IEEE Transactions on Services Computing最新文献_第6页

Generalizable Pareto-Optimal Offloading with Reinforcement Learning in Mobile Edge Computing 移动边缘计算中基于强化学习的广义pareto最优卸载

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-08-29 DOI: 10.1109/tsc.2025.3604371

Ning Yang, Junrui Wen, Meng Zhang, Ming Tang

引用次数: 0

Towards a Trust Ecosystem for Crowdsourcing IoT Services: A Macro Perspective 面向众包物联网服务的信任生态系统：宏观视角

IF 5.8 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-08-29 DOI: 10.1109/TSC.2025.3604379

Dianjie Lu;Guijuan Zhang;Yu Guo;Xiaohua Jia

{"title":"Towards a Trust Ecosystem for Crowdsourcing IoT Services: A Macro Perspective","authors":"Dianjie Lu;Guijuan Zhang;Yu Guo;Xiaohua Jia","doi":"10.1109/TSC.2025.3604379","DOIUrl":"10.1109/TSC.2025.3604379","url":null,"abstract":"Trust plays a crucial role in crowdsourcing Internet of Things (IoT), as it can be used to select trustworthy participants to improve the quality of crowdsourced services and strengthen system security. While traditional research has focused on micro-level aspects, including trust computation and propagation, a comprehensive macro-level trust analysis remains underexplored. In this paper, we propose a macroscopic trust ecosystem analysis framework for crowdsourcing IoT services. We first construct a Trust Ecosystem Model (TEM), where trust clusters serve as an abstraction to capture and quantify overall trust characteristics based on their size and structure. To analyze the dynamic evolution of TEM, we propose a Percolation-based Trust Ecosystem Analysis Model (P-TEAM), which maps the formation of trust clusters to a joint site-bond percolation process. Thus, the study of TEM evolution can be reframed into an investigation of how trust clusters evolve as users’ trust attributes change. Through P-TEAM, we identify the critical thresholds associated with trust attributes that trigger trust phase transitions in crowdsourcing IoT services, which act as key metrics for evaluating the ecosystem’s robustness macroscopically. Finally, we further evaluate the trust ecosystem beyond these thresholds by calculating the proportions of trusted giant components. We validate our approach on directed networks, using both synthetic and real-world datasets. The experimental results further substantiate our findings and provide valuable insights into constructing a healthy and sustainable trust ecosystem for crowdsourcing IoT services.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 5","pages":"3292-3306"},"PeriodicalIF":5.8,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919271","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

AttResRec: Learning User Credibility for Attack Resistant Matrix Factorization Recommendation 学习用户可信度的抗攻击矩阵分解推荐

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-08-29 DOI: 10.1109/tsc.2025.3604375

Jiangpeng Zhao, Wen Zhang, Song Wang, Quan Bai, Kuien Liu

引用次数: 0

Trident: A Provider-Oriented Resource Management Framework for Serverless Computing Platforms Trident：面向提供者的无服务器计算平台资源管理框架

IF 5.8 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-08-28 DOI: 10.1109/TSC.2025.3603867

Botao Zhu;Yifei Zhu;Chen Chen;Linghe Kong

{"title":"Trident: A Provider-Oriented Resource Management Framework for Serverless Computing Platforms","authors":"Botao Zhu;Yifei Zhu;Chen Chen;Linghe Kong","doi":"10.1109/TSC.2025.3603867","DOIUrl":"10.1109/TSC.2025.3603867","url":null,"abstract":"Serverless computing has become increasingly popular due to its flexible and hassle-free service, relieving users from traditional resource management burdens. However, the shift in responsibility has led to unprecedented challenges for serverless providers in managing virtual machines (VMs) and serving heterogeneous function instances. Serverless providers need to purchase, provision and manage VM instances from IaaS providers, aiming to minimize VM provisioning costs while ensuring compliance with Service Level Objectives (SLOs). In this paper, we propose Trident, a provider-oriented resource management framework for serverless computing platforms. Trident optimizes three major serverless computing provisioning problems for serverless providers: workload prediction, VM provisioning, and function placement. Specifically, Trident introduces a novel dynamic model selection algorithm for more accurate workload prediction. With the prediction results, Trident then carefully designs a hierarchical reinforcement learning (HRL)-based approach for VM provisioning with a mix of types and configurations. To further improve resource utilization, Trident employs an effective collocation placement strategy for efficient function container scheduling. Evaluations on the Azure Function dataset demonstrate that Trident maintains the lowest probability of violating SLOs while simultaneously achieving substantial cost savings of up to 71.8% in provisioning expense compared to state-of-the-art methods from industry and academia.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 5","pages":"3334-3347"},"PeriodicalIF":5.8,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144915645","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

Serving LLM in Distributed GPU Cluster with Fine-Grain Pipeline Constraints 基于细粒度管道约束的分布式GPU集群LLM服务

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-08-28 DOI: 10.1109/tsc.2025.3603576

Yanying Lin, Shijie Peng, Shuaipeng Wu, Yanbo Li, Chengzhi Lu, Kejiang Ye, Chengzhong Xu

引用次数: 0

A Game-Theoretic Approach for Microservice Request Dispatching in Mobile Edge Computing Systems 移动边缘计算系统微服务请求调度的博弈论方法

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-08-26 DOI: 10.1109/tsc.2025.3602905

Hongyue Wu, Bowen Shi, Qiang He, Guangming Cui, Shizhan Chen, Zhiyong Feng, Albert Y. Zomaya, Shuiguang Deng

引用次数: 0

Secure and Privacy Preserving Scalable GDPR Enforcement System 安全和隐私保护可扩展的GDPR执行系统

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-08-26 DOI: 10.1109/tsc.2025.3603041

Mou Dutta, Subhasish Dhal, Hema Gohain

引用次数: 0

Comment on “RCME: A Reputation Incentive Committee Consensus-Based for Matchmaking Encryption in IoT Healthcare” 评论“RCME：基于声誉激励委员会共识的物联网医疗撮合加密”

IF 5.8 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-08-25 DOI: 10.1109/TSC.2025.3601977

Jiseung Kim;Hyung Tae Lee

引用次数: 0

A Novel Reciprocal Dual-channel Preference Extraction and Refinement Network for Category-aware Service Recommendation 一种新的双向双通道偏好提取和优化网络用于类别感知服务推荐

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-08-21 DOI: 10.1109/tsc.2025.3601040

Shuxiang Xu, Qibu Xiang, Yushun Fan, Jia Zhang

引用次数: 0

Understanding AWS Provider Dependencies Updates in Infrastructure-as-Code: Empirical Study, Taxonomy, and Insights 了解基础设施即代码中的AWS提供商依赖关系更新：实证研究、分类和见解

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-08-20 DOI: 10.1109/tsc.2025.3601051

Mahi Begoug, Ali Ouni, Jasem Khelifi

引用次数: 0