IEEE Transactions on Services Computing最新文献_第7页

MedShield: A Fast Cryptographic Framework for Private Multi-Service Medical Diagnosis MedShield：私有多服务医疗诊断的快速加密框架

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-01-07 DOI: 10.1109/TSC.2025.3526369

Fuyi Wang;Jinzhi Ouyang;Xiaoning Liu;Lei Pan;Leo Yu Zhang;Robin Doss

{"title":"MedShield: A Fast Cryptographic Framework for Private Multi-Service Medical Diagnosis","authors":"Fuyi Wang;Jinzhi Ouyang;Xiaoning Liu;Lei Pan;Leo Yu Zhang;Robin Doss","doi":"10.1109/TSC.2025.3526369","DOIUrl":"10.1109/TSC.2025.3526369","url":null,"abstract":"The substantial progress in privacy-preserving machine learning (PPML) facilitates outsourced medical computer-aided diagnosis (MedCADx) services. However, existing PPML frameworks primarily concentrate on enhancing the efficiency of prediction services, without exploration into diverse medical services such as medical segmentation. In this article, we propose <monospace>MedShield</monospace>, a pioneering cryptographic framework for diverse MedCADx services (i.e., multi-service, including medical imaging prediction and segmentation). Based on a client-server (two-party) setting, <monospace>MedShield</monospace> efficiently protects medical records and neural network models without fully outsourcing. To execute multi-service securely and efficiently, our technical contributions include: 1) optimizing computational complexity of matrix multiplications for linear layers at the expense of free additions/subtractions; 2) introducing a secure most significant bit protocol with crypto-friendly activations to enhance the efficiency of non-linear layers; 3) presenting a novel layer for upscaling low-resolution feature maps to support multi-service scenarios in practical MedCADx. We conduct a rigorous security analysis and extensive evaluations on benchmarks (MNIST and CIFAR-10) and real medical records (breast cancer, liver disease, COVID-19, and bladder cancer) for various services. Experimental results demonstrate that <monospace>MedShield</monospace> achieves up to <inline-formula><tex-math>$2.4times$</tex-math></inline-formula>, <inline-formula><tex-math>$4.3times$</tex-math></inline-formula>, and <inline-formula><tex-math>$2times$</tex-math></inline-formula> speed up for MNIST, CIFAR-10, and medical datasets, respectively, compared with prior work when conducting prediction services. For segmentation services, <monospace>MedShield</monospace> preserves the precision of the unprotected version, showing a 1.23% accuracy improvement.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"954-968"},"PeriodicalIF":5.5,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936236","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}

引用次数: 0

Deep Reinforcement Learning for Scheduling Applications in Serverless and Serverful Hybrid Computing Environments 无服务器和有服务器混合计算环境中调度应用的深度强化学习

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-01-07 DOI: 10.1109/TSC.2024.3520864

Anupama Mampage;Shanika Karunasekera;Rajkumar Buyya

{"title":"Deep Reinforcement Learning for Scheduling Applications in Serverless and Serverful Hybrid Computing Environments","authors":"Anupama Mampage;Shanika Karunasekera;Rajkumar Buyya","doi":"10.1109/TSC.2024.3520864","DOIUrl":"10.1109/TSC.2024.3520864","url":null,"abstract":"Serverless computing has gained popularity as a novel cloud execution model for applications in recent times. Businesses constantly try to leverage this new paradigm to add value to their revenue streams. The serverless eco-system accommodates many application domains successfully. However, its inherent properties such as cold start delays and relatively high per unit charges appear as a shortcoming for certain application workloads, when compared to a traditional Virtual Machine (VM) based execution scenario. A few research works exist, that study how serverless computing could be used to mitigate the challenges in a VM based cluster environment, for certain applications. In contrast, this work proposes a generalized framework for determining which workloads are best able to reap benefits of a serverless computing environment. In essence, we present a potential hybrid scheduling solution for exploiting the benefits of both a serverless and a VM based serverful computing environment. Our proposed framework leverages the actor-critic based deep reinforcement learning architecture coupled with the proximal policy optimization technique, in determining the best scheduling decision for workload executions. Extensive experiments conducted demonstrate the effectiveness of such a solution, in terms of user cost and application performance, with improvements of up to 44% and 11% respectively.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"718-728"},"PeriodicalIF":5.5,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936237","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}

引用次数: 0

Intent-Guided Bilateral Long and Short-Term Information Mining With Contrastive Learning for Sequential Recommendation 意向引导下的双向长短期信息挖掘与序列推荐的对比学习

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-01-03 DOI: 10.1109/TSC.2024.3520868

Junhui Niu;Wei Zhou;Fengji Luo;Yihao Zhang;Jun Zeng;Junhao Wen

{"title":"Intent-Guided Bilateral Long and Short-Term Information Mining With Contrastive Learning for Sequential Recommendation","authors":"Junhui Niu;Wei Zhou;Fengji Luo;Yihao Zhang;Jun Zeng;Junhao Wen","doi":"10.1109/TSC.2024.3520868","DOIUrl":"10.1109/TSC.2024.3520868","url":null,"abstract":"The current sequential recommendation systems mainly focus on mining information related to users to make personalized recommendations. However, there are two subjects in the user historical interaction sequence: users and items. We believe that mining sequence information only from the users’ perspective is limited, ignoring effective information from the perspective of items, which is not conducive to alleviating the data sparsity problem. To explore potential links between items and use them for recommendation, we propose Intent-guided Bilateral Long and Short-Term Information Mining with Contrastive Learning for Sequential Recommendation (IBLSRec), which interpretively integrates three kinds of information mined from the sequence: user preferences, user intentions, and potential relationships between items. Specifically, we model the potential relationships between interactive items from a long-term and short-term perspective. The short-term relationship between items is regarded as noise; the long-term relationship between items is regarded as a stable common relationship and integrated with the user's personalized preferences. In addition, user intent is used to guide the modeling of user preferences to refine the representation of user preferences further. A large number of experiments on four real data sets validate the superiority of our model.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"212-225"},"PeriodicalIF":5.5,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924661","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}

引用次数: 0

2024 Reviewers List* 2024审稿人名单*

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-12-31 DOI: 10.1109/TSC.2024.3511772

引用次数: 0

Automatic Data Generation and Optimization for Digital Twin Network 数字孪生网络的自动数据生成与优化

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-12-26 DOI: 10.1109/TSC.2024.3522504

Mei Li;Cheng Zhou;Lu Lu;Yan Zhang;Tao Sun;Danyang Chen;Hongwei Yang;Zhiqiang Li

{"title":"Automatic Data Generation and Optimization for Digital Twin Network","authors":"Mei Li;Cheng Zhou;Lu Lu;Yan Zhang;Tao Sun;Danyang Chen;Hongwei Yang;Zhiqiang Li","doi":"10.1109/TSC.2024.3522504","DOIUrl":"10.1109/TSC.2024.3522504","url":null,"abstract":"With the rise of new applications such as AR/VR, cloud gaming, and vehicular networks, traditional network management solutions are no longer cost-effective. Digital Twin Network (DTN) creates a real-time virtual twin of the physical network, which improves the network's stability, security, and operational efficiency. AI models have been used to model complex network environments in DTN, whose quality mainly depends on the model architecture and data. This paper proposes an automatic data generation and optimization method for DTN called AutoOPT, which focuses on generating and optimizing data for data-driven DTN AI modeling through data-centric AI. The data generation stage generates data in small networks based on scale-independent indicators, which helps DTN AI models generalize to large networks. The data optimization stage automatically filters out high-quality data through seed sample selection and incremental optimization, which helps enhance the accuracy and generalization of DTN AI models. We apply AutoOPT to the DTN performance modeling scenario and evaluate it on simulated and real network data. The experimental results show that AutoOPT is more cost-efficient than state-of-the-art solutions while achieving similar results, and it can automatically select high-quality data for scenarios that require data quality improvement.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"85-97"},"PeriodicalIF":5.5,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888936","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}

引用次数: 0

Towards Close-to-Zero Runtime Collection Overhead: Raft-Based Anomaly Diagnosis on System Faults for Distributed Storage System 趋近于零运行时采集开销：基于raft的分布式存储系统故障异常诊断

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-12-25 DOI: 10.1109/TSC.2024.3521675

Lingzhe Zhang;Tong Jia;Mengxi Jia;Hongyi Liu;Yong Yang;Zhonghai Wu;Ying Li

{"title":"Towards Close-to-Zero Runtime Collection Overhead: Raft-Based Anomaly Diagnosis on System Faults for Distributed Storage System","authors":"Lingzhe Zhang;Tong Jia;Mengxi Jia;Hongyi Liu;Yong Yang;Zhonghai Wu;Ying Li","doi":"10.1109/TSC.2024.3521675","DOIUrl":"10.1109/TSC.2024.3521675","url":null,"abstract":"Distributed storage systems are fundamental infrastructures of today’s large-scale software systems such as cloud systems. Diagnosing anomalies in distributed storage systems is essential for maintaining software availability. Existing anomaly diagnosis approaches mainly rely on the run-time data including monitoring data and application logs. However, collecting and analyzing the run-time data requires huge computing, storage, and management costs. Typically, more fine-grained run-time data can reveal more symptoms of anomalies, but on the contrary, requires more computing, storage, and management costs. As a result, solving the anomaly diagnosis problem is a balancing between the quality of run-time data and system overhead or cost. In this paper, we take into account both data quality and system overhead or cost by introducing a new type of run-time data-Raft logs. Raft logs are naturally produced by distributed storage systems and collecting raft logs will not bring any extra system overhead. To verify the ability of Raft logs in reflecting anomalies, we conduct a comprehensive study on the interconnection between the anomalies and Raft logs. Based on the study, we propose an effective <bold>Raft-<bold>Based <bold>Anomaly <bold>Diagnosis approach named <bold>RBAD. For evaluation, we expose the first open-sourced comprehensive dataset with multiple runtime data containing both Raft logs, application logs and monitoring data. Experiments based on this dataset demonstrate RBAD’s superiority, outperforming monitoring-based methods by 15.38% and log-based methods by 53.10%.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"1054-1067"},"PeriodicalIF":5.5,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888213","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}

引用次数: 0

ARAScaler: Adaptive Resource Autoscaling Scheme Using ETimeMixer for Efficient Cloud-Native Computing ARAScaler：使用ETimeMixer实现高效云原生计算的自适应资源自动缩放方案

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-12-25 DOI: 10.1109/TSC.2024.3522815

Byeonghui Jeong;Young-Sik Jeong

{"title":"ARAScaler: Adaptive Resource Autoscaling Scheme Using ETimeMixer for Efficient Cloud-Native Computing","authors":"Byeonghui Jeong;Young-Sik Jeong","doi":"10.1109/TSC.2024.3522815","DOIUrl":"10.1109/TSC.2024.3522815","url":null,"abstract":"The container resource autoscaling techniques offer scalability and continuity for microservices operating in cloud-native computing environments. However, they manage resources inefficiently, causing resource waste and overload under complex workload patterns. In addition, these techniques fail to prevent oscillations caused by dynamic workloads, increasing the operational complexity. Therefore, we propose an adaptive resource autoscaling scheme (ARAScaler) to ensure the stability and resource efficiency of microservices with minimal scaling events. ARAScaler predicts future workloads using enhanced TimeMixer (ETimeMixer) applied with the convolutional method. Additionally, ARAScaler segments the predicted workload to identify burst, nonburst, dynamic, and static states and scales by calculating the optimal number of container instances for each identified state. The offline simulation results using seven cloud-workload trace datasets demonstrate the high prediction accuracy of ETimeMixer and the superior scaling performance of ARAScaler. The ARAScaler achieved a resource utilization of approximately 70% or higher with few updates and recorded the fewest resource overload instances compared to existing container resource autoscaling techniques.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"72-84"},"PeriodicalIF":5.5,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888210","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}

引用次数: 0

Serv-HU: Service Hand-off for UAV-as-a-Service Serv-HU：无人机即服务的服务切换

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-12-24 DOI: 10.1109/TSC.2024.3521684

Arijit Roy;Veera Manikantha Rayudu Tummala;Vinay Yadam

{"title":"Serv-HU: Service Hand-off for UAV-as-a-Service","authors":"Arijit Roy;Veera Manikantha Rayudu Tummala;Vinay Yadam","doi":"10.1109/TSC.2024.3521684","DOIUrl":"10.1109/TSC.2024.3521684","url":null,"abstract":"In this work, we propose a UAV Service Hand-off scheme (Serv-HU) for the UAV-as-a-Service (UaaS) platform to provide seamless UAV services to the end-users. Traditionally, a service provider of a UaaS platform serves a limited application area due to the unavailability of adequate resources such as UAVs. Failing to deliver the service by the service providers for the requested entire application area by the end-user affects the reputation of the service providers. Consequently, the service delivery for a partial application area impacts the overall business, which is unacceptable for a Service-Oriented Architecture. To address this issue, we design a service hand-off scheme that enables the service providers to serve the entire requested application area by the end users with the help of other available service providers. We consider the presence of two types of service providers – Primary (PSP) and Secondary (SSP) in a UaaS platform. We apply a two-stage approach for the UAV service delivery to the end-users. In the first stage, a PSP optimally selects the SSPs for serving the uncovered application area by the PSP. The end-users request the service from the PSP, and on failing to provide the service for the entire application area, the PSP makes the service available from the optimally selected SSPs. In the second stage, we design an optimal pricing strategy that helps in determining the price charged to the end-users considering the involvement of PSPs and SSPs. We apply the Lagrangian multiplier method and Karush-Kuhn-Tucker (KKT) conditions to achieve the outcomes of these two stages. The simulation results depict that the charged price is reduced by <inline-formula><tex-math>$10.3 - 12.7%$</tex-math></inline-formula> while we apply the optimal SSP selection strategy as compared to the random selection of SSPs.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"414-426"},"PeriodicalIF":5.5,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884229","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}

引用次数: 0

Prediction of Heterogeneous Device Task Runtime Based on Edge Server-Oriented Deep Neuro-Fuzzy System 基于边缘服务器的深度神经模糊系统异构设备任务运行时预测

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-12-23 DOI: 10.1109/TSC.2024.3520869

Haijie Wu;Weiwei Lin;Wangbo Shen;Xiumin Wang;C. L. Philip Chen;Keqin Li

{"title":"Prediction of Heterogeneous Device Task Runtime Based on Edge Server-Oriented Deep Neuro-Fuzzy System","authors":"Haijie Wu;Weiwei Lin;Wangbo Shen;Xiumin Wang;C. L. Philip Chen;Keqin Li","doi":"10.1109/TSC.2024.3520869","DOIUrl":"10.1109/TSC.2024.3520869","url":null,"abstract":"Predicting the runtime of tasks is of great significance as it can help users better understand the future runtime consumption of the tasks and make decisions for their heterogeneous devices, or be applied to task scheduling. Learning features from user task history data for predicting task runtime is a mainstream method. However, this method faces many challenges when applied to edge intelligence. In the Big Data era, user devices and data features are constantly evolving, necessitating frequent model retrains. Meanwhile, the noisy data from these devices requires robust methods for valuable insight extraction. In this paper, we propose an edge server-oriented deep neuro-fuzzy system (ESODNFS) that can be trained and inferred on edge servers, for providing users with task runtime prediction services. We divided the dataset and trained it on multiple improved adaptive-network-based fuzzy inference system units (ANFISU), and finally conducted joint training on a deep neural network (DNN). By partitioning the dataset, we reduced the number of parameters for each ANFISU, and at the same time, multiple units can be trained in parallel, supporting fast training and iteration. Additionally, the application of fuzzy inference can effectively learn the features in noisy data and make accurate predictions. The experimental results show that ESODNFS can accurately predict the runtime of real tasks. Compared with other DNN and DNFS, it can achieve good prediction results while reducing training time by over 35%.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"372-384"},"PeriodicalIF":5.5,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879581","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}

引用次数: 0

VPFLI: Verifiable Privacy-Preserving Federated Learning With Irregular Users Based on Single Server VPFLI：基于单服务器的不规则用户可验证隐私保护联邦学习

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2024-12-23 DOI: 10.1109/TSC.2024.3520867

Yanli Ren;Yerong Li;Guorui Feng;Xinpeng Zhang

{"title":"VPFLI: Verifiable Privacy-Preserving Federated Learning With Irregular Users Based on Single Server","authors":"Yanli Ren;Yerong Li;Guorui Feng;Xinpeng Zhang","doi":"10.1109/TSC.2024.3520867","DOIUrl":"10.1109/TSC.2024.3520867","url":null,"abstract":"Federated learning (FL) is widely used in neural network-based deep learning, which allows multiple users to jointly train a model without disclosing their data. However, the data quality of the users is not uniform, and some users with poor computing ability and outdated equipments called irregular ones may collect low-quality data and thus reduce the accuracy of the global model. In addition, the untrusted server may return wrong aggregation results to cheat the users. To solve these problems, we propose a verifiable privacy-preserving FL protocol with irregular users (VPFLI) based on single server. The protocol is privacy-preserving for the untrusted server and it is proved secure based on drop-tolerant homomorphic encryption. For low-quality datasets, their proportion would be decreased in the aggregation results in order to ensure the accuracy of the global model. Also, the aggregation results can be effectively verified by the users based on linear homomorphic hash. Moreover, VPFLI is proposed based on single server, which is more applicable in reality compare with the previous ones based on two non-colluding servers. The experiments show that VPFLI improves the accuracy of the model from 83.5% to 91.5% based on MNIST dataset compared to the traditional FL protocols.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"1124-1136"},"PeriodicalIF":5.5,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879963","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}

引用次数: 0