{"title":"Generalizable Pareto-Optimal Offloading with Reinforcement Learning in Mobile Edge Computing","authors":"Ning Yang, Junrui Wen, Meng Zhang, Ming Tang","doi":"10.1109/tsc.2025.3604371","DOIUrl":"https://doi.org/10.1109/tsc.2025.3604371","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"164 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919267","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}
{"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}
Jiangpeng Zhao, Wen Zhang, Song Wang, Quan Bai, Kuien Liu
{"title":"AttResRec: Learning User Credibility for Attack Resistant Matrix Factorization Recommendation","authors":"Jiangpeng Zhao, Wen Zhang, Song Wang, Quan Bai, Kuien Liu","doi":"10.1109/tsc.2025.3604375","DOIUrl":"https://doi.org/10.1109/tsc.2025.3604375","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"52 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144919268","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}
{"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}
{"title":"Comment on “RCME: A Reputation Incentive Committee Consensus-Based for Matchmaking Encryption in IoT Healthcare”","authors":"Jiseung Kim;Hyung Tae Lee","doi":"10.1109/TSC.2025.3601977","DOIUrl":"10.1109/TSC.2025.3601977","url":null,"abstract":"Recently, Yang et al. proposed a reputation incentive committee consensus-based matchmaking scheme (IEEE Transactions on Services Computing, 2024), claiming to achieve indistinguishability under adaptive chosen ciphertext attacks (IND-CCA2). In this work, we present a plaintext recovery attack against their scheme under the adaptive chosen ciphertext attack model, analyze the design and proof flaws enabling the attacks, and suggest a countermeasure to achieve the IND-CCA2 security.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 5","pages":"3362-3363"},"PeriodicalIF":5.8,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900518","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}