{"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}
Pub Date : 2025-11-17DOI: 10.1109/tsc.2025.3621855
Congcong Chen, Xinyu Liu, Kaifeng Huang, Lifei Wei, Yang Shi
{"title":"Panther: A Cost-Effective Privacy-Preserving Framework for GNN Training and Inference Services in Cloud Environments","authors":"Congcong Chen, Xinyu Liu, Kaifeng Huang, Lifei Wei, Yang Shi","doi":"10.1109/tsc.2025.3621855","DOIUrl":"https://doi.org/10.1109/tsc.2025.3621855","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"124 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145535444","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 : 2025-11-17DOI: 10.1109/TSC.2025.3633988
Zhengquan Li;Zheng Song
Web services are widely used in modern software, providing diverse data and functionalities. Some data and functionalities are critical to an application’s execution and user experience, posing strict requirements on the Quality of Service (QoS) of their delivery (e.g., latency and reliability), which services often fail to meet. Previous studies show that composing homogeneous services, i.e., simultaneously invoking multiple services providing the same functionalities and returning the first response, can improve latency and reliability. However, this approach increases the workloads on cloud servers and causes additional network traffic, limiting its deployment at scale. Our empirical study reveals that services deliver varying QoS across different locations, making it possible to reduce the invocation cost by tailoring the composition strategy for different clients. In this paper, we introduce an approach that composes homogeneous services dynamically for each client, improving user-perceived QoS while minimizing the invocation costs. In particular, our approach first probes the QoS of all homogeneous services for a client, and then calculates an optimal composition strategy that satisfies the QoS requirements specified by App developers with minimum cost. We prototyped our approach as an Android library and tested it via both real-world experiments and simulations. The evaluation results show that our approach significantly improves QoS compared to invoking a single service with average best QoS across all locations (enhancing reliability to 100%, reducing average latency by 7% and tail latency by 35%) while incurring 50% less cost than static homogeneous composition, making it a useful tool for service-oriented applications.
{"title":"Tailored Homogeneous Service Composition At Runtime to Enhance User-Perceived Performance","authors":"Zhengquan Li;Zheng Song","doi":"10.1109/TSC.2025.3633988","DOIUrl":"10.1109/TSC.2025.3633988","url":null,"abstract":"Web services are widely used in modern software, providing diverse data and functionalities. Some data and functionalities are critical to an application’s execution and user experience, posing strict requirements on the Quality of Service (QoS) of their delivery (e.g., latency and reliability), which services often fail to meet. Previous studies show that composing homogeneous services, i.e., simultaneously invoking multiple services providing the same functionalities and returning the first response, can improve latency and reliability. However, this approach increases the workloads on cloud servers and causes additional network traffic, limiting its deployment at scale. Our empirical study reveals that services deliver varying QoS across different locations, making it possible to reduce the invocation cost by tailoring the composition strategy for different clients. In this paper, we introduce an approach that composes homogeneous services dynamically for each client, improving user-perceived QoS while minimizing the invocation costs. In particular, our approach first probes the QoS of all homogeneous services for a client, and then calculates an optimal composition strategy that satisfies the QoS requirements specified by App developers with minimum cost. We prototyped our approach as an Android library and tested it via both real-world experiments and simulations. The evaluation results show that our approach significantly improves QoS compared to invoking a single service with average best QoS across all locations (enhancing reliability to 100%, reducing average latency by 7% and tail latency by 35%) while incurring 50% less cost than static homogeneous composition, making it a useful tool for service-oriented applications.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 1","pages":"700-711"},"PeriodicalIF":5.8,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145535443","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 : 2025-11-14DOI: 10.1109/tsc.2025.3633175
Huan Zhou, Deng Meng, Jianmeng Guo, Peng Sun, Liang Zhao, Bin Guo, Zhiwen Yu
{"title":"Broker-assisted Computation Offloading and Resource Pricing in MEC Networks: a Two-Stage Stackelberg Game Approach","authors":"Huan Zhou, Deng Meng, Jianmeng Guo, Peng Sun, Liang Zhao, Bin Guo, Zhiwen Yu","doi":"10.1109/tsc.2025.3633175","DOIUrl":"https://doi.org/10.1109/tsc.2025.3633175","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"6 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145515607","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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