{"title":"Calling Out Trustless Users: A Trust Propagation Scheme for Decentralized Trust Management","authors":"Yong Yu, Haochen Yang, Yannan Li, Robert H. Deng","doi":"10.1109/tsc.2025.3601226","DOIUrl":"https://doi.org/10.1109/tsc.2025.3601226","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"15 1","pages":"1-13"},"PeriodicalIF":8.1,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900523","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":"Fault-Tolerant Cost-Efficient Scheduling for Energy and Deadline-Constrained IoT Workflows in Edge-Cloud Continuum","authors":"Ahmad Taghinezhad-Niar, Javid Taheri","doi":"10.1109/tsc.2025.3599497","DOIUrl":"https://doi.org/10.1109/tsc.2025.3599497","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"47 1","pages":"1-12"},"PeriodicalIF":8.1,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900531","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":"Integrating Functional and Structural Semantics for Web API Recommendation via Multi Meta-path Aggregation","authors":"Chunyan Sang, Y ahao Liu, Shigen Liao, Junhao Wen","doi":"10.1109/tsc.2025.3599574","DOIUrl":"https://doi.org/10.1109/tsc.2025.3599574","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"25 1","pages":"1-14"},"PeriodicalIF":8.1,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900530","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}
Yi Hu;Liangbo Hou;Junhui Hu;Mingyuan Ren;Menglan Hu;Chao Cai;Kai Peng
{"title":"Time-Varying Microservice Orchestration With Routing for Dynamic Call Graphs via Multi-Scale Deep Reinforcement Learning","authors":"Yi Hu;Liangbo Hou;Junhui Hu;Mingyuan Ren;Menglan Hu;Chao Cai;Kai Peng","doi":"10.1109/TSC.2025.3597631","DOIUrl":"10.1109/TSC.2025.3597631","url":null,"abstract":"Lightweight microservices as a software architecture have been widely adopted in online application development. However, in highly-concurrent microservice scenarios, frequent data communications, complex call dependencies, and dynamic delay requirements bring great challenges to efficient microservice orchestration. In this case, service deployment and request routing are interactively-coupled in multi-instance modeling, and cannot be locally optimized effectively, thereby enlarging the difficulty for collaborative orchestration. To accommodate time-varying request properties and dynamic microservice multiplexing, orchestration schemes are frequently adapted to real-time parallel request queues, further complicating the difficulty. Nevertheless, most previous work failed to propose appropriate models and methods for the above issues. Therefore, this paper investigates the online microservice orchestration with probabilistic routing for dynamic call graphs in clouds. First, we formulate the time-slot-based joint optimization problem as a Markov Decision Process. The open Jackson queuing networks are used to accurately establish multi-instance models and analyze the request queuing, processing, and communicating delays. Then, we propose an efficient curiosity-driven deep reinforcement learning algorithm, which meticulously implements instance-level orchestration through multi-dimensional collaborative decisions and multi-time-scale trigger events. Finally, through comprehensive trace-driven experiments, our proposed approach significantly outperforms other baselines in terms of orchestration cost and resource utilization.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 5","pages":"3276-3291"},"PeriodicalIF":5.8,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850618","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 Fairness Exploration and Optimization for Digital Service Networks","authors":"Zhongxuan Han;Li Zhang;Chaochao Chen;Xiaolin Zheng;Yuyuan Li;Shuiguang Deng;Guanjie Cheng;Schahram Dustdar","doi":"10.1109/TSC.2025.3595214","DOIUrl":"https://doi.org/10.1109/TSC.2025.3595214","url":null,"abstract":"Digital service networks often face the challenge of <bold>S</b>ervice-<bold>O</b>riented <bold>F</b>airness (SOF), where service nodes with varying levels of activity may receive unequal treatment. This article takes the recommendation service system as a representative case to explore and mitigate the impact of SOF. The SOF issue in the recommendation service system can be abstracted as <bold>U</b>ser-<bold>O</b>riented <bold>F</b>airness (UOF), where service models often exhibit bias toward a small group of users, resulting in significant unfairness in the quality of recommendations. Existing research on UOF faces three major limitations, and no single approach effectively addresses all of them. <bold>Limitation 1:</b> Post-processing methods fail to address the root cause of the UOF issue. <bold>Limitation 2:</b> Some in-processing methods rely heavily on unstable user similarity calculations under severe data sparsity problems. <bold>Limitation 3:</b> Other in-processing methods overlook the disparate treatment of individual users within user groups. In this article, we propose a novel <bold>I</b>ndividual <bold>R</b>eweighting for <bold>U</b>ser-<bold>O</b>riented <bold>F</b>airness framework, namely IR-UOF, to address all the aforementioned limitations. The motivation behind IR-UOF is to <italic>introduce an in-processing strategy that addresses the UOF issue at the individual level without the need to explore user similarities.</i> We first conduct extensive experiments on three real-world recommendation service datasets using four backbone recommendation models to demonstrate the effectiveness of IR-UOF in mitigating UOF and improving recommendation fairness. Furthermore, we select two general digital service datasets to prove that IR-UOF can be extended to tackle the general SOF issue in other types of digital service networks. In summary, the IR-UOF framework achieves optimal model performance across all datasets, while improving fairness by at least 3.8% in recommendation systems and 24.7% in general service systems.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 5","pages":"3307-3320"},"PeriodicalIF":5.8,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248075","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}