IEEE Transactions on Services Computing最新文献_第6页

Lightweight and Privacy-Preserving Reconfigurable Authentication Scheme for IoT Devices

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-01-29 DOI: 10.1109/TSC.2025.3536314

Prosanta Gope;Fei Hongming;Biplab Sikdar

{"title":"Lightweight and Privacy-Preserving Reconfigurable Authentication Scheme for IoT Devices","authors":"Prosanta Gope;Fei Hongming;Biplab Sikdar","doi":"10.1109/TSC.2025.3536314","DOIUrl":"10.1109/TSC.2025.3536314","url":null,"abstract":"The Internet of Things (IoT) has revolutionized connectivity by enabling a large number of devices to autonomously exchange real-time data over the Internet. However, IoT devices used in public spaces are vulnerable to physical and cloning attacks. To address this issue, researchers have introduced the concept of physical-unclonable functions (PUFs) to enhance security in IoT applications. While PUF-based security solutions typically rely on static challenge-response behavior, many practical applications require dynamic or reconfigurable PUFs. For instance, PUF-based key storage may require updating or revoking secrets, and protection against modeling attacks, where an attacker can derive a PUF model from a set of challenge-response pairs (CRPs) using learning capabilities. In this paper, we introduce LR-OPUF, a reconfigurable one-time PUF, and propose a lightweight and privacy-preserving authentication scheme based on this LR-OPUF foundation. One notable feature of our authentication scheme is that it enables a device to prove its legitimacy to a semi-honest verifier without disclosing the CRPs. Through security and performance analyses, we demonstrate that our approach not only ensures vital security aspects but also exhibits high computational efficiency.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"912-925"},"PeriodicalIF":5.5,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143056297","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

Large-Scale Service Mesh Orchestration With Probabilistic Routing in Cloud Data Centers 云数据中心中具有概率路由的大规模服务网格编排

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-01-20 DOI: 10.1109/TSC.2025.3526373

Kai Peng;Yi Hu;Haonan Ding;Haoxuan Chen;Liangyuan Wang;Chao Cai;Menglan Hu

{"title":"Large-Scale Service Mesh Orchestration With Probabilistic Routing in Cloud Data Centers","authors":"Kai Peng;Yi Hu;Haonan Ding;Haoxuan Chen;Liangyuan Wang;Chao Cai;Menglan Hu","doi":"10.1109/TSC.2025.3526373","DOIUrl":"10.1109/TSC.2025.3526373","url":null,"abstract":"Service mesh architectures are emerging as a promising microservice paradigm for developing online cloud applications. However, in large-scale microservice scenarios, frequent service communications, intricate call dependencies, and stringent latency requirements bring great pressure to efficient service mesh orchestration. In this case, the problems of service deployment and request routing based on service mesh architectures are tightly-coupled and interdependent, and cannot be effectively optimized individually, enlarging the difficulty for collaborative orchestration. When microservice multiplexing, parallel dependencies, and multi-instance modeling are considered, the difficulty is further aggravated. Nonetheless, most existing work failed to propose appropriate models and methods for the above challenges. Therefore, this article studies the large-scale service mesh orchestration with probabilistic routing and constrained bandwidths for parallel call graphs. We leverage the open Jackson queuing network theory to capture crucial microservices and analyze request processing, queuing, and communication latency for massive user requests in a fine-grained way. Then, this article proposes an efficient three-stage heuristic, which achieves elegant multi-instance consolidation and probabilistic multi-queue routing to reduce response latency and cost. We also provide the algorithm complexity and mathematical analysis of the performance. Finally, extensive trace-driven experiments are performed to validate the superiority of our proposed algorithm over other baselines.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"868-882"},"PeriodicalIF":5.5,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991505","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

Intelligent Transaction Generation Control for Permissioned Blockchain-Based Services 基于许可区块链服务的智能交易生成控制

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-01-20 DOI: 10.1109/TSC.2025.3528318

Dongsun Kim;Sinwoong Yun;Sungho Lee;Jemin Lee;Dusit Niyato

{"title":"Intelligent Transaction Generation Control for Permissioned Blockchain-Based Services","authors":"Dongsun Kim;Sinwoong Yun;Sungho Lee;Jemin Lee;Dusit Niyato","doi":"10.1109/TSC.2025.3528318","DOIUrl":"10.1109/TSC.2025.3528318","url":null,"abstract":"Since the permissioned blockchain technology has been proposed to ensure data integrity in distributed systems, the low throughput and high latency have been recognized as major issues. In some applications, the data, available later than allowed time, can be useless, so the effective throughput is newly considered, defined as the average number of transactions per second, committed within the required latencies. For maximizing the effective throughput, we propose a novel intelligent transaction generation control (i-TGC) method to determine the transaction generation for each client. To improve performance in the dynamic environment of blockchain services based on real-time information, we employ the reinforcement learning (RL) for the i-TGC algorithm. Our experiment results show the i-TGC outperforms the probabilistic transaction generation control (p-TGC), which generates transactions randomly with the optimal probability that maximizes the effective throughput. We also verify the performance of the i-TGC for various environments with different block sizes, block generation timeout, traffic patterns, and the number of clients. The i-TGC can be a way to accelerate the usage of the permissioned blockchain for latency-sensitive services.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"828-838"},"PeriodicalIF":5.5,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991503","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

Federated Contrastive Learning for Cross-Domain Recommendation 跨领域推荐的联邦对比学习

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-01-15 DOI: 10.1109/TSC.2025.3528325

Qingren Wang;Yuchuan Zhao;Yi Zhang;Yiwen Zhang;Shuiguang Deng;Yun Yang

{"title":"Federated Contrastive Learning for Cross-Domain Recommendation","authors":"Qingren Wang;Yuchuan Zhao;Yi Zhang;Yiwen Zhang;Shuiguang Deng;Yun Yang","doi":"10.1109/TSC.2025.3528325","DOIUrl":"10.1109/TSC.2025.3528325","url":null,"abstract":"Conventional cross-domain recommendation models, which require centrally collecting varieties of original data from users, usually meet a challenge that users are reluctant to provide their real feedbacks because of privacy concerns. Thus, federated learning is incorporated into cross-domain recommendation, since it aggregates parameters of local models trained on user sides to train a global recommendation model, instead of centralized data collection. However, the deviations between the global model and local ones, which are caused by users’ data with non-independent and identical distributions, significantly challenge existing federated learning-based models in terms of alleviating data sparsity and cold-start problems. This article proposes a novel end-to-end federated contrastive learning-based model towards cross-domain recommendation, namely <inline-formula><tex-math>${{Fed-CLR}}$</tex-math></inline-formula>. It first uses an inference model to characterize interaction distributions of users in source domain(s), then reconstructs historical interactions of users in target domain(s) with a generative model, and finally performs federated contrastive learning at model level (including inner-model and inter-model) to help reduce deviations between the global model and local ones. Particularly, a constraint mechanism, namely <inline-formula><tex-math>${{Con-Mec}}$</tex-math></inline-formula>, is proposed to achieve consistency reinforcement from the aspect of inner- and inter-models. The experimental results on three real-world datasets not only show that <inline-formula><tex-math>${{Fed-CLR}}$</tex-math></inline-formula> outperforms the state-of-the-art peers, but also demonstrate that <inline-formula><tex-math>${{Fed-CLR}}$</tex-math></inline-formula> achieves a faster convergence speed than classical federated learning-based models.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"812-827"},"PeriodicalIF":5.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142986193","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

Availability and Reliability of Core Networks (4G/5G) From a Deployment Standpoint 从部署角度看核心网（4G/5G）的可用性和可靠性

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-01-10 DOI: 10.1109/TSC.2025.3528332

Priyatosh Mandal

引用次数: 0

A Customized Genetic Algorithm for SLA-Aware Service Provisioning in Infrastructure-Less Vehicular Cloud Networks 面向无基础设施车载云网络sla感知服务提供的自定义遗传算法

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-01-10 DOI: 10.1109/TSC.2025.3528317

Farhoud Jafari Kaleibar;Marc St-Hilaire;Masoud Barati

{"title":"A Customized Genetic Algorithm for SLA-Aware Service Provisioning in Infrastructure-Less Vehicular Cloud Networks","authors":"Farhoud Jafari Kaleibar;Marc St-Hilaire;Masoud Barati","doi":"10.1109/TSC.2025.3528317","DOIUrl":"10.1109/TSC.2025.3528317","url":null,"abstract":"Vehicular Ad-hoc Networks (VANETs) and in-vehicle networks offer complementary perspectives on Intelligent Transportation Systems (ITS), enabling communication between vehicles and within individual vehicles, respectively. While VANETs focus on vehicle-to-vehicle communication, the growing demand for dynamic resource sharing and data processing across a fleet of vehicles highlights the need for Vehicular Cloud Networks (VCNs). VCNs, despite their lack of fixed infrastructure and the continuous mobility of vehicles, provide a promising solution for improving resource management and data sharing, making them critical for achieving efficient Service Level Agreements (SLAs) in infrastructure-less environments. This article addresses these challenges by employing a hierarchical clustering technique and proposing a novel mathematical formulation for resource provisioning in infrastructure-less vehicular clouds. The formulation considers diverse criteria, including provider and requester mobility, data volume, and service delay tolerance, to ensure SLA adherence. A customized genetic algorithm is used to solve the maximization problem, incorporating a grouping mechanism for efficient problem solving. Simulations using the NS2 network simulator and the IBM CPLEX optimization tool validate the feasibility of the proposed approach and demonstrate its superior performance compared to the other methods.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"499-512"},"PeriodicalIF":5.5,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961322","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

LogNotion: Highlighting Massive Logs to Assist Human Reading and Decision Making logconcept：突出显示大量日志以帮助人类阅读和决策

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-01-10 DOI: 10.1109/TSC.2025.3528327

Guojun Chu;Jingyu Wang;Tao Sun;Qi Qi;Haifeng Sun;Zirui Zhuang;Jianxin Liao

{"title":"LogNotion: Highlighting Massive Logs to Assist Human Reading and Decision Making","authors":"Guojun Chu;Jingyu Wang;Tao Sun;Qi Qi;Haifeng Sun;Zirui Zhuang;Jianxin Liao","doi":"10.1109/TSC.2025.3528327","DOIUrl":"10.1109/TSC.2025.3528327","url":null,"abstract":"Massive logs contain crucial information about the working status of software systems, which contributes to anomaly detection and troubleshooting. For engineers, it is a laborious task to manually inspect raw logs to know the system running status, and therefore an automated log summarization tool can be helpful. However, due to the specificity of logs in terms of grammar, vocabulary and semantics, existing natural language-based methods cannot perform well in log analysis. To address these issues, we propose LogNotion, a general log summarization framework that highlights the log messages to assist human reading and decision making. We first explore the role played by triplets in log analysis, and propose a triplet extraction method based on sequence tagging and component alignment, in which the specificity of logs is fully taken into account. Then, we propose an unsupervised log summarization method to extract both regular and noteworthy information based on triplets. Comprehensive experiments are conducted on seven real-world log datasets and the results show that LogNotion improves the average ROUGE-1 by 0.26, recall by 0.12, and compression ratio by 2.13%, compared to state-of-the-art log summarization tools. The helpfulness, readability and generalizability are also verified through human evaluation and cross-dataset tests.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"940-953"},"PeriodicalIF":5.5,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961318","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 Mobility-Aware Digital Twin Migrations in Edge Computing 边缘计算中移动感知数字孪生迁移的深度强化学习

IF 5.5 2区计算机科学

IEEE Transactions on Services Computing Pub Date : 2025-01-10 DOI: 10.1109/TSC.2025.3528331

Yuncan Zhang;Luying Wang;Weifa Liang

{"title":"Deep Reinforcement Learning for Mobility-Aware Digital Twin Migrations in Edge Computing","authors":"Yuncan Zhang;Luying Wang;Weifa Liang","doi":"10.1109/TSC.2025.3528331","DOIUrl":"10.1109/TSC.2025.3528331","url":null,"abstract":"The past decade witnessed an explosive growth on the number of IoT devices (objects/suppliers), including portable mobile devices, autonomous vehicles, sensors and intelligence appliances. To realize the digital representations of objects, Digital Twins (DTs) are key enablers to provide real-time monitoring, behavior simulations and predictive decisions for objects. On the other hand, Mobile Edge Computing (MEC) has been envisioned as a promising paradigm to provide delay-sensitive services for mobile users (consumers) at the network edge, e.g., real-time healthcare, AR/VR, online gaming, smart cities, and so on. In this paper, we study a novel DT migration problem for high quality service provisioning in an MEC network with the mobility of both suppliers and consumers for a finite time horizon, with the aim to minimize the sum of the accumulative DT synchronization cost of all suppliers and the total service cost of all consumers requesting for different DT services. To this end, we first show that the problem is NP-hard, and formulate an integer linear programming solution to the offline version of the problem. We then develop a Deep Reinforcement Learning (DRL) algorithm for the DT migration problem, by considering the system dynamics and heterogeneity of different resource consumptions, mobility traces of both suppliers and consumers, and workloads of cloudlets. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"704-717"},"PeriodicalIF":5.5,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961320","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

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 5.5 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

{"title":"A Hybrid Optimization Framework for Age of Information Minimization in UAV-Assisted MCS","authors":"Yuxin Liu;Qingyong Deng;Zhiwen Zeng;Anfeng Liu;Zhetao Li","doi":"10.1109/TSC.2025.3528339","DOIUrl":"10.1109/TSC.2025.3528339","url":null,"abstract":"UAVs-enabled Mobile Crowdsensing (UMCS) has gained considerable attention recently, but it is challenging to meet the data collection needs of the entire city using only the UAV with limited energy. Furthermore, how to effectively minimize Age-of-Information (AoI) and ensure data quality has not been well solved in previous studies. Therefore, this paper proposes a hybrid optimization framework for AoI minimization, which recruits massive distributed workers as the main force for data collection, while the UAV acts as a data collection collaborator and is more inclined to fly to the SNs that cannot establish connections with workers, To mitigate the potential security threats incurred by dishonest workers of the MCS system, we first provide a Greedy-based Multi-worker Task Assignment (GMTA) strategy, aiming to assign more urgent data collection tasks to reliable workers under workload constraints. Then, we propose a Deep-Reinforcement-Learning-based Global AoI Minimization (DRL-GAM) strategy for the UAV path planning to find a set of optimal actions to minimize the global AoI. Based on the real dataset, our simulation experiments show that compared with traditional strategies, our DRL-GAM strategy can reduce the global AoI by an average of 6.49%<inline-formula><tex-math>$sim$</tex-math></inline-formula>68.21% in various network sizes, and is more stable for the average standard deviation is only 51.75% of other strategies.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"527-542"},"PeriodicalIF":5.5,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961319","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

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 5.5 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

{"title":"TF-DDRL: A Transformer-Enhanced Distributed DRL Technique for Scheduling IoT Applications in Edge and Cloud Computing Environments","authors":"Zhiyu Wang;Mohammad Goudarzi;Rajkumar Buyya","doi":"10.1109/TSC.2025.3528346","DOIUrl":"10.1109/TSC.2025.3528346","url":null,"abstract":"With the continuous increase of IoT applications, their effective scheduling in edge and cloud computing has become a critical challenge. The inherent dynamism and stochastic characteristics of edge and cloud computing, along with IoT applications, necessitate solutions that are highly adaptive. Currently, several centralized Deep Reinforcement Learning (DRL) techniques are adapted to address the scheduling problem. However, they require a large amount of experience and training time to reach a suitable solution. Moreover, many IoT applications contain multiple interdependent tasks, imposing additional constraints on the scheduling problem. To overcome these challenges, we propose a Transformer-enhanced Distributed DRL scheduling technique, called TF-DDRL, to adaptively schedule heterogeneous IoT applications. This technique follows the Actor-Critic architecture, scales efficiently to multiple distributed servers, and employs an off-policy correction method to stabilize the training process. In addition, Prioritized Experience Replay (PER) and Transformer techniques are introduced to reduce exploration costs and capture long-term dependencies for faster convergence. Extensive results of practical experiments show that TF-DDRL, compared to its counterparts, significantly reduces response time, energy consumption, monetary cost, and weighted cost by up to 60%, 51%, 56%, and 58%, respectively.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"1039-1053"},"PeriodicalIF":5.5,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961594","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