IEEE Transactions on Emerging Topics in Computing最新文献

{"title":"IEEE Transactions on Emerging Topics in Computing Publication Information","authors":"","doi":"10.1109/TETC.2025.3572317","DOIUrl":"https://doi.org/10.1109/TETC.2025.3572317","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 2","pages":"C2-C2"},"PeriodicalIF":5.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11045261","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Guest Editorial: Special Section on Applied Software Aging and Rejuvenation","authors":"Raffaele Romagnoli;Jianwen Xiang","doi":"10.1109/TETC.2025.3579813","DOIUrl":"https://doi.org/10.1109/TETC.2025.3579813","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 2","pages":"281-282"},"PeriodicalIF":5.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11045264","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Software Aging Detection and Rejuvenation Assessment in Heterogeneous Virtual Networks","authors":"Alberto Avritzer;Andrea Janes;Andrea Marin;Catia Trubiani;Andre van Hoorn;Matteo Camilli;Daniel S. Menasché;André B. Bondi","doi":"10.1109/TETC.2025.3547612","DOIUrl":"https://doi.org/10.1109/TETC.2025.3547612","url":null,"abstract":"In this article, we report on the application of resiliency enforcement strategies that were applied to a microservices system running on a real-world deployment of a large cluster of heterogeneous Virtual Machines (VMs). We present the evaluation results obtained from measurement and modeling implementations. The measurement infrastructure was composed of 15 large and 15 extra-large VMs. The modeling approach used Markov Decision Processes (MDP). On the measurement testbed, we implemented three different levels of software rejuvenation granularity to achieve software resiliency. We have discovered two threats to resiliency in this environment. The first threat to resiliency was a memory leak that was part of the underlying open-source infrastructure in each VM. The second threat to resiliency was the result of the contention for resources in the physical host, which is dependent on the number and size of VMs deployed to the physical host. In the MDP modeling approach, we evaluated four strategies for assigning tasks to VMs with different configurations and different levels of parallelism. Using the large cluster under study, we compared our approach of using software aging and rejuvenation with the state-of-the-art approach of using a network of VMs deployed to a private cloud without software aging detection and rejuvenation. In summary, we show that in a private cloud with non-elastic resource allocation in the physical hosts, careful performance engineering needs to be performed to optimize the trade-offs between the number of VMs allocated and the total memory allocated to each VM.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 2","pages":"299-313"},"PeriodicalIF":5.1,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10923615","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Transactions on Emerging Topics in Computing Publication Information","authors":"","doi":"10.1109/TETC.2025.3543119","DOIUrl":"https://doi.org/10.1109/TETC.2025.3543119","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"C2-C2"},"PeriodicalIF":5.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10918568","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"2024 Reviewers List*","authors":"","doi":"10.1109/TETC.2025.3530016","DOIUrl":"https://doi.org/10.1109/TETC.2025.3530016","url":null,"abstract":"We thank the following reviewers for the time and energy they have given to <italic>TETC</i>:","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"276-278"},"PeriodicalIF":5.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10918565","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial Special Section on Emerging Edge AI for Human-in-the-Loop Cyber Physical Systems","authors":"Radu Marculescu;Jorge Sá Silva","doi":"10.1109/TETC.2024.3472428","DOIUrl":"https://doi.org/10.1109/TETC.2024.3472428","url":null,"abstract":"Edge Artificial Intelligence (AI) enables us to deploy distributed AI models, optimize computational and energy resources, minimize communication demands, and, most importantly, meet privacy requirements for Internet of Things (IoT) applications. Since data remains on the end-devices and only model parameters are shared with the server, it becomes possible to leverage the vast amount of data collected from smartphones and IoT devices without compromising the user's privacy. However, Federated Learning (FL) solutions also have well-known limitations. In particular, as systems that account for human behaviour become increasingly vital, future technologies need to become attuned to human behaviours. Indeed, we are already witnessing unparalleled advancements in technology that empower our tools and devices with intelligence, sensory abilities, and communication features. At the same time, continued advances in the miniaturization of computational capabilities can enable us to go far beyond the simple tagging and identification, towards integrating computational resources directly into these objects, thus making our tools “intelligent”. Yet, there is limited scientific work that considers humans as an integral part of these IoT-powered cyber-physical systems.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 1","pages":"3-4"},"PeriodicalIF":5.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10918564","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NegCPARBP: Enhancing Privacy Protection for Cross-Project Aging-Related Bug Prediction Based on Negative Database","authors":"Dongdong Zhao;Zhihui Liu;Fengji Zhang;Lei Liu;Jacky Wai Keung;Xiao Yu","doi":"10.1109/TETC.2025.3546549","DOIUrl":"https://doi.org/10.1109/TETC.2025.3546549","url":null,"abstract":"The emergence of <underline>A</u>ging-<underline>R</u>elated <underline>B</u>ug<underline>s</u> (ARBs) poses a significant challenge to software systems, resulting in performance degradation and increased error rates in resource-intensive systems. Consequently, numerous ARB prediction methods have been developed to mitigate these issues. However, in scenarios where training data is limited, the effectiveness of ARB prediction is often suboptimal. To address this problem, <underline>C</u>ross-<underline>P</u>roject <underline>A</u>ging-<underline>R</u>elated <underline>B</u>ug <underline>P</u>rediction (CPARBP) is proposed, which utilizes data from other projects (i.e., source projects) to train a model aimed at predicting potential ARBs in a target project. However, the use of source-project data raises privacy concerns and discourages companies from sharing their data. Therefore, we propose a method called <underline>C</u>ross-<underline>P</u>roject <underline>A</u>ging-<underline>R</u>elated <underline>B</u>ug <underline>P</u>rediction based on <underline>Neg</u>ative Database (NegCPARBP) for privacy protection. NegCPARBP first converts the feature vector of a software file into a binary string. Second, the corresponding <underline>N</u>egative <underline>D</u>ata<underline>B</u>ase (<italic>NDB</i>) is generated based on this binary string, containing data that is significantly more expressive from the original feature vector. Furthermore, to ensure more accurate prediction of ARB-prone and ARB-free files based on privacy-protected data (i.e., maintain the data utility), we propose a novel negative database generation algorithm that captures more information about important features, using information gain as a measure. Finally, NegCPARBP extracts a new feature vector from the <italic>NDB</i> to represent the original feature vector, facilitating data sharing and ARB prediction objectives. Experimental results on Linux, MySQL, and NetBSD datasets demonstrate that NegCPARBP achieves a high defense against attacks (privacy protection performance reaching 0.97) and better data utility compared to existing privacy protection methods.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 2","pages":"283-298"},"PeriodicalIF":5.1,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323167","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":"Towards Label-Efficient Deep Learning-Based Aging-Related Bug Prediction With Spiking Convolutional Neural Networks","authors":"Yunzhe Tian;Yike Li;Kang Chen;Zhenguo Zhang;Endong Tong;Jiqiang Liu;Fangyun Qin;Zheng Zheng;Wenjia Niu","doi":"10.1109/TETC.2025.3531051","DOIUrl":"https://doi.org/10.1109/TETC.2025.3531051","url":null,"abstract":"Recent advances in Deep Learning (DL) have enhanced Aging-Related Bug (ARB) prediction for mitigating software aging. However, DL-based ARB prediction models face a dual challenge: overcoming overfitting to enhance generalization and managing the high labeling costs associated with extensive data requirements. To address the first issue, we utilize the sparse and binary nature of spiking communication in Spiking Neural Networks (SNNs), which inherently provides brain-inspired regularization to effectively alleviate overfitting. Therefore, we propose a Spiking Convolutional Neural Network (SCNN)-based ARB prediction model along with a training framework that handles the model’s spatial-temporal dynamics and non-differentiable nature. To reduce labeling costs, we introduce a Bio-inspired and Diversity-aware Active Learning framework (BiDAL), which prioritizes highly informative and diverse samples, enabling more efficient usage of the limited labeling budget. This framework incorporates bio-inspired uncertainty to enhance informativeness measurement along with using a diversity-aware selection strategy based on clustering to prevent redundant labeling. Experiments on three ARB datasets show that ARB-SCNN effectively reduces overfitting, improving generalization performance by 6.65% over other DL-based classifiers. Additionally, BiDAL boosts label efficiency for ARB-SCNN training, outperforming four state-of-the-art active learning methods by 4.77% within limited labeling budgets.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 2","pages":"314-329"},"PeriodicalIF":5.1,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323163","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":"Guest Editorial: Special Section on “Approximate Data Processing: Computing, Storage and Applications”","authors":"Ke Chen;Shanshan Liu;Weiqiang Liu;Fabrizio Lombardi;Nader Bagherzadeh","doi":"10.1109/TETC.2024.3488452","DOIUrl":"https://doi.org/10.1109/TETC.2024.3488452","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 4","pages":"954-955"},"PeriodicalIF":5.1,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10779333","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Transactions on Emerging Topics in Computing Information for Authors","authors":"","doi":"10.1109/TETC.2024.3499715","DOIUrl":"https://doi.org/10.1109/TETC.2024.3499715","url":null,"abstract":"","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"12 4","pages":"C2-C2"},"PeriodicalIF":5.1,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10779345","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}