keyword ：
service placement service placement Mobile edge computing Mobile edge computing reinforcement learning reinforcement learning service priority service priority
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| GB/T 7714 | Fang, Juan , Teng, Ziyi , Lu, Pengfan . PriMSP: Mobility-Aware Service Placement Strategy Based on Service Priority [J]. | 2025 10TH INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION SYSTEM, ICCCS , 2025 : 1019-1024 . |
| MLA | Fang, Juan et al. "PriMSP: Mobility-Aware Service Placement Strategy Based on Service Priority" . | 2025 10TH INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION SYSTEM, ICCCS (2025) : 1019-1024 . |
| APA | Fang, Juan , Teng, Ziyi , Lu, Pengfan . PriMSP: Mobility-Aware Service Placement Strategy Based on Service Priority . | 2025 10TH INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION SYSTEM, ICCCS , 2025 , 1019-1024 . |
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Abstract ：
With the rapid development of smart applications and mobile communication technologies, mobile edge computing (MEC) has made significant progress. However, the increase in the number of terminal devices and the growing complexity of service scenarios pose new challenges to efficient task processing. To address this challenge, this paper investigates the dynamic task offloading problem in the "cloud-edge-device" three-layer edge computing network architecture. To address this problem, we propose a distributed collaboration mechanism that supports device-level collaboration, device-edge collaboration, and edge-level collaboration, and design a Multi-Agent-based Task Offloading Algorithm (MATOA). In this algorithm, each agent interacts with each other through a deep reinforcement learning algorithm in a dynamic network environment to achieve continuous learning and parameter optimization. Simulation experiments show that compared with the baseline algorithm, this approach has good robustness in dynamic network environments, and can effectively reduce the overall execution latency of the task during the peak period of the task.
Keyword ：
Multi-agent Deep reinforcement learning Multi-agent Deep reinforcement learning Mobile edge computing Mobile edge computing Task offloading Task offloading Distributed Distributed
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| GB/T 7714 | Fang, Juan , Liu, Yaqi , Qu, Dezheng . A Distributed Task Offloading Strategy Based on Multi-Agent Systems in Mobile Edge Computing [J]. | 2025 10TH INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION SYSTEM, ICCCS , 2025 : 981-986 . |
| MLA | Fang, Juan et al. "A Distributed Task Offloading Strategy Based on Multi-Agent Systems in Mobile Edge Computing" . | 2025 10TH INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION SYSTEM, ICCCS (2025) : 981-986 . |
| APA | Fang, Juan , Liu, Yaqi , Qu, Dezheng . A Distributed Task Offloading Strategy Based on Multi-Agent Systems in Mobile Edge Computing . | 2025 10TH INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION SYSTEM, ICCCS , 2025 , 981-986 . |
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Abstract ：
In CPU-GPU heterogeneous systems, there exists intense resource contention between CPUs and GPUs. Traditional resource arbitration policies fail to account for the heterogeneity of cores, leading to inefficient network resource utilization for the CPU, which negatively impacts its performance. In heterogeneous networks, the degree of resource contention varies across different regions. This paper first uses reinforcement learning to analyze the message feature weights relied upon for resource arbitration in different network regions. To achieve more efficient resource allocation, a regional-contention-driven arbitration policy is proposed. The simulation results show that, compared to traditional arbitration policy, the overall network latency is reduced by 7.99%, and CPU performance is improved by 11.42%. Furthermore, a dynamic regional-contention-driven arbitration policy is proposed, which further reduces the overall network latency by 10.47% and increases CPU performance by 16.79% compared to traditional arbitration policy.
Keyword ：
Heterogenous architectures Heterogenous architectures Machine learning Machine learning Network-on-chip (NoC) Network-on-chip (NoC) Arbitration policy Arbitration policy
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| GB/T 7714 | Fang, Juan , Cheng, Haoyu , Wang, Yuening et al. DRCD: a regional-contention-driven arbitration policy for CPU-GPU heterogeneous systems [J]. | JOURNAL OF SUPERCOMPUTING , 2025 , 81 (3) . |
| MLA | Fang, Juan et al. "DRCD: a regional-contention-driven arbitration policy for CPU-GPU heterogeneous systems" . | JOURNAL OF SUPERCOMPUTING 81 . 3 (2025) . |
| APA | Fang, Juan , Cheng, Haoyu , Wang, Yuening , Zhai, Ran . DRCD: a regional-contention-driven arbitration policy for CPU-GPU heterogeneous systems . | JOURNAL OF SUPERCOMPUTING , 2025 , 81 (3) . |
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Abstract ：
In the rapidly evolving landscape of the Metaverse, the synergistic integration of the Internet of Things (IoT) and Digital Twins (DT) represents a revolutionary paradigm shift, seamlessly bridging the real and virtual worlds. While this innovative convergence offers unprecedented potential, it also exposes a broader spectrum of security vulnerabilities that challenge conventional approaches. This research aims to fortify the multifaceted ecosystem of the Metaverse, with a particular emphasis on securing IoT healthcare data. Ensuring the protection of health information within the extensive network of interconnected devices in the Metaverse is paramount. Addressing this critical need, we introduce the Dynamic Adaptive Security Mechanism (DASM), an advanced Artificial Intelligence (AI)-driven framework meticulously crafted to enhance security adaptively. DASM operates as a comprehensive and real-time defensive layer, continuously recalibrating its strategies to reinforce the security matrix for both IoT and Digital Twins. This study provides a detailed examination of the foundational architecture of DASM and its AI-driven adaptive processes. We elucidate its pivotal role in strengthening the security framework within the complex terrain of the Metaverse. Through rigorous testing and validation using the IoT healthcare security dataset, the Random Forest model emerges as the top performer, achieving near-perfect metrics, including a Matthews Correlation Coefficient (MCC) of 0.9989 and superior Balanced Accuracy, while also offering reduced training and inference times compared to the LSTM model. Although the LSTM model demonstrates strong accuracy, the ensemble approach of Random Forest balances computational efficiency and performance. The DASM framework sets a new benchmark in IoT security, offering a scalable and effective solution with significant implications for the future of Metaverse applications.
Keyword ：
IoT security IoT security Digital Twins Digital Twins Metaverse Metaverse Machine learning Machine learning Internet of Things Internet of Things Artificial intelligence Artificial intelligence
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| GB/T 7714 | Qureshi, Saima Siraj , He, Jingsha , Zhu, Nafei et al. Enhancing IoT security and healthcare data protection in the metaverse: A Dynamic Adaptive Security Mechanism [J]. | EGYPTIAN INFORMATICS JOURNAL , 2025 , 30 . |
| MLA | Qureshi, Saima Siraj et al. "Enhancing IoT security and healthcare data protection in the metaverse: A Dynamic Adaptive Security Mechanism" . | EGYPTIAN INFORMATICS JOURNAL 30 (2025) . |
| APA | Qureshi, Saima Siraj , He, Jingsha , Zhu, Nafei , Nazir, Ahsan , Fang, Juan , Ma, Xiangjun et al. Enhancing IoT security and healthcare data protection in the metaverse: A Dynamic Adaptive Security Mechanism . | EGYPTIAN INFORMATICS JOURNAL , 2025 , 30 . |
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Abstract ：
Modern processors rely on advanced memory management techniques such as predicting and prefetching to bridge the processor-memory gap. However, current mechanisms are primarily designed for a specific memory hierarchy level or access pattern, resulting in inefficiencies for diverse workloads with mixed memory access patterns. To address this challenge, this paper proposes DHCM, a Dynamic Hierarchy Coordination Mechanism that intelligently schedules prediction hierarchies and dynamically optimizes memory access processes to enhance system performance. DHCM integrates a hierarchy coordination mechanism driven by a State Trigger. This mechanism dynamically leverages system feedback to prioritize and coordinate memory operations, enabling the simultaneous management of both off-chip load requests and on-chip cache accesses. Through extensive evaluations on the ChampSim simulator, DHCM demonstrates its adaptability and efficiency with an average IPC improvement of 34.08% and 24.09% on single-core and multi-core systems, respectively. Additionally, DHCM contributes a 64.17% miss coverage and 89.33% DRAM-loads reduction.
Keyword ：
Data prefetcher Data prefetcher Cache Cache Multi-core Multi-core Hardware prefetcher Hardware prefetcher Micro-architecture Micro-architecture
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| GB/T 7714 | Fang, Juan , Li, Jingjing , Teng, Ziyi . Dhcm: a dynamic hierarchy coordination mechanism for memory optimization [J]. | JOURNAL OF SUPERCOMPUTING , 2025 , 81 (5) . |
| MLA | Fang, Juan et al. "Dhcm: a dynamic hierarchy coordination mechanism for memory optimization" . | JOURNAL OF SUPERCOMPUTING 81 . 5 (2025) . |
| APA | Fang, Juan , Li, Jingjing , Teng, Ziyi . Dhcm: a dynamic hierarchy coordination mechanism for memory optimization . | JOURNAL OF SUPERCOMPUTING , 2025 , 81 (5) . |
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Breast cancer is one of the most prevalent cancers among women, with early detection playing a critical role in improving survival rates. This study introduces a novel transformer-based explainable model for breast cancer lesion segmentation (TEBLS), aimed at enhancing the accuracy and interpretability of breast cancer lesion segmentation in medical imaging. TEBLS integrates a multi-scale information fusion approach with a hierarchical vision transformer, capturing both local and global features by leveraging the self-attention mechanism. This model addresses the limitations of existing segmentation methods, such as the inability to effectively capture long-range dependencies and fine-grained semantic information. Additionally, TEBLS incorporates visualization techniques to provide insights into the segmentation process, enhancing the model's interpretability for clinical use. Experiments demonstrate that TEBLS outperforms traditional and existing deep learning-based methods in segmenting complex breast cancer lesions with variations in size, shape, and texture, achieving a mean DSC of 81.86% and a mean AUC of 97.72% on the CBIS-DDSM test set. Our model not only improves segmentation accuracy but also offers a more explainable framework, which has the potential to be used in clinical settings.
Keyword ：
breast cancer lesion segmentation breast cancer lesion segmentation transformer transformer explainable model explainable model
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| GB/T 7714 | Wang, Huina , Wei, Lan , Liu, Bo et al. Transformer-Based Explainable Model for Breast Cancer Lesion Segmentation [J]. | APPLIED SCIENCES-BASEL , 2025 , 15 (3) . |
| MLA | Wang, Huina et al. "Transformer-Based Explainable Model for Breast Cancer Lesion Segmentation" . | APPLIED SCIENCES-BASEL 15 . 3 (2025) . |
| APA | Wang, Huina , Wei, Lan , Liu, Bo , Li, Jianqiang , Li, Jinshu , Fang, Juan et al. Transformer-Based Explainable Model for Breast Cancer Lesion Segmentation . | APPLIED SCIENCES-BASEL , 2025 , 15 (3) . |
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Abstract ：
In modern parallel system architectures, prefetchers are essential to mitigating the performance challenges posed by long memory access latencies. These architectures rely heavily on efficient memory access patterns to maximize system throughput and resource utilization. Prefetch aggressiveness is a central parameter in managing these access patterns; although increased prefetch aggressiveness can enhance performance for certain applications, it often risks causing cache pollution and bandwidth contention, leading to significant performance degradation in other workloads. While many existing prefetchers rely on static or simple built-in aggressiveness controllers, a more flexible, adaptive approach based on system-level feedback is essential to achieving optimal performance across parallel computing environments. In this paper, we introduce an Adaptive Prefetch Aggressiveness Control (APAC) framework that leverages Reinforcement Learning (RL) to dynamically manage prefetch aggressiveness in parallel system architectures. The APAC controller operates as an RL agent, which optimizes prefetch aggressiveness by dynamically responding to system feedback on prefetch accuracy, timeliness, and cache pollution. The agent receives a reward signal that reflects the impact of each adjustment on both performance and memory bandwidth, learning to adapt its control strategy based on workload characteristics. This data-driven adaptability makes APAC particularly well-suited for parallel architectures, where efficient resource management across cores is essential to scaling system performance. Our evaluation with the ChampSim simulator demonstrates that APAC effectively adapts to diverse workloads and system configurations, achieving performance gains of 6.73$\%$% in multi-core systems compared to traditional Feedback Directed Prefetching (FDP). By improving memory bandwidth utilization, reducing cache pollution, and minimizing inter-core interference, APAC significantly enhances prefetching performance in multi-core processors. These results underscore APAC's potential as a robust solution for performance optimization in parallel system architectures, where efficient resource management is paramount for scaling modern processing environments.
Keyword ：
Data prefetchers Data prefetchers Accuracy Accuracy prefetcher aggressiveness controller prefetcher aggressiveness controller reinforcement learning reinforcement learning Pollution Pollution Adaptive systems Adaptive systems Bandwidth Bandwidth Systems architecture Systems architecture Random access memory Random access memory Resource management Resource management Prefetching Prefetching memory bandwidth memory bandwidth System performance System performance parallel system architectures parallel system architectures Real-time systems Real-time systems
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| GB/T 7714 | Yang, Huijing , Fang, Juan , Hou, Yumin et al. Reinforcement Learning-Driven Adaptive Prefetch Aggressiveness Control for Enhanced Performance in Parallel System Architectures [J]. | IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS , 2025 , 36 (5) : 977-993 . |
| MLA | Yang, Huijing et al. "Reinforcement Learning-Driven Adaptive Prefetch Aggressiveness Control for Enhanced Performance in Parallel System Architectures" . | IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 36 . 5 (2025) : 977-993 . |
| APA | Yang, Huijing , Fang, Juan , Hou, Yumin , Su, Xing , Xiong, Neal N. . Reinforcement Learning-Driven Adaptive Prefetch Aggressiveness Control for Enhanced Performance in Parallel System Architectures . | IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS , 2025 , 36 (5) , 977-993 . |
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Abstract ：
Serving the ever-growing demand for computation, storage, and networking resources for multi-tenant in cloud computing is an important mission of Data Center Networks (DCNs). In this paper, we study the dynamic request updating problem, and our objective is to maximize the elasticity of cloud-based DCNs while achieving rapid response to multi-tenants. We use virtual clusters under the hose communication model to denote requests. Instead of using heuristic algorithms as the existing work does, this paper introduces a novel two-stage dynamic request updating framework with elastic resource scheduling strategy. In the first stage, we propose a multi-tenant fast initial provisioning scheme to realize the real-time response and analyze its optimality and complexity. Additionally, we provide a deep reinforcement learning-based dynamic updating strategy to enhance the elasticity of virtual clusters that are being used or scaling during the second stage. We train a fully connected neural network by creating a new feasible action set to realize the reduction, and it approximates the policy based on a proposed aggressive objective selection method to improve training speed while avoiding high dimensions caused by large scales of tenants and DCNs. Extensive evaluations demonstrate that our scheme outperforms baselines in terms of both elasticity and efficiency.
Keyword ：
Clustering algorithms Clustering algorithms Elasticity Elasticity Neural networks Neural networks dynamic request updating dynamic request updating Data centers Data centers Dynamic scheduling Dynamic scheduling elastic scheduling elastic scheduling multi-tenant multi-tenant Cloud computing Cloud computing Processor scheduling Processor scheduling Data center network Data center network resource provisioning resource provisioning
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| GB/T 7714 | Lu, Shuaibing , Wu, Jie , Shi, Jiamei et al. Towards Dynamic Request Updating With Elastic Scheduling for Multi-Tenant Cloud-Based Data Center Network [J]. | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2024 , 11 (2) : 2223-2237 . |
| MLA | Lu, Shuaibing et al. "Towards Dynamic Request Updating With Elastic Scheduling for Multi-Tenant Cloud-Based Data Center Network" . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 11 . 2 (2024) : 2223-2237 . |
| APA | Lu, Shuaibing , Wu, Jie , Shi, Jiamei , Fang, Juan , Zhang, Jiayue , Liu, Haiming . Towards Dynamic Request Updating With Elastic Scheduling for Multi-Tenant Cloud-Based Data Center Network . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2024 , 11 (2) , 2223-2237 . |
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Abstract ：
Edge computing has emerged as a promising paradigm to meet the increasing demands of latency-sensitive and computationally intensive applications. In this context, efficient server deployment and service placement are crucial for optimizing performance and increasing platform profit. This paper investigates the problem of server deployment and service placement in a multi-user scenario, aiming to enhance the profit of Mobile Network Operators while considering constraints related to distance thresholds, resource limitations, and connectivity requirements. We demonstrate that this problem is NP-hard. To address it, we propose a two-stage method to decouple the problem. In stage I, server deployment is formulated as a combinatorial optimization problem within the framework of a Markov Decision Process (MDP). We introduce the Server Deployment with Q-learning (SDQ) algorithm to establish a relatively stable server deployment strategy. In stage II, service placement is formulated as a constrained Integer Nonlinear Programming (INLP) problem. We present the Service Placement with Interior Barrier Method (SPIB) and Tree-based Branch-and-Bound (TDB) algorithms and theoretically prove their feasibility. For scenarios where the number of users changes dynamically, we propose the Distance-and-Utilization Balance Algorithm (DUBA). Extensive experiments validate the exceptional performance of our proposed algorithms in enhancing the profit.
Keyword ：
Mobile handsets Mobile handsets Symbols Symbols Mobile edge computing Mobile edge computing profit-driven optimization profit-driven optimization Servers Servers Clustering algorithms Clustering algorithms Heuristic algorithms Heuristic algorithms Programming Programming integer nonlinear programming integer nonlinear programming Costs Costs Telecommunications Telecommunications Base stations Base stations Optimization Optimization reinforcement learning reinforcement learning
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| GB/T 7714 | Fang, Juan , Wu, Shen , Lu, Shuaibing et al. Enhanced Profit-Driven Optimization for Flexible Server Deployment and Service Placement in Multi-User Mobile Edge Computing Systems [J]. | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2024 , 11 (6) : 6194-6206 . |
| MLA | Fang, Juan et al. "Enhanced Profit-Driven Optimization for Flexible Server Deployment and Service Placement in Multi-User Mobile Edge Computing Systems" . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 11 . 6 (2024) : 6194-6206 . |
| APA | Fang, Juan , Wu, Shen , Lu, Shuaibing , Teng, Ziyi , Chen, Huijie , Xiong, Neal N. . Enhanced Profit-Driven Optimization for Flexible Server Deployment and Service Placement in Multi-User Mobile Edge Computing Systems . | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING , 2024 , 11 (6) , 6194-6206 . |
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