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}
Pub Date : 2025-11-14DOI: 10.1109/TSC.2025.3632836
Taochun Wang;Leilei Shen;Fulong Chen;Kuide Wang;Chuanxin Zhao;Yonglong Luo
With the exponential growth of mobile devices, Mobile Crowdsensing (MCS) has emerged as a new paradigm for various types of tasks. However, most existing studies focus primarily on the utility of either buyers or sellers, with limited exploration of the task types for buyers and the user types for sellers. Therefore, this paper investigates the incentive mechanisms for different types of sellers under varying task types. In this study, we classify sellers into two groups: teams and individuals, and classify buyers’ tasks into two categories: simple tasks and complex tasks. Considering the characteristics of different task types and seller groups, we design two auction schemes: the Two-Stage Auction Scheme for Simple Tasks (TDAS-S) and the Two-Stage Auction Scheme for Complex Tasks (TDAS-C). In the first stage, we design a seller auction model based on the Stackelberg game to ensure the maximization of the seller’s utility. In the second stage, we design an auction model for buyers, utilizing a variant of the Vickrey Auction and marginal contribution theory to pay the selected sellers in both TDAS-S and TDAS-C, ensuring the maximization of the buyer’s utility. We further prove that the proposed scheme satisfies properties such as individual rationality, truthfulness, and budget balance. Finally, through extensive experiments on real-world datasets, we demonstrate that our scheme effectively balances the utility conflicts between buyers and sellers, allowing each party to maximize their respective utilities.
{"title":"Two-Stage Auctions Based on Different Seller Types in Crowdsensing","authors":"Taochun Wang;Leilei Shen;Fulong Chen;Kuide Wang;Chuanxin Zhao;Yonglong Luo","doi":"10.1109/TSC.2025.3632836","DOIUrl":"10.1109/TSC.2025.3632836","url":null,"abstract":"With the exponential growth of mobile devices, Mobile Crowdsensing (MCS) has emerged as a new paradigm for various types of tasks. However, most existing studies focus primarily on the utility of either buyers or sellers, with limited exploration of the task types for buyers and the user types for sellers. Therefore, this paper investigates the incentive mechanisms for different types of sellers under varying task types. In this study, we classify sellers into two groups: teams and individuals, and classify buyers’ tasks into two categories: simple tasks and complex tasks. Considering the characteristics of different task types and seller groups, we design two auction schemes: the Two-Stage Auction Scheme for Simple Tasks (TDAS-S) and the Two-Stage Auction Scheme for Complex Tasks (TDAS-C). In the first stage, we design a seller auction model based on the Stackelberg game to ensure the maximization of the seller’s utility. In the second stage, we design an auction model for buyers, utilizing a variant of the Vickrey Auction and marginal contribution theory to pay the selected sellers in both TDAS-S and TDAS-C, ensuring the maximization of the buyer’s utility. We further prove that the proposed scheme satisfies properties such as individual rationality, truthfulness, and budget balance. Finally, through extensive experiments on real-world datasets, we demonstrate that our scheme effectively balances the utility conflicts between buyers and sellers, allowing each party to maximize their respective utilities.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 1","pages":"738-751"},"PeriodicalIF":5.8,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145515608","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 : 2025-11-13DOI: 10.1109/TSC.2025.3631913
Zhijing Li;Jianbo Yu;Zhijun Huang;Yusheng Huang
As the landscape of industry and commerce continues to evolve, it presents increasing challenges for traditional operation and maintenance of services. These challenges arise from the need to adapt to dynamic market conditions, integrate complex systems and technologies, ensure uninterrupted service delivery, manage large volumes of data, and meet ever-growing customer expectations. Identifying the root cause of a failure is a crucial aspect of day-to-day operation and maintenance. With an accurate and prompt diagnosis, it becomes possible to take timely action and address the underlying issue at its core. Research on root cause analysis has been active in recent years, as it is recognized as a potential solution for effectively managing complex system states. This survey delves into the current state of research on root cause analysis, investigates recent research trends, and presents the commonly available public datasets. We organize studies along two orthogonal axes—application scenarios (cloud services, microservices, industrial systems) and input-data types (logs, traces, metrics, reports)—and synthesize algorithmic families, hybrid/LLM approaches, evaluation metrics, datasets, and tooling. To our knowledge, this is the first survey to classify RCA methods by both scenario and input-data perspectives while providing a consolidated inventory of datasets and tools, offering a roadmap for researchers.
{"title":"Surveying Root Cause Analysis Techniques: A Comprehensive Review of Aspects for Multi-Service Applications","authors":"Zhijing Li;Jianbo Yu;Zhijun Huang;Yusheng Huang","doi":"10.1109/TSC.2025.3631913","DOIUrl":"10.1109/TSC.2025.3631913","url":null,"abstract":"As the landscape of industry and commerce continues to evolve, it presents increasing challenges for traditional operation and maintenance of services. These challenges arise from the need to adapt to dynamic market conditions, integrate complex systems and technologies, ensure uninterrupted service delivery, manage large volumes of data, and meet ever-growing customer expectations. Identifying the root cause of a failure is a crucial aspect of day-to-day operation and maintenance. With an accurate and prompt diagnosis, it becomes possible to take timely action and address the underlying issue at its core. Research on root cause analysis has been active in recent years, as it is recognized as a potential solution for effectively managing complex system states. This survey delves into the current state of research on root cause analysis, investigates recent research trends, and presents the commonly available public datasets. We organize studies along two orthogonal axes—application scenarios (cloud services, microservices, industrial systems) and input-data types (logs, traces, metrics, reports)—and synthesize algorithmic families, hybrid/LLM approaches, evaluation metrics, datasets, and tooling. To our knowledge, this is the first survey to classify RCA methods by both scenario and input-data perspectives while providing a consolidated inventory of datasets and tools, offering a roadmap for researchers.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"19 1","pages":"808-825"},"PeriodicalIF":5.8,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11245222","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145509594","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}
In the rapidly evolving landscape of cloud computing, serverless architectures offer a paradigm shift towards fine-grained function deployment and meticulous resource auto-scaling. Despite its growing popularity, existing systems often struggle to ensure the stability of function execution due to frequent cold starts and high concurrency demands. Our observations reveal a critical issue where a few hotspot functions excessively create new containers, resulting in substantial response latency fluctuations. To address this challenge, we propose Eunomia, a SLO-aware (Service Level Objective-aware) serverless framework. Eunomia introduces an optimized Poisson model with dynamic, sliding windows to accurately capture the arrival patterns of hotspot functions. Based on the optimized Poisson model, it proposes a queuing-based delay execution approach to mitigate initialization overhead by promoting instance reuse. Additionally, Eunomia designs flexible instance orchestration, providing dedicated concurrency pools for hotspot functions and dynamically adjusting the number of active instances. Experimental results demonstrate that Eunomia ensures 97% tail latency under a 100 ms response latency SLO, and outperforms the second-best baseline by 46% when memory is limited.
{"title":"SLO-Aware Instance Management With Queuing-Based Delay Execution","authors":"Xinquan Cai;Kai Yan;Yili Gong;Chuang Hu;Dazhao Cheng","doi":"10.1109/TSC.2025.3632416","DOIUrl":"10.1109/TSC.2025.3632416","url":null,"abstract":"In the rapidly evolving landscape of cloud computing, serverless architectures offer a paradigm shift towards fine-grained function deployment and meticulous resource auto-scaling. Despite its growing popularity, existing systems often struggle to ensure the stability of function execution due to frequent cold starts and high concurrency demands. Our observations reveal a critical issue where a few hotspot functions excessively create new containers, resulting in substantial response latency fluctuations. To address this challenge, we propose Eunomia, a SLO-aware (Service Level Objective-aware) serverless framework. Eunomia introduces an optimized Poisson model with dynamic, sliding windows to accurately capture the arrival patterns of hotspot functions. Based on the optimized Poisson model, it proposes a queuing-based delay execution approach to mitigate initialization overhead by promoting instance reuse. Additionally, Eunomia designs flexible instance orchestration, providing dedicated concurrency pools for hotspot functions and dynamically adjusting the number of active instances. Experimental results demonstrate that Eunomia ensures 97% tail latency under a 100 ms response latency SLO, and outperforms the second-best baseline by 46% when memory is limited.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 6","pages":"4167-4180"},"PeriodicalIF":5.8,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145509591","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 : 2025-11-11DOI: 10.1109/tsc.2025.3631804
Xingguo Jiang, Hong Luo, Yan Sun, Sajal K. Das
{"title":"CIRCA: A Framework for Collaborative Identification of Root Cause Analysis in IoT Microservices","authors":"Xingguo Jiang, Hong Luo, Yan Sun, Sajal K. Das","doi":"10.1109/tsc.2025.3631804","DOIUrl":"https://doi.org/10.1109/tsc.2025.3631804","url":null,"abstract":"","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 1","pages":""},"PeriodicalIF":8.1,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145491859","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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