IEEE Transactions on Services Computing最新文献

{"title":"A Fine-Grained Regularization Scheme for Non-negative Latent Factorization of High-Dimensional and Incomplete Tensors","authors":"Hao Wu;Yan Qiao;Xin Luo","doi":"10.1109/TSC.2024.3486171","DOIUrl":"10.1109/TSC.2024.3486171","url":null,"abstract":"A Dynamically Weighted Directed Network (DWDN) fundamentally illustrates the complex interactions among massive nodes from a big-data-oriented application, like the dynamic interactions among numerous terminals in a metropolitan network management system (MNMS). A High-Dimensional and Incomplete (HDI) tensor is able to flexibly quantize it, where lots of entries are missing primarily due to the impossibility in discovering the full interactions among numerous nodes. Such an HDI tensor can be effectively represented by a Latent Factorization of Tensors (LFT) model for extracting useful knowledge like potential links from it, while existing LFT models commonly adopt general regularization schemes without considering an HDI tensor's imbalanced known data, which impairs their generality. To address this issue, this paper develops an Fine-grained Regularized Nonnegative Latent factorization of tensors (FRNL) model based on two-fold ideas: a) innovatively proposing an Swish-p-based and fine-grained regularization scheme where the regularization effects acting on individual latent feature is proportional to its related instance count for precisely representing the imbalanced distribution of an HDI tensor's known data; b) implementing the self-adaptation of the model hyper-parameters via a fuzzy controller to achieve high practicability. The convergence ability of FRNL is justified theoretically. Experimental studies on eight DWDNs emerging from a real MNMS illustrate that compared with state-of-the-art LFT models, the proposed FRNL model obtains significantly higher learning accuracy and computational efficiency in representing a DWDN.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3006-3021"},"PeriodicalIF":5.5,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142489482","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":"FREB: Participant Selection in Federated Learning With Reputation Evaluation and Blockchain","authors":"Jian An;Siyu Tang;Xiangyan Sun;Xiaolin Gui;Xin He;Feifei Wang","doi":"10.1109/TSC.2024.3486185","DOIUrl":"10.1109/TSC.2024.3486185","url":null,"abstract":"Federated Learning (FL) offers a distributed machine learning framework that enables collaborative model training across multiple data sources without the need to share raw data, thereby preserving data privacy. This framework is particularly well-suited for cross-departmental and cross-enterprise intelligent decision-making in smart manufacturing. However, challenges remain in selecting reliable participants and ensuring the secure transmission of parameters to defend against potential attacks. Malicious participants may upload low-quality data or compromise data privacy during model aggregation. To address these issues, we propose the Federated Reputation Evaluation Blockchain (FREB), which integrates a reputation evaluation mechanism with blockchain technology. By leveraging blockchain, FL tasks are executed through trusted transactions, with smart contracts ensuring transparency and accountability. In contrast to traditional contribution evaluation methods, FREB employs a multi-weight subjective logic model combined with Shapley values to assess participant reliability. Reputation scores are calculated based on factors such as activity, model contribution, stability, and data quality, guiding the selection of participants. Additionally, a PoR-based model aggregation method is implemented, and noise is added to the model parameters to protect sensitive data from potential attacks. Experimental results on real-world datasets demonstrate that FREB effectively mitigates malicious node attacks and encourages high-quality participants, while maintaining model accuracy and data privacy.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3685-3698"},"PeriodicalIF":5.5,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142489485","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":"Survey of Post-Quantum Lattice-Based Ciphertext-Policy Attribute-Based Encryption Schemes for Cloud Storage: Taxonomy, Open Issues, and Future Directions","authors":"Gudipati Sravya;Pasupuleti Syam Kumar;R. Padmavathy","doi":"10.1109/TSC.2024.3479930","DOIUrl":"10.1109/TSC.2024.3479930","url":null,"abstract":"Ciphertext Policy Attribute-Based Encryption (CP-ABE) is one of the most prevalent cryptographic primitives for realizing privacy and fine-grained access control in cloud computing. However, most of the existing CP-ABE schemes constructed using Paring-Based Cryptography (PBC) from Discrete Logarithm Problem (DLP) or Diffie Hellman Problem (DHP) assumptions are susceptible to quantum attacks, whereas CP-ABE schemes constructed using lattice-based cryptography based on Learning with Errors (LWE) and Ring-LWE (R-LWE) assumptions are quantum-safe and ensure fine-grained access control. This paper comprehensively surveys the existing Lattice-based CP-ABE (LCP-ABE) schemes based on LWE and R-LWE assumptions. Further, this paper analyzes and compares the security and performance features of existing LCP-ABE schemes. Finally, this paper identifies the open issues and future directions that need further investigation on LCP-ABE schemes.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"4540-4557"},"PeriodicalIF":5.5,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142439675","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":"Tango: Harmonious Optimization for Mixed Services in Kubernetes-Based Edge Clouds","authors":"Shihao Shen;Yicheng Feng;Mengwei Xu;Yuanming Ren;Xiaofei Wang;Victor C.M. Leung;Wenyu Wang","doi":"10.1109/TSC.2024.3479926","DOIUrl":"10.1109/TSC.2024.3479926","url":null,"abstract":"Deploying Latency-Critical (LC) services and Best-Effort (BE) services together is expected to improve resource utilization in edge clouds. However, co-locating LC and BE services on edge clouds presents unique challenges. Unlike cloud datacenters, edge clouds are heterogeneous, resource-constrained, and geographically distributed, leading to fiercer competition for resources and greater difficulty in balancing fluctuating co-located workloads. Due to the lack of consideration for the characteristics of edge environments, previous solutions designed for cloud datacenters are no longer applicable. To address these challenges, we introduce \u0000<italic>Tango</i>\u0000, a harmonious scheduling framework for \u0000<italic>Kubernetes</i>\u0000-based edge cloud systems with mixed services. \u0000<italic>Tango</i>\u0000 incorporates novel components and mechanisms for elastic resource allocation on the edge, as well as two traffic scheduling algorithms that efficiently manage distributed edge resources. \u0000<italic>Tango</i>\u0000 fosters harmony not only by supporting compatible mixed services but also by offering collaborative solutions that complement each other. Based on a non-intrusive design for \u0000<italic>Kubernetes</i>\u0000, \u0000<italic>Tango</i>\u0000 further enhances it with automatic scaling and traffic scheduling capabilities. Compared to state-of-the-art approaches, experiments on large-scale hybrid edge clouds, driven by real workload traces, show that \u0000<italic>Tango</i>\u0000 improves system resource utilization by 36.9%, QoS-guarantee satisfaction rate by 11.3%, and throughput by 47.6%.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"4354-4367"},"PeriodicalIF":5.5,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142439671","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":"Cloned Identity Detection in Social-Sensor Clouds Based on Incomplete Profiles","authors":"Ahmed Alharbi;Hai Dong;Xun Yi;Prabath Abeysekara","doi":"10.1109/TSC.2024.3479912","DOIUrl":"10.1109/TSC.2024.3479912","url":null,"abstract":"We propose a novel approach to effectively detect cloned identities of social-sensor cloud service providers (i.e. social media users) in the face of incomplete non-privacy-sensitive profile data. Named ICD-IPD, the proposed approach first extracts account pairs with similar usernames or screen names from a given set of user accounts collected from a social media. It then learns a multi-view representation associated with a given account and extracts two categories of features for every single account. These two categories of features include profile and Weighted Generalised Canonical Correlation Analysis (WGCCA)-based features that may potentially contain missing values. To counter the impact of such missing values, a missing value imputer will next impute the missing values of the aforementioned profile and WGCCA-based features. After that, the proposed approach further extracts two categories of augmented features for each account pair identified previously, namely, 1) similarity and 2) differences-based features. Finally, these features are concatenated and fed into a Light Gradient Boosting Machine classifier to detect identity cloning. We evaluated and compared the proposed approach against the existing state-of-the-art identity cloning approaches and other machine or deep learning models atop a real-world dataset. The experimental results show that the proposed approach outperforms the state-of-the-art approaches and models in terms of Precision, Recall and F1-score.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3227-3240"},"PeriodicalIF":5.5,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142439673","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":"Winning at the Starting Line: Unreliable Data Replica Selection for Edge Data Integrity Verification","authors":"Yao Zhao;Youyang Qu;Yong Xiang;Feifei Chen;Md Palash Uddin;Longxiang Gao","doi":"10.1109/TSC.2024.3479909","DOIUrl":"10.1109/TSC.2024.3479909","url":null,"abstract":"<u>M</u>\u0000obile \u0000<u>E</u>\u0000dge \u0000<u>C</u>\u0000omputing (MEC) is an emerging technology, where App vendors are allowed to cache multiple data replicas on geographically distributed edge servers to serve adjacent mobile subscribers. However, this benefit introduces an extra workload for edge servers and App vendors, as they must audit the integrity of multiple data replicas periodically considering various threats caused by distributed and dynamic MEC environments. The large-scale growth of data replicas certainly is a challenge to design more efficient \u0000<u>E</u>\u0000dge \u0000<u>D</u>\u0000ata \u0000<u>I</u>\u0000ntegrity (EDI) verification approaches. Existing solutions are mostly limited to improving efficiency by optimizing proof generation and verification methods, while the improvement is still far from satisfactory due to adopting indiscriminate inspection philosophy (checking all data replicas without discrimination). In this paper, we make the first attempt to abstract a pre-processing phase and correspondingly study the \u0000<u>U</u>\u0000nreliable data \u0000<u>R</u>\u0000eplica \u0000<u>S</u>\u0000election (URS) problem. It can be seamlessly integrated into existing EDI solutions by solving the URS problem at the start of each verification round. Such pre-selection can significantly enhance overall EDI verification efficiency by incorporating the cache service \u0000<u>Q</u>\u0000uality \u0000<u>o</u>\u0000f \u0000<u>S</u>\u0000ervice (QoS) and verification success rate, especially in scenarios with a large number of data replicas. Specifically, we first formalize the URS problem as a constrained optimization problem, and further prove its \u0000<inline-formula><tex-math>$mathcal {NP}$</tex-math></inline-formula>\u0000 -hardness. To address the problem efficiently, we transform it into an easy-to-handle form and develop a \u0000<u>P</u>\u0000riority-based approach named URS-P. Both theoretical analysis and experimental evaluation validate the effectiveness and efficiency of our proposed solution.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"4481-4493"},"PeriodicalIF":5.5,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142439674","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":"SPACE4AI-D: A Design-Time Tool for AI Applications Resource Selection in Computing Continua","authors":"Hamta Sedghani;Federica Filippini;Danilo Ardagna","doi":"10.1109/TSC.2024.3479935","DOIUrl":"10.1109/TSC.2024.3479935","url":null,"abstract":"Nowadays, Artificial Intelligence (AI) applications are becoming increasingly popular in a wide range of industries, mainly thanks to Deep Neural Networks (DNNs) that needs powerful resources. Cloud computing is a promising approach to serve AI applications thanks to its high processing power, but this sometimes results in an unacceptable latency because of long-distance communication. Vice versa, edge computing is close to where data are generated and therefore it is becoming crucial for their timely, flexible, and secure management. Given the more distributed nature of the edge and the heterogeneity of its resources, efficient component placement and resource allocation approaches become critical in orchestrating the application execution. In this paper, we formulate the resource selection and AI applications component placement problem in a computing continuum as a Mixed Integer Non-Linear Problem (MINLP), and we propose a design-time tool for its efficient solution. We first propose a Random Greedy algorithm to minimize the cost of the placement while guaranteeing some response time performance constraints. Then, we develop some heuristic methods such as Local Search, Tabu Search, Simulated Annealing and Genetic Algorithms, to improve the initial solutions provided by the Random Greedy. To evaluate our proposed approach, we designed an extensive experimental campaign, comparing the heuristics methods with one another and then the best heuristic against Best Cost Performance Constraint (BCPC) algorithm, a state-of-the-art approach. The results demonstrate that our proposed approach finds lower-cost solution than BCPC (27.6% on average) under the same time limit in large-scale systems. Finally, during the validation in a real edge system including FaaS resources our approach finds the globally optimal solution, suffering a deviation of around 12% between actual and predicted costs.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"4324-4339"},"PeriodicalIF":5.5,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142439672","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":"FedOrbit: Energy Efficient Federated Learning for Orbital Edge Computing Using Block Minifloat Arithmetic","authors":"Mohammad Reza Jabbarpour;Bahman Javadi;Philip H.W. Leong;Rodrigo N. Calheiros;David Boland","doi":"10.1109/TSC.2024.3478768","DOIUrl":"10.1109/TSC.2024.3478768","url":null,"abstract":"Low Earth Orbit (LEO) satellite constellations have diverse applications, including earth observation, communication services, navigation, and positioning. These constellations have evolved into a valuable data source; however, their use in a ground station (GS) for analysis via machine learning algorithms presents challenges due to constraints on power consumption, communication bandwidth, and onboard computing capabilities. While the combination of Federated Learning (FL) and Orbital Edge Computing has been employed to address these challenges, its heavy reliance on the GS for model aggregation and edge resource limitations remains a research challenge. This article presents FedOrbit, a novel energy-efficient and decentralised FL method to optimise communication with the GS and reduce power consumption. FedOrbit utilises reinforcement learning for cluster formation, satellite visiting patterns for master satellite selection, and block minifloat arithmetic for power reduction. Extensive performance evaluation under Walker Delta-based LEO constellation configurations and different datasets reveals that FedOrbit can maintain high accuracy while significantly reduce communication demand, power consumption and training time in comparison to state-of-the-art FL approaches. The proposed technique can also reduce the training time by 5× compared with the centralised FL approaches. In addition, the utilisation of block minifloat representation as low-precision arithmetic enhanced the energy consumption by 3.5× compared with the single-precision (FP32) format.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3657-3671"},"PeriodicalIF":5.5,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142415694","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":"Effective Graph Modeling and Contrastive Learning for Time-Aware QoS Prediction","authors":"Hao Wu;Shuting Tian;Binbin Jin;Yiji Zhao;Lei Zhang","doi":"10.1109/TSC.2024.3478836","DOIUrl":"10.1109/TSC.2024.3478836","url":null,"abstract":"Accurate and reliable service quality prediction has become a key issue in service recommendation and network measurement scenarios. However, traditional methods for time-aware QoS prediction face two main challenges: (I) data sparsity makes it difficult to estimate and recover global information from the limited known data; (II) shallow learning models struggle to represent the intricate relationships between objects, and thus suffer poor prediction performance. To this end, we propose a time-aware QoS prediction framework that combines the merits of graph modeling, graph representation learning, and contrastive learning. First, a novel graph schema is proposed to capture the complex interactions between user-service-slots. Then, a prediction model is developed leveraging a graph convolutional network to learn the node representations by aggregating feature information from neighboring nodes. Finally, a novel contrastive learning strategy is used to improve the robustness of node representation. Experimental results on a large-scale dataset demonstrated that our proposed method significantly outperforms the state-of-the-art prediction methods on response time and throughput prediction tasks.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3513-3526"},"PeriodicalIF":5.5,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142415698","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":"Dynamic Adaptive Federated Learning on Local Long-Tailed Data","authors":"Juncheng Pu;Xiaodong Fu;Hai Dong;Pengcheng Zhang;Li Liu","doi":"10.1109/TSC.2024.3478796","DOIUrl":"10.1109/TSC.2024.3478796","url":null,"abstract":"Federated learning provides privacy protection to the collaborative training of global model based on distributed private data. The local private data is often in the presence of long-tailed distribution in reality, which downgrades the performance and causes biased results. In this paper, we propose a dynamic adaptive federated learning optimization algorithm with the Grey Wolf Optimizer and Markov Chain, named FedWolf, to solve the problems of performance degradation and result bias caused by the local long-tailed data. FedWolf is launched with a set of randomly initialized parameters instead of a shared parameter employed by existing methods. Then multi-level participants are elected based on the F1 scores calculated from the uploaded parameters. A dynamic weighting strategy based on the participant level is used to adaptively update parameters without artificial control. The above parameter updating is modelled as a Markov Process. After all communication rounds are completed, the future performance (including the probability of each participant is elected as different participant level) of participants is predicted through the historical Markov states. Finally, the probability of each participant is elected as the level 1 is used as the contribution weight and the global model is obtained through dynamic contribution weight aggregating. We introduce the Gini index to evaluate the bias of classification results. Extensive experiments are conducted to validate the effectiveness of FedWolf in solving the problems of performance cracks and categorization result bias as well as the robustness of adaptive parameter updating in resisting outliers and malicious users.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"17 6","pages":"3485-3498"},"PeriodicalIF":5.5,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142415536","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}