Zhixing Lu;Laurence T. Yang;Azreen Azman;Shunli Zhang;Fang Zhou
{"title":"Tensor-Based Factorial Hidden Markov Model for Cyber-Physical-Social Services","authors":"Zhixing Lu;Laurence T. Yang;Azreen Azman;Shunli Zhang;Fang Zhou","doi":"10.1109/TSC.2025.3565382","DOIUrl":"10.1109/TSC.2025.3565382","url":null,"abstract":"With the rapid development and widespread application of information, computer, and communication technologies, Cyber-Physical-Social Systems (CPSS) have gained increasing importance and attention. To enable intelligent applications and provide better services for CPSS users, efficient data analytical models are crucial. This paper presents a novel data analytic framework for CPSS services. First, a Tensor-Based Factorial Hidden Markov Model (T-FHMM) is introduced to comprehensively analyze multi-user activity features, enhancing CPSS activity analytics. A tensor-based Forward-Backward algorithm is then designed for T-FHMM to efficiently perform evaluation tasks using multiple probabilistic computing micro-services. Additionally, a tensor-based Baum-Welch algorithm is developed to accurately learn model parameters via parameter optimization micro-services. Furthermore, a tensor-based Viterbi algorithm is implemented with specific micro-services to improve prediction tasks. Finally, the comprehensive performance of the proposed model and algorithms is validated on three open datasets through self-comparison and other-comparison. Experimental results demonstrate that the proposed method outperforms compared methods in terms of accuracy, precision, recall, and F1-score.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1825-1837"},"PeriodicalIF":5.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889728","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":"MSCCL: A Framework for Enhancing Mashup Service Clustering With Contrastive Learning","authors":"Qiang Hu;Haoquan Qi;Shengzhi Du;Pengwei Wang","doi":"10.1109/TSC.2025.3565389","DOIUrl":"10.1109/TSC.2025.3565389","url":null,"abstract":"Obtaining high-quality service function vectors and aggregating neighborhood features in service association graph are prevalent methods for Mashup service clustering. However, existing methods often focus on enhancing the service functional feature extraction while overlooking distinctions among different services when creating service function vectors. Additionally, neighborhood feature aggregation is typically considered within a single association graph, lacking contrast optimization of different association features. To address these challenges, we propose a novel framework, MSCCL (Mashup Service Clustering with Contrastive Learning). MSCCL consists of two core components: a service function vector generation module and a neighborhood feature aggregation module. Contrastive learning is employed to enhance vector quality and optimize feature aggregation in both modules. We present a service clustering method within MSSCL that combines techniques from BERT (Bidirectional Encoder Representations from Transformers) and GAT (Graph Attention Networks). Compared to state-of-the-art methods, this approach reduces DBI by 2.03% to 12.58%, while enhancing SC, NMI, and Purity by 2.24% to 15.47%, 3.34% to 11.39%, and 2.58% to 13.65%, respectively. Furthermore, the experiments demonstrate that the popular models for service function vector generation and neighborhood feature aggregation can all be integrated into MSSCL. After being integrated into MSSCL, the clustering performance of these models was significantly improved, highlighting the effectiveness and generalizability of MSSCL.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1588-1601"},"PeriodicalIF":5.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889743","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":"ARRQP: Anomaly Resilient Real-Time QoS Prediction Framework With Graph Convolution","authors":"Suraj Kumar;Soumi Chattopadhyay","doi":"10.1109/TSC.2025.3565376","DOIUrl":"10.1109/TSC.2025.3565376","url":null,"abstract":"In the realm of modern service-oriented architecture, ensuring Quality of Service (QoS) is of paramount importance. The ability to predict QoS values in advance empowers users to make informed decisions, ensuring that the chosen service aligns with their expectations. This harmonizes seamlessly with the core objective of service recommendation, which is to adeptly steer users towards services tailored to their distinct requirements and preferences. However, achieving accurate and real-time QoS predictions in the presence of various issues and anomalies, including outliers, data sparsity, grey sheep instances, and cold start scenarios, remains a challenge. Current state-of-the-art methods often fall short when addressing these issues simultaneously, resulting in performance degradation. In response, in this article, we introduce an <bold>A</b>nomaly-<bold>R</b>esilient <bold>R</b>eal-time <bold>Q</b>oS <bold>P</b>rediction framework (called ARRQP). Our primary contributions encompass proposing an innovative approach to QoS prediction aimed at enhancing prediction accuracy, with a specific emphasis on improving resilience to anomalies in the data. ARRQP utilizes the power of graph convolution techniques, a powerful tool in graph-based machine learning, to capture intricate relationships and dependencies among users and services. By leveraging graph convolution, our framework enhances its ability to model and seize complex relationships within the data, even when the data is limited or sparse. ARRQP integrates both contextual information and collaborative insights, enabling a comprehensive understanding of user-service interactions. By utilizing robust loss functions, this approach effectively reduces the impact of outliers during the training of the predictive model. Additionally, we introduce a method for detecting grey sheep users or services that is resilient to sparsity. These grey sheep instances are subsequently treated separately for QoS prediction. Furthermore, we address the cold start problem as a distinct challenge by emphasizing contextual features over collaborative features. This approach allows us to effectively handle situations where newly introduced users or services lack historical data. Experimental results on the publicly available benchmark WS-DREAM 1 dataset demonstrate the framework's effectiveness in achieving accurate and timely QoS predictions, even in scenarios where anomalies abound.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1245-1261"},"PeriodicalIF":5.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889868","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":"Online Traffic Allocation for Video Service Providers in Cloud-Edge Cooperative Systems","authors":"Zhiwei Zhou;Li Pan;Shijun Liu","doi":"10.1109/TSC.2025.3565363","DOIUrl":"10.1109/TSC.2025.3565363","url":null,"abstract":"Currently, with the popularity of live video applications, VSPs (video service providers) begin to use cloud servers to enhance user experience and reduce operational costs. In this paper, we consider VSPs leveraging a cloud-edge cooperative model to deliver video services for cost reduction. Since bandwidth costs make up a significant portion of VSPs’ operating expenses, we mainly consider bandwidth cost optimizations in traffic allocation. In addition, the QoE (quality of experience) is also very important, while the latency has a larger impact on QoE. Thus our traffic allocation approach aims to strike a fine balance between minimizing bandwidth cost and bounding the latency experienced by clients. Such a trade-off is difficult to optimize with some prevailing bandwidth billing methods such as the 95<sup>th</sup> percentile bandwidth billing. We quantify such a trade-off by constructing a linear bandwidth cost optimization problem. We first describe the offline version of the optimization problem, and then design an online greedy algorithm that considers minimizing the current bandwidth cost at each time slot. By applying the Lyapunov optimization framework, we design another online algorithm based on the original greedy one. We prove that the time average delay achieved by our online algorithm is smaller than the upper bound we set when certain conditions are satisfied. Through extensive simulation experiments, we show that the proposed online algorithm can significantly reduce both the bandwidth cost and the time average delay of clients.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1618-1626"},"PeriodicalIF":5.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889734","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":"A Reliable Distributed-Cloud Storage Based on Permissioned Blockchain","authors":"Kaimin Zhang;Xingwei Wang;Bo Yi;Min Huang;Lin Qiu;Enliang Lv;Jingjing Guo","doi":"10.1109/TSC.2025.3565388","DOIUrl":"10.1109/TSC.2025.3565388","url":null,"abstract":"Traditional single-cloud storage suffers from single point of failure, leading to low data availability. As a result, it fails to meet users’ demands for reliable cloud storage services. Therefore, the current cloud storage paradigm has shifted to distributed-cloud storage (e.g., multi-cloud storage, JointCloud storage), where users store multiple replicas of data across multiple Cloud Service Providers (CSPs). However, this imposes significant storage pressure on CSPs. To reduce costs and maximize profits, some malicious CSPs may delete user data, undermining trust in cloud services and hindering the growth of the cloud computing industry. To address this issue, we propose a novel distributed-cloud storage based on permissioned blockchain, which effectively reduces storage costs while ensuring data availability. Firstly, we integrate Byzantine Fault Tolerance in permissioned blockchain with erasure coding (EC) to replace the traditional multi-cloud multi-replica storage approach. This integration significantly reduces storage costs while providing an efficient means for data recovery. Based on blockchain, we further propose a data integrity auditing approach that eliminates reliance on semi-trusted third-party auditors and enables decentralized data integrity verification. Combined with this auditing approach, our EC-based data recovery approach ensures data availability while enhancing users’ trust in distributed-cloud storage. Theoretical analysis indicates that our scheme reduces storage overhead from <inline-formula><tex-math>$O(n)$</tex-math></inline-formula> to <inline-formula><tex-math>$O(1)$</tex-math></inline-formula> with <inline-formula><tex-math>$n$</tex-math></inline-formula> CSPs while ensuring data availability. Meanwhile, experimental results demonstrate that computational overhead is reduced by approximately 78% compared to traditional multi-cloud multi-replica storage, achieving the cost-effective and highly reliable distributed-cloud storage.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1216-1231"},"PeriodicalIF":5.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889811","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":"Split Computing for Mobile Devices: Energy and Latency Perspective","authors":"Daeyoung Jung;Jaewook Lee;Hyeonjae Jeong;Dongju Cha;Heewon Kim;Sangheon Pack","doi":"10.1109/TSC.2025.3564885","DOIUrl":"10.1109/TSC.2025.3564885","url":null,"abstract":"To tackle the difficulties of running sophisticated deep neural network (DNN) models on mobile devices, split computing presents a viable solution by offloading computations to the edge server. Current split computing schemes typically aim to lower either inference latency or energy use separately; however, optimizing both simultaneously is quite challenging due to numerous shifting factors, such as intensive continuous DNN model inferences, DNN model traits, and device/network conditions. Moreover, in practical applications, edge server overload might lead to substantial queuing delays, adding complexity to the optimization process. This article outlines a joint optimization problem that simultaneously seeks to minimize both inference latency and energy consumption, with a distinct inclusion of queue clearance latency for an accurate analysis of the continuously generated DNN model inferences. To address this intricate optimization challenge, we introduce a low-complexity heuristic algorithm that sets split point decisions based on the residual energy of mobile devices for each DNN inference cycle. Upon evaluation, our proposed algorithm demonstrates notable improvements by reducing inference latency by between 73.37% and 99.39%, and cutting down energy usage by between 39.97% and 94.67% compared to fully local processing on mobile devices.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1798-1810"},"PeriodicalIF":5.5,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884564","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":"ML-Based Intrusion Detection as a Service: Traffic Split Offloading and Cost Allocation in a Multi-Tier Architecture","authors":"Didik Sudyana;Yuan-Cheng Lai;Ying-Dar Lin;Piotr Chołda","doi":"10.1109/TSC.2025.3563680","DOIUrl":"10.1109/TSC.2025.3563680","url":null,"abstract":"An Intrusion Detection System (IDS) employing machine learning (ML) solutions is crucial for identifying network intrusions. To minimize operational expenses and enhance performance, enterprises have begun outsourcing IDS management to service providers, giving rise to the concept of Intrusion Detection as a Service (IDaS). Earlier research primarily aimed at enhancing the accuracy of ML-based IDS models or expediting their computational process. However, from the service provider’s perspective, an optimal architecture ensuring minimal computation cost and processing delay is crucial to increasing revenue. This study evaluates the performance of IDaS in a multi-tier architecture, utilizing traffic split offloading to enhance performance by mapping three in-sequence ML-based IDS tasks (pre-processing, binary detection, multi-class classification) to the architectures as the offloading destinations. We employ a simulated annealing-based traffic offloading and cost allocation (SA-TOCA) algorithm to determine the offloading ratio for each traffic path and the cost requirements for each tier. The results indicate that the edge-cloud architecture is 15% and four times more cost-effective compared to the fog-edge and fog-cloud architectures, respectively, and it demonstrates superior performance in minimizing processing delays. Offloading the majority of traffic to the edge and the remainder to the cloud proves to be an efficient strategy, reducing both computation costs and average delays.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 3","pages":"1557-1572"},"PeriodicalIF":5.5,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884555","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}