Pub Date : 2025-11-25DOI: 10.1109/tsc.2025.3632804
Luqi Huang, Fuchun Guo, Willy Susilo, Li Wang
{"title":"IoT-Cloud Data Sharing and Access Control System with Efficient Policy Updating","authors":"Luqi Huang, Fuchun Guo, Willy Susilo, Li Wang","doi":"10.1109/tsc.2025.3632804","DOIUrl":"https://doi.org/10.1109/tsc.2025.3632804","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"1 1","pages":"1-12"},"PeriodicalIF":8.1,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145599022","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":"SageCopilot: an LLM-empowered Autonomous Agent for Data Science as a Service","authors":"Yuan Liao, Jiang Bian, Yuhui Yun, Shuo Wang, Yubo Zhang, Jiaming Chu, Tao Wang, Yuchen Li, Xuhong Li, Shilei Ji, Haoyi Xiong","doi":"10.1109/tsc.2025.3635384","DOIUrl":"https://doi.org/10.1109/tsc.2025.3635384","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"11 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567456","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":"Location Privacy Protection Method Based on Local Differential Privacy in Crowdsensing With Approximately Accurate Task Allocation","authors":"Yutao Huang, Tianjiao Ni, Ying Liu, Peng Hu, Qingying Yu, Yonglong Luo","doi":"10.1109/tsc.2025.3635240","DOIUrl":"https://doi.org/10.1109/tsc.2025.3635240","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"104 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567255","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}
Pub Date : 2025-11-19DOI: 10.1109/TSC.2025.3634801
Alejandro García-Fernández;José Antonio Parejo;Francisco Javier Cavero;Antonio Ruiz-Cortés
The SaaS paradigm has popularized the usage of pricings, allowing providers to offer a wide range of subscription possibilities. This creates a vast configuration space for customers, enabling them to choose the features, limits and guarantees that best suit their needs. Regardless of the reasons, changes in pricings are frequent, and are increasing their complexity. Therefore, for those responsible for the development and operation of SaaS, it would be ideal to minimize the time required to transfer changes in SaaS pricing to the software and underlying infrastructure, without compromising quality and reliability. We call this pricing-driven self-adaptation, and this work explores the extent of industry support for it. First, after analyzing 240 pricings from 37 different SaaS over seven years, we reveal a trend of exponentially increasing complexity, mainly driven by a sustained increase in the number of add-ons. Second, acknowledging feature toggling as a promising technique for enabling pricing-driven self-adaptation, we evaluate 18 existing solutions to assess their suitability. However, results reveal a gap between their capabilities and the requirements imposed by the growing complexity of pricings. In light of these results, establishing a standard for pricing serialization and advancing automation in pricing-driven self-adaptation emerge as key steps toward reducing the time-to-market of SaaS pricing updates.
{"title":"Trends in Industry Support for Pricing-Driven DevOps in SaaS","authors":"Alejandro García-Fernández;José Antonio Parejo;Francisco Javier Cavero;Antonio Ruiz-Cortés","doi":"10.1109/TSC.2025.3634801","DOIUrl":"10.1109/TSC.2025.3634801","url":null,"abstract":"The SaaS paradigm has popularized the usage of pricings, allowing providers to offer a wide range of subscription possibilities. This creates a vast configuration space for customers, enabling them to choose the features, limits and guarantees that best suit their needs. Regardless of the reasons, changes in pricings are frequent, and are increasing their complexity. Therefore, for those responsible for the development and operation of SaaS, it would be ideal to minimize the time required to transfer changes in SaaS pricing to the software and underlying infrastructure, without compromising quality and reliability. We call this pricing-driven self-adaptation, and this work explores the extent of industry support for it. First, after analyzing 240 pricings from 37 different SaaS over seven years, we reveal a trend of exponentially increasing complexity, mainly driven by a sustained increase in the number of add-ons. Second, acknowledging feature toggling as a promising technique for enabling pricing-driven self-adaptation, we evaluate 18 existing solutions to assess their suitability. However, results reveal a gap between their capabilities and the requirements imposed by the growing complexity of pricings. In light of these results, establishing a standard for pricing serialization and advancing automation in pricing-driven self-adaptation emerge as key steps toward reducing the time-to-market of SaaS pricing updates.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 1","pages":"726-737"},"PeriodicalIF":5.8,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11260961","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145553453","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}
Pub Date : 2025-11-17DOI: 10.1109/tsc.2025.3633668
Zihang Su, Xiang He, Wenrui Wang, Zhongjie Wang
{"title":"Crossport: a Cloud-Edge-End Microservice Architecture for Collaborative Rendering in Metaverse Services","authors":"Zihang Su, Xiang He, Wenrui Wang, Zhongjie Wang","doi":"10.1109/tsc.2025.3633668","DOIUrl":"https://doi.org/10.1109/tsc.2025.3633668","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"50 2 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145535446","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}
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