IEEE Transactions on Cloud Computing最新文献_第10页

Reinforcement Learning-Based Equipment Combination Selection Optimization for Multi-Layer Kill Webs 基于强化学习的多层杀伤网设备组合选择优化

IF 5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2026-04-01 Epub Date: 2026-04-14 DOI: 10.1109/TCC.2026.3683729

Chao Fang;Waris Ali;Hang Xu;Yingshan Li;Dongyang Wang;Zhihao Qu;Deze Zeng

{"title":"Reinforcement Learning-Based Equipment Combination Selection Optimization for Multi-Layer Kill Webs","authors":"Chao Fang;Waris Ali;Hang Xu;Yingshan Li;Dongyang Wang;Zhihao Qu;Deze Zeng","doi":"10.1109/TCC.2026.3683729","DOIUrl":"https://doi.org/10.1109/TCC.2026.3683729","url":null,"abstract":"Modern network-centric operations increasingly rely on multi-layer Kill Webs (KWs), enabling redundant and non-linear sensing-to-strike pathways while introducing a combinatorial equipment selection problem under uncertainty and resource constraints. This paper formulates a multi-layer KW equipment combination selection as a sequential decision-making problem by explicitly modeling heterogeneous equipment capabilities, resource constraints, and the network topology. To address this problem, we developed an RL learning-based optimization framework, where a multi-objective reward function integrates normalized relevance, operational risk, and timeliness, with a penalty mechanism for infeasible or incomplete kill-chain closure. Based on the jointly captured state information (e.g., network structure, equipment attributes, target characteristics, and resource availability), an Actor-Critic (AC) algorithm is developed to learn adaptive equipment combination selection across different operational stages using temporal-difference advantage estimation and entropy regularization. Simulation results under diverse battlefield scenarios demonstrate that the proposed framework consistently outperforms Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Particle Swarm Optimization (PSO), achieving at least a 19.6% improvement in overall operational effectiveness while maintaining low decision latency.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"14 2","pages":"1172-1186"},"PeriodicalIF":5.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148196086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PhishBERT: Phishing Detection Using Certificate Transparency Logs PhishBERT：使用证书透明度日志进行网络钓鱼检测

IF 5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2026-04-01 Epub Date: 2026-04-29 DOI: 10.1109/TCC.2026.3688965

Qingqing Xie;Yuanyuan Liu;Yantian Hou

引用次数: 0

Lyapunov-Driven Optimization for Energy-Cost Minimization in Data Center With Renewable Energy and Time-Varying Electricity Prices 具有可再生能源和时变电价的数据中心能源成本最小化的lyapunov驱动优化

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2026-04-01 Epub Date: 2026-03-03 DOI: 10.1109/TCC.2026.3670125

Wenjia Zhao;Linghao Xu;Yong Qi

{"title":"Lyapunov-Driven Optimization for Energy-Cost Minimization in Data Center With Renewable Energy and Time-Varying Electricity Prices","authors":"Wenjia Zhao;Linghao Xu;Yong Qi","doi":"10.1109/TCC.2026.3670125","DOIUrl":"https://doi.org/10.1109/TCC.2026.3670125","url":null,"abstract":"Data centers are increasingly adopting renewable energy sources to mitigate environmental impact and reduce operational costs. However, effectively optimizing energy costs remains challenging due to unpredictable workloads and fluctuating renewable energy availability. This article introduces LOECM, a Lyapunov-driven online scheduling algorithm designed to minimize energy cost without relying on future information. LOECM is formulated as an online stochastic optimization problem and leverages Lyapunov optimization techniques to balance immediate cost minimization against task queue stability. By exploiting the inherent structure of the optimization problem, we design an efficient solver with constant time and space complexity (<inline-formula><tex-math>$mathcal {O}(1)$</tex-math></inline-formula>). Additionally, we propose the LOECM scheduling policy to dynamically manage workloads and node activation based on a covering subset strategy. To evaluate the practicality and effectiveness of our approach, we implement a prototype system called LOECMHadoop, integrating the LOECM scheduler into Hadoop—a widely-used data processing platform—across a 10-node cluster. Experimental results show that our power model has an average deviation of approximately 5%, and LOECM achieves over 20% electricity cost savings compared without our policy on the prototype system.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"14 2","pages":"684-700"},"PeriodicalIF":5.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Alarm Prediction Framework Based on the Propagation Dependency and Reinforcement Learning Toward Cloud Services 基于传播依赖和强化学习的云服务告警预测框架

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2026-04-01 Epub Date: 2026-02-16 DOI: 10.1109/TCC.2026.3665339

Peng Qi;Dan Tao;Ruipeng Gao

{"title":"An Alarm Prediction Framework Based on the Propagation Dependency and Reinforcement Learning Toward Cloud Services","authors":"Peng Qi;Dan Tao;Ruipeng Gao","doi":"10.1109/TCC.2026.3665339","DOIUrl":"https://doi.org/10.1109/TCC.2026.3665339","url":null,"abstract":"In cloud platforms, the occurrence of failures in cloud services is usually accompanied by a substantial number of alarms, posing challenges to the operation, management, and maintenance. To address this issue, this paper proposes an alarm prediction framework based on propagation dependency and reinforcement learning for cloud services. First, a three-layer cloud service alarm knowledge graph is constructed, encompassing the device layer, service layer, and alarm layer, with the objective of integrating alarm data. Then, to filter out irrelevant alarms, an alarm compression schema is designed, taking into account both structural and semantic information. It achieves the preservation of propagation information by mining frequent alarm subtrees. After that, a reinforcement learning-based alarm prediction model is devised, including an alarm-driven reinforcement learning network and a global alarm discriminator. The former formulates the alarm propagation problem as a reinforcement learning inferencing task, and designs an immediate reward function to simulate the local propagation patterns of alarms. The latter guides the agent to learn alarm evolution patterns from a global perspective through an adversarial approach. Experimental results on two datasets demonstrate the effectiveness of our alarm compression and prediction models.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"14 2","pages":"551-564"},"PeriodicalIF":5.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148505541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Agent Deep Reinforcement Learning-Based Distributed Task Assignment in Multi-UAV Cooperative Edge Computing 基于多智能体深度强化学习的多无人机协同边缘计算分布式任务分配

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2026-04-01 Epub Date: 2026-02-26 DOI: 10.1109/TCC.2026.3668236

Wendong Zuo;Siteng Liao;Yangguang Cui;Tong Liu;Hui Cai;Shaohua Wan

{"title":"Multi-Agent Deep Reinforcement Learning-Based Distributed Task Assignment in Multi-UAV Cooperative Edge Computing","authors":"Wendong Zuo;Siteng Liao;Yangguang Cui;Tong Liu;Hui Cai;Shaohua Wan","doi":"10.1109/TCC.2026.3668236","DOIUrl":"https://doi.org/10.1109/TCC.2026.3668236","url":null,"abstract":"Leveraging flexible deployment and wide-area coverage, UncrewedAerial Vehicles (UAVs) assisted edge computing (EC) extends computation capability to the network edge and has become a key paradigm for improving the quality of service in the Internet of Things. However, constrained by onboard resources, UAVs struggle to independently process massive heterogeneous tasks in real time. Existing studies mainly focus on cooperation between UAVs and ground devices or the cloud, while the coupled cooperative relationships among UAVs and the performance scalability in large-scale UAV networks remain insufficiently characterized. To this end, we propose a distributed task assignment framework for a multi-UAV cooperative EC system, where each UAV explicitly accounts for cooperation with other UAVs and, leveraging controllable mobility, assigns a portion of local tasks to other UAVs or base stations to minimize the long-term system-wide total latency. To address the resulting joint trajectory control and task assignment optimization problem, we first formulate it as a Markov decision process. We then propose a Multi-head self-Attention critic-assisted MADDPG (MA<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>DDPG) algorithm to train local online decision models for UAVs. Under the fully cooperative setting, we employ a centralized Critic to exploit global information and reduce parameter redundancy; concurrently, a multi-head self-attention mechanism is incorporated into the Critic to aggregate multi-UAV interaction information, delineate implicit cooperative dependencies, and alleviate the network input dimensionality curse arising from increasing UAV scale. Finally, extensive simulation experiments validate the effectiveness of the proposed method.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"14 2","pages":"609-625"},"PeriodicalIF":5.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148506811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UAV Deployment Optimization in Multi-UAV-Aided MEC Systems Using Federated Deep Reinforcement Learning 基于联邦深度强化学习的多无人机辅助MEC系统中无人机部署优化

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2026-04-01 Epub Date: 2026-03-30 DOI: 10.1109/TCC.2026.3678411

Zheyi Chen;Dequan Fu;Longhai Zheng;Xing Chen;Chunming Rong;Geyong Min

{"title":"UAV Deployment Optimization in Multi-UAV-Aided MEC Systems Using Federated Deep Reinforcement Learning","authors":"Zheyi Chen;Dequan Fu;Longhai Zheng;Xing Chen;Chunming Rong;Geyong Min","doi":"10.1109/TCC.2026.3678411","DOIUrl":"https://doi.org/10.1109/TCC.2026.3678411","url":null,"abstract":"Automatic aerial vehicle (UAV) aided Mobile Edge Computing (MEC) has emerged as a promising technique to offer computing support for high-mobility and high-demand mobile devices (MDs). However, due to the dynamic scale of UAVs and MDs as well as their changeable resource availability and demands, it is very challenging to quickly make suitable UAV deployment plans for satisfying the real-time requirements. Existing solutions commonly adopt the centralized decision-making manner based on the global information, which leads to poor scalability, excessive search time, and repeated training costs. To address these important challenges, we propose a novel Federated deep Reinforcement learning based UAV Deployment optimization method (FRUD) for multi-UAV-aided MEC systems, aiming to minimize the average task response time via optimizing the real-time deployment locations of large-scale UAVs. In FRUD, each UAV independently conducts the deployment decision-making based on the local information of runtime environments rather than using the global information. Next, through feedback control and multi-UAV cooperation, an effective UAV deployment plan can be gradually formed. Simulation and real-world testbed evaluation results show that the proposed FRUD well handles the UAV deployment problem in large-scale and dynamic multi-UAV-aided MEC systems and outperforms the state-of-art methods.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"14 2","pages":"985-998"},"PeriodicalIF":5.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DRL-Based Computing Resource Matching for Task Inference and Block Mining in Blockchain-Assisted Edge Intelligence 基于drl的区块链辅助边缘智能任务推理和块挖掘计算资源匹配

IF 5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2026-04-01 Epub Date: 2026-04-29 DOI: 10.1109/TCC.2026.3688901

Zhibo Hao;Wenhao Fan;Chenhui Bao;Guan Wang;Lun Li;Penghui Chen;Bihua Tang;Yuan’an Liu

{"title":"DRL-Based Computing Resource Matching for Task Inference and Block Mining in Blockchain-Assisted Edge Intelligence","authors":"Zhibo Hao;Wenhao Fan;Chenhui Bao;Guan Wang;Lun Li;Penghui Chen;Bihua Tang;Yuan’an Liu","doi":"10.1109/TCC.2026.3688901","DOIUrl":"https://doi.org/10.1109/TCC.2026.3688901","url":null,"abstract":"In a blockchain-assisted edge intelligence system, blockchain can effectively enhance the reliability and trustworthiness of artificial intelligence (AI) task inference. When delegating an AI task from a user device to an edge server, blockchain will broadcast it to the entire network by recording it as a transaction and then conduct the verification process. Computing resource matching is a core issue of blockchain-assisted edge intelligence system, because block mining and task inference will face sharing of computing resources. The prerequisite for achieving optimal system performance is to match the block generation speed in block mining with the number of AI tasks per unit time in the edge intelligence system by computing resource allocation. However, prior studies have neglected the matching issue mentioned above and have not incorporated discussions on the properties of AI tasks, i.e., task inference accuracy. We propose a strategy for task offloading and resource allocation in the blockchain-assisted edge intelligence system. To minimize both the overall delay of task processing and the energy consumption of the user devices, we propose a combined optimization problem that includes task offloading decision, transmission power control, task data size adjustment, and allocation of computing resource on the premise of satisfying the task inference accuracy constraint. To this end, we propose a deep reinforcement learning (DRL) algorithm that incorporates the twin delayed deep deterministic policy gradient (TD3) with a swift numerical approach, significantly lessening the time complexity of the algorithm. To demonstrate the superiority of our scheme, we analyze the complexity and convergence of our algorithm, and then undertake numerous simulation experiments and compare the performance with the other 3 reference schemes in 6 scenarios.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"14 2","pages":"1229-1243"},"PeriodicalIF":5.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148196152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Benchmarking AI Inference Efficiency in Public and Private Clouds: An MLPerf-Based Comparative Study 公共云和私有云中人工智能推理效率的基准测试：基于mlperf的比较研究

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2026-04-01 Epub Date: 2026-03-19 DOI: 10.1109/TCC.2026.3674888

Carl Beitsayadeh;Pamayla E. Darbyshire

{"title":"Benchmarking AI Inference Efficiency in Public and Private Clouds: An MLPerf-Based Comparative Study","authors":"Carl Beitsayadeh;Pamayla E. Darbyshire","doi":"10.1109/TCC.2026.3674888","DOIUrl":"https://doi.org/10.1109/TCC.2026.3674888","url":null,"abstract":"AI inference is emerging as a major driver of energy consumption in large-scale computing environments, yet most existing research focuses on the training phase of machine learning models. This cross-sectional secondary analysis benchmarks inference throughput and energy efficiency across public and private cloud infrastructures using 739 standardized submissions from MLPerf Inference v5.0. Efficiency was estimated as throughput normalized by vendor-reported thermal design power (TDP), providing a reproducible proxy for performance-per-watt comparison. Grounded in systems theory, the analysis treats inference efficiency as an emergent property of interacting hardware, infrastructure, and workload subsystems. Statistical methods included Welch's t-tests, one- and two-way ANOVA, and linear and log-linear regression with residual diagnostics and model-fit evaluation. Results indicated no significant cloud-type effect, significant differences across accelerator models (with NVIDIA H200-SXM-141GB exhibiting approximately 45% higher median efficiency), and no cloud–accelerator interaction. The log-linear specification provided the best model fit, suggesting multiplicative scaling in efficiency across accelerator generations. The study introduces a transparent benchmarking framework for estimating inference efficiency using open MLPerf data and highlights inference optimization as an important pathway toward sustainable AI infrastructure.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"14 2","pages":"862-876"},"PeriodicalIF":5.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Oikonomos-II+: A Reinforcement-Learning, Cloud-Resource Recommender for HPC & AI Workloads Oikonomos-II+：用于高性能计算和人工智能工作负载的强化学习，云资源推荐

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2026-04-01 Epub Date: 2026-04-09 DOI: 10.1109/TCC.2026.3682234

River Betting;Qilin Chen;Chris De Zeeuw;Christos Strydis

{"title":"Oikonomos-II+: A Reinforcement-Learning, Cloud-Resource Recommender for HPC & AI Workloads","authors":"River Betting;Qilin Chen;Chris De Zeeuw;Christos Strydis","doi":"10.1109/TCC.2026.3682234","DOIUrl":"https://doi.org/10.1109/TCC.2026.3682234","url":null,"abstract":"Oikonomos-II+ is a hybrid, reinforcement-learning system for recommending optimal cloud-instance types for High-Performance Computing (HPC) and Artificial-Intelligence (AI) applications. Unlike existing approaches that require historical data or repeated job executions, Oikonomos-II+ learns online using user-submitted jobs. It combines a modified Neural-LinUCB algorithm with Gaussian-Process regression to model the relationship between job parameters, instance types, and execution time. This allows it to balance exploration and exploitation efficiently, even in the absence of prior data. We evaluated six configurations of Oikonomos-II+ on a diverse set of HPC and AI workloads, optimizing for cost and speed. Results show that the complete system converges to optimal resource choices, outperforming purely predictive or search-based approaches. By treating deployed applications as a black box and by eliminating the need for preexisting training data or auxiliary runs, Oikonomos-II+ provides a general-purpose, low-overhead solution for dynamic resource selection in heterogeneous cloud environments.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"14 2","pages":"1141-1157"},"PeriodicalIF":5.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11478011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148505273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diktopos: A Two-Stage Framework for Joint Container-Based Microservice Placement and Distributed Volume Allocation on Cloud-Edge Networks Diktopos：在云边缘网络上联合基于容器的微服务放置和分布式卷分配的两阶段框架

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2026-04-01 Epub Date: 2026-03-20 DOI: 10.1109/TCC.2026.3676203

Mohammed Dhiya Eddine Gouaouri;Sihem Ouahouah;Miloud Bagaa;Messaoud Ahmed Ouameur;Daniel Massicotte;Adlen Ksentini

{"title":"Diktopos: A Two-Stage Framework for Joint Container-Based Microservice Placement and Distributed Volume Allocation on Cloud-Edge Networks","authors":"Mohammed Dhiya Eddine Gouaouri;Sihem Ouahouah;Miloud Bagaa;Messaoud Ahmed Ouameur;Daniel Massicotte;Adlen Ksentini","doi":"10.1109/TCC.2026.3676203","DOIUrl":"https://doi.org/10.1109/TCC.2026.3676203","url":null,"abstract":"The Cloud-Edge collaborative computing enables the deployment of latency-sensitive and data-intensive applications closer to end users. However, it introduces significant challenges for microservice placement, due to resource heterogeneity, limited edge capacity, and the need to satisfy storage requirements using aggregated resources across multiple nodes. To address these issues, we propose <bold>Diktopos</b>, a topology-aware, two-stage scheduling framework that jointly optimizes microservice placement and distributed storage volume allocation in cloud-edge networks. The joint optimization problem is decomposed into two subproblems: (i) microservice placement and (ii) distributed volume allocation, with the objective of minimizing computation, communication, energy, and storage costs. At its core, Diktopos employs a low-complexity, rank-based heuristic that ensures scalable and accurate placement across heterogeneous edge nodes. Simulation results show that our method achieves near-optimal placement decisions (within 1.67% of the optimal solution), and converges up to 5× faster than state-of-the-art approaches in large-scale deployments. Real-world experiments in Kubernetes environments demonstrate up to 53% latency reduction compared to the default scheduler, and up to 23% improvement over other baselines, confirming Diktopos’ effectiveness in dynamic, resource-constrained edge scenarios.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"14 2","pages":"931-948"},"PeriodicalIF":5.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0