IEEE Transactions on Services Computing最新文献_第4页

A Hybrid Optimization Framework for Age of Information Minimization in UAV-assisted MCS 无人机辅助MCS信息最小化时代的混合优化框架

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-01-10 DOI: 10.1109/tsc.2025.3528339

Yuxin Liu, Qingyong Deng, Zhiwen Zeng, Anfeng Liu, Zhetao Li

引用次数: 0

TF-DDRL: A Transformer-enhanced Distributed DRL Technique for Scheduling IoT Applications in Edge and Cloud Computing Environments TF-DDRL：一种用于在边缘和云计算环境中调度物联网应用的变压器增强分布式DRL技术

IF 8.1 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-01-10 DOI: 10.1109/tsc.2025.3528346

Zhiyu Wang, Mohammad Goudarzi, Rajkumar Buyya

引用次数: 0

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 8.1 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

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 8.1 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

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 8.1 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

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