IEEE Transactions on Services Computing最新文献

{"title":"A QoS Prediction Framework via Utility Maximization and Region-Aware Matrix Factorization","authors":"Xia Chen;Yugen Du;Guoxing Tang;Fan Chen;Yingwei Luo;Hanting Wang","doi":"10.1109/TSC.2025.3541554","DOIUrl":"10.1109/TSC.2025.3541554","url":null,"abstract":"With the surge of Web services, users are more concerned about Quality-of-Service (QoS) information when choosing Web services with similar functionalities. Today, effectively and accurately predicting QoS values is a tough challenge. Typically, traditional methods only use the QoS values provided by users to predict the missing QoS values, ignoring the arbitrariness of some users in providing observed QoS values and failing to consider the existence of anomalous QoS values with contingencies caused by some unstable Web services. Taking into account the above, this article proposes HyLoReF-us, a new framework for QoS prediction. HyLoReF-us uses the user reputation to measure the trustworthiness of users and the service reputation to measure the stability of web services. First, considering the utility generated by the invocation between users and Web services, HyLoReF-us employs a Logit model to calculate the user reputation and service reputation. Second, after combining the location information of users and services, as well as their reputations, HyLoReF-us obtains QoS predictions through an improved Matrix Factorization (MF) model. Finally, a series of experiments were conducted on the standard WS-DREAM dataset. Experimental results show that HyLoReF-us outperforms current state-of-the-art or baseline methods at Matrix Densities (MD) from 5% to 30%.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"557-571"},"PeriodicalIF":5.5,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143401620","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":"An Active Defense Adjudication Method Based on Adaptive Anomaly Sensing for Mimic IoT","authors":"Sisi Shao;Tiansheng Gu;Yijun Nie;Zongkai Ji;Fei Wu;Zhongjie Ba;Yimu Ji;Kui Ren;Guozi Sun","doi":"10.1109/TSC.2024.3436673","DOIUrl":"https://doi.org/10.1109/TSC.2024.3436673","url":null,"abstract":"Security issues in the Internet of Things (IoT) are inevitable. Uncertain threats, such as known vulnerabilities and backdoors exist within IoT, and traditional passive network security technologies are ineffective against uncertain threats. To address the above issues, we propose an active defense adjudication method based on adaptive anomaly sensing for mimic IoT. The method constructs a mimic IoT active defense architecture, improving system security and reliability despite prevailing security threats. In addition, an intelligent anomaly sensing algorithm is integrated into the adjudication module of the mimic IoT active defense architecture to support arbitration. An adaptive anomaly sensing model based on multi-feature selection is used to determine the anomaly score of the IoT device outputs, and this model fully considers the reliability of the adjudication data and improves the accuracy of the adjudication. Finally, we conduct a comparative analysis of the proposed adjudication algorithm against three others via a mimic power communication IoT system as an application scenario. The experimental results show that our algorithm can improve security and reduce the failure rate of the mimic IoT system.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"57-71"},"PeriodicalIF":5.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361272","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":"Next PoI Recommendation Based on Graph Convolutional Networks and Multiple Context-Awareness","authors":"Wei Zhou;Cheng Fu;Chunyan Sang;Min Gao;Junhao Wen","doi":"10.1109/TSC.2024.3463500","DOIUrl":"https://doi.org/10.1109/TSC.2024.3463500","url":null,"abstract":"Next Point-of-interest recommendation involves modeling user interactions with Point-of-interests (PoIs) to analyze user behavior patterns and suggest future scenarios. Data sparsity problems in PoI recommendations can significantly impact the performance of the recommendation model. This paper introduces the Graph Convolutional Network and Multiple Context-Aware PoI Recommendation model (GMCA). First, we present a weighted graph convolutional network that aims to capture the optimal representations of users and PoIs within the user-PoI interaction graph. Second, we employ a fine-grained approach to analyze user check-in records and cluster them into multiple user activity centers. Furthermore, we incorporate time, location, and social context information into the matrix decomposition process. Third, User activity centers are constructed by clustering user check-in records, and the geographical influence of PoI location on user behavioral patterns is explored using probabilistic factor decomposition. The evaluation of the GMCA model on the Yelp and Gowalla datasets shows a significant improvement in Precision@10 indicators. Specifically, there is a 13.85% increase in Precision@10 on the Yelp dataset and a 9.01% increase on the Gowalla dataset. The effectiveness of the GMCA model has been confirmed through numerous experiments conducted on two public datasets.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 1","pages":"302-313"},"PeriodicalIF":5.5,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361432","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":"Behavior Tomographer: Identifying Hidden Cybercrimes by Behavior Interior Structure Modeling","authors":"Cheng Wang;Hangyu Zhu","doi":"10.1109/TSC.2025.3539194","DOIUrl":"10.1109/TSC.2025.3539194","url":null,"abstract":"Identifying hidden cybercrimes is a challenging task, as these behaviors are often carefully planned by criminals with counter-surveillance awareness. Existing solutions for cybercrime detection struggle to uncover enough clues to identify hidden criminal behaviors. Malicious behaviors are concealed beneath benign behaviors, and the boundaries between malicious and benign behaviors in the representation space are blurred to evade mainstream deep learning-based security authentication models. We introduce a <underline>b</u>ehavior <underline>t</u>omographer (BT) to reconstruct the behavior structure from three slices: agent, event, and attribute slices, enabling more granular detection of hidden cybercrimes. The core idea of BT is to reconstruct interior information about behavior structure from multiple slices, much like computed tomography in modern medicine enables the reconstruction of internal body. It enables the extraction of discriminative information from intricate interior associations between behavioral attributes rather than surface information meticulously crafted by criminals. Our experiments are conducted on two representative cybercrime datasets. Promising experimental results demonstrate that BT outperforms state-of-the-art models on key metrics, achieving around 0.99 AUC-ROC and approximately 0.9 AUC-PR. Moreover, BT notably excels at low false positive rates, showcasing its high effectiveness for real-world applications.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"673-689"},"PeriodicalIF":5.5,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143258432","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":"DRMQ: Dynamic Resource Management for Enhanced QoS in Collaborative Edge-Edge Industrial Environments","authors":"Fengyi Huang;Wenhua Wang;Qin Liu;Wentao Fan;Jianxiong Guo;Weijia Jia;Jiannong Cao;Tian Wang","doi":"10.1109/TSC.2025.3539201","DOIUrl":"10.1109/TSC.2025.3539201","url":null,"abstract":"In the fast-developing industrial environments, extensive focus on resource management within Mobile Edge Computing (MEC) aims to ensure low-latency QoS, however, some tasks offloaded to the cloud still experience high latency. Additionally, high energy consumption, poor link reliability, and excessive processing delays are intolerable for industrial applications. Compared to general servers, edge computing devices based on Arm architecture exhibit lower latency and higher energy efficiency. This highlights the need for improved heterogeneous Collaborative Edge-Edge Industrial Environments (CEIE) and precise multi-user QoS metrics. Thus, we focus on dynamic resource management within the CEIE architecture to better satisfy diverse industrial applications, formulating a multi-stage Mixed Integer Nonlinear Programming (MINLP) problem to minimize system costs. To reduce the computational complexity of solving the MINLP, we decompose the original problem into multi-user task offloading, Communication Resource Allocation (CmRA), and Computational Resource Allocation (CpRA) problems. These transformed problems are then tackled using DRMQ: an integrated learning optimization approach that combines model-free, priority experience replay-based Double Deep Q-Network (iDDQN) with model-based optimization, accelerating the Q-value function's convergence speed and reducing training time. Extensive simulations show that our proposed optimization scheme can reduce the average weighted system cost by at least 43.168% . Moreover, testbed experiments demonstrate that the proposed algorithm can reduce the average system cost by at least 42.650% in real-world applications, outperforming existing methods.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"743-757"},"PeriodicalIF":5.5,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143258433","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":"SHELDB: Client Storage Aware Homomorphic Encrypted Database Processing Framework With Low Communication Overhead","authors":"Tanusree Parbat;Ayantika Chatterjee","doi":"10.1109/TSC.2025.3539189","DOIUrl":"10.1109/TSC.2025.3539189","url":null,"abstract":"Database as a service (DBaaS) in cloud raises severe concern in terms of data security. Storing data in encrypted form may confirm data confidentiality. But, database processing cannot be supported in this encrypted form. Theoretically, homomorphic encryption is a solution to support direct encrypted data processing. In this work, we highlight one of the major challenges of FHE- encrypted query processing that demands huge data transfer requirements from cloud to client for final decryption at the end of SQL query execution. We show in light of Chosen Plaintext Attack (CPA) that in spite of performing conditional filtering through SQL queries over encrypted databases, the size of the resultant dataset cannot be less than the original size of the database. In this work, we make an effort to propose a new encrypted query processing framework termed as <i>SHELDB</i> which supports client storage compatibility and low communication overhead using block-wise final result transmission from cloud to client by extending the concept of TOP operator implementation in standard SQL. However, it is to be noted that realization of such optimization is not straightforward because of circuit-based implementation requirements with FHE gates. Our experimental demonstration shows that the proposed framework is capable of executing all TPC-C standard SQL queries with the aid of 8-core parallel processing within <inline-formula><tex-math>$sim 12.65$</tex-math></inline-formula> minutes for an encrypted database of <inline-formula><tex-math>$768 times 9$</tex-math></inline-formula> size with 16-bits elements each. Though the computation time is linear with the number of rows, we have explored map-reduce type parallel processing techniques to reduce the timing requirements for databases with larger rows. Consequently, our new query processing framework reduces the communication overhead from m to <inline-formula><tex-math>$delta k$</tex-math></inline-formula> rows (<inline-formula><tex-math>$1 leq delta leq block$</tex-math></inline-formula>) where the encrypted database contains m rows, <inline-formula><tex-math>$delta$</tex-math></inline-formula> is the number of blocks to be transmitted each time with <inline-formula><tex-math>$k$</tex-math></inline-formula> = (<inline-formula><tex-math>$m/block$</tex-math></inline-formula>) rows. In spite of <inline-formula><tex-math>$k$</tex-math></inline-formula> being a controllable parameter according to client storage and <inline-formula><tex-math>$delta$</tex-math></inline-formula> is dependent on the query parameters, final security analysis explains why the proposed technique is general database attack-resistant.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"1026-1038"},"PeriodicalIF":5.5,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191813","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 Multi-Domain Survey on Time-Criticality in Cloud Computing","authors":"R. Andreoli;R. Mini;P. Skarin;H. Gustafsson;J. Harmatos;L. Abeni;T. Cucinotta","doi":"10.1109/TSC.2025.3539197","DOIUrl":"10.1109/TSC.2025.3539197","url":null,"abstract":"Conventional cloud services and infrastructures are mainly designed to maximize utilization of resources and provide best-effort Quality-of-Service levels. However, many emerging use cases in both public and private cloud computing scenarios are time-critical in nature. For example, automated vehicles, smart cities, and automated factories, are all application domains characterized by the need for highly reliable and consistent low-latency services. The incorporation of predictable execution properties in cloud solutions is essential to meet these requirements. This paper provides an overview of the current research landscape in cloud computing, summarizing the key aspects to enable support of time-critical applications. The paper explores various levels of the typical cloud software stack: machine virtualization and containers, resource management and orchestration, fault tolerance, serverless computing, data storage and management, and communications.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"1152-1170"},"PeriodicalIF":5.5,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191812","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":"Joint Service Deployment and Task Offloading for Datacenters With Edge Heterogeneous Servers","authors":"Fu Xiao;Weibei Fan;Lei Han;Tie Qiu;Xiuzhen Cheng","doi":"10.1109/TSC.2025.3539199","DOIUrl":"10.1109/TSC.2025.3539199","url":null,"abstract":"Mobile edge computing (MEC) can improve execution efficiency and reduce overhead for offloading computing tasks to edge servers with more resources. In the microservice system, the current research only considers the cross segment communication cost of computing tasks, does not consider the case of the same end, and ignores the discovery and invocation optimization of associated services. In this paper, we propose <i>CACO</i>, which is a novel content-aware classification offloading framework for MEC based on correlation matrix. <i>CACO</i> first designs an adaptive service discovery model, which can make timely response and adjustment to the changes of the external environment. It then investigates an efficient affinity matrix based service discovery algorithm, which expresses the association relationship between services by constructing a service association matrix. In addition, <i>CACO</i> constructs a relational model by giving different weight coefficients to the delay and energy loss, which improves the delay and energy loss of message processing in a satisfying manner. Simulation results indicate that <i>CACO</i> reduces the total traffic of redundant messages by 46.2% <inline-formula><tex-math>$sim$</tex-math></inline-formula>76.5%, respectively compared with state-of-the-art solutions. Testbed benchmarks show that it can also improve the stability by reducing control overhead by 34.5% <inline-formula><tex-math>$sim$</tex-math></inline-formula>81.6% .","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"839-853"},"PeriodicalIF":5.5,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191810","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":"BackFillMe: An Energy and Performance Efficient Virtual Machine Scheduler for IaaS Datacenters","authors":"Muhammad Zakarya;Lee Gillam;Mohammad Reza Chalak Qazani;Ayaz Ali Khan;Khaled Salah;Omer Rana","doi":"10.1109/TSC.2025.3539190","DOIUrl":"10.1109/TSC.2025.3539190","url":null,"abstract":"Backfilling refers to the practice of allowing small jobs to be completed ahead of schedule as long as they do not cause the first job in the line to wait. Users are expected to offer estimates of how long jobs will take to complete in order to make these decisions possible, and these projections are often based on historical data. However, predictions are very hard and may not be accurate, particularly in cloud computing scenarios where jobs or applications run on Virtual Machines (VMs). In addition, scheduling and consolidation techniques can improve the energy efficiency and performance of applications. Consolidation involves VM migrations that can have a negative impact on workload performance and users’ costs. Backfilling can be used as an alternative technique for consolidation (short-term) and/or can be used along with consolidation (long-term). Backfilling methods are well-utilised in single computing systems, but are relatively unexplored in cloud resource allocation. A backfilling-based resource allocation and consolidation technique is proposed. Using real workloads from the Google cluster traces, we investigate the impact of backfilling on infrastructure energy efficiency and performance. For 12583 heterogeneous servers and approximately three million jobs that belong to three different applications, we observed that approximately 19% energy savings and 6% workload performance improvements are achievable using the backfilling approach. Furthermore, our evaluation suggests that using VM runtime as a criterion for the backfilling approach is approximately 3.56%–7.78% more energy and 1.91%–3.38% more performance efficient than using priority as a backfilling criterion.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"660-672"},"PeriodicalIF":5.5,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191811","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 False Positive Resilient Distributed Trust Management Framework for Collaborative Intrusion Detection Systems","authors":"Kadhim Hayawi;Imran Makhdoom;Saifullah Khalid;Richard Adeyemi Ikuesan;Mohammed Kaosar;Ishfaq Ahmad","doi":"10.1109/TSC.2025.3539202","DOIUrl":"10.1109/TSC.2025.3539202","url":null,"abstract":"Collaborative Intrusion Detection System (CIDS) protect large networks against distributed attacks. However, a CIDS is vulnerable to insider attacks that decrease the mutual trust among the CIDS nodes. Most existing trust management approaches rely on a central authority, trusted third parties or network peers for managing trust. The current techniques are prone to high false positives and vulnerable to various reputation attacks. For instance, device attestation manages trust among CIDS nodes by verifying the integrity of a node’s hardware and software configuration. However, it lacks real-time monitoring of the dynamic state, limiting its effectiveness against ongoing attacks and malware. Therefore, incorporating the system’s dynamic state in the trust framework is crucial, but it causes false positives requiring corrective mechanisms. To address these challenges, this paper proposes a blockchain-based integrated trust management framework for CIDS, incorporating the device’s genome attestation, the system’s dynamic parameters, and a false positive resilient reputation mechanism. By storing the reputation scores on the blockchain, the framework alleviates the need for a third party for trust management and thus mitigates attacks applicable to reputation-based systems. The paper performs a comprehensive security and performance analysis of the proposed framework to gauge its efficiency and study the effects of a penalty on a node’s reputation during the recovery and rally phases. We also study the impact of false positives on the reputation of a node. The results show that Hyperledger Fabric offers lower transaction latency and low CPU utilization compared to Ethereum Blockchain.","PeriodicalId":13255,"journal":{"name":"IEEE Transactions on Services Computing","volume":"18 2","pages":"513-526"},"PeriodicalIF":5.5,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191814","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}