IEEE Transactions on Cloud Computing最新文献

{"title":"Blockchain Authorized Privacy-Preserving Framework Using Edge Computing for the Cloud-Assisted Internet of Medical Things","authors":"B. D. Deebak;Seong Oun Hwang","doi":"10.1109/TCC.2026.3656828","DOIUrl":"https://doi.org/10.1109/TCC.2026.3656828","url":null,"abstract":"Recent advancements in mobile cloud computing demand a proper constitution of security components including authentication, authorization, and access control to prevent unauthorized data access. Most computing environments like healthcare handle massive amounts of data generated by the Internet of Medical Things (IoMT) like programmable pacemakers and connect a system of supporting infrastructure to manage the delivery of sensitive information over dedicated networks. Despite its development in various end-user applications (e.g., supply chains and logistics), cloud-IoMT device security and data privacy still persist in addressing a few practical challenges demanding further investigation, such as limited security integration and unpatched vulnerabilities. Additionally, centralized user authentication is not applicable to cross-domain application systems like trusted platforms where system capabilities limit their potential growth with connected devices. Thus, this paper proposes a blockchain-authorized privacy-preserving (BAPP) framework using edge computing for the cloud-assisted IoMT. The proposed framework utilizes a hybrid architecture in the edge server to offer centralized authentication. Accordingly, the edge server is associated with a private blockchain network to ensure proper authentication and verification among IoMT devices and to examine metrics namely committed transactions, latency, and throughput. Formal and informal analyses prove the resiliency of the proposed BAPP framework to meet the design requirements of IoMT systems. Above all, experiment analysis shows that the proposed framework achieves better performance in terms of authentication delay (<inline-formula><tex-math>$ approx 340.5;text{ms}$</tex-math></inline-formula>), average throughput (<inline-formula><tex-math>$approx 94.75%$</tex-math></inline-formula>), computation time (<inline-formula><tex-math>$ 9.548;text{ms}$</tex-math></inline-formula>), and average latency (<inline-formula><tex-math>$ approx 14.11;sec $</tex-math></inline-formula>) compared to other authentication schemes.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"14 2","pages":"445-462"},"PeriodicalIF":5.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507110","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":"Truthful Multi-Combinatorial Double Auction Mechanism for Cloud Resource Allocation","authors":"Qihui Li;Zhonglin He;Asher Wu;Liting Yu;Xiaohua Jia","doi":"10.1109/TCC.2026.3669486","DOIUrl":"https://doi.org/10.1109/TCC.2026.3669486","url":null,"abstract":"Efficient resource allocation in cloud computing environments is crucial for ensuring platform performance and service quality. To address the heterogeneity of cloud resources and the diverse demands of cloud users, this paper proposes a truthful multi-combinatorial double auction (TMCDA) mechanism for cloud resource allocation. The mechanism introduces resource weight ratios to reflect the relative importance and proportional relationships among different resource types, thereby enabling a more precise evaluation of heterogeneous resource combinations. Meanwhile, cloud users are allowed to submit multiple substitutable combinatorial resource requests, which increases allocation flexibility and improves transaction success rates. In the payment design, a critical price calculation method similar to the second-price auction is adopted to guarantee truthfulness and economic efficiency. By integrating resource bundle matching with weighted pricing, the proposed mechanism improves resource utilization and overall social welfare. Simulation results demonstrate that, compared with Truthful Combinatorial/Multi-unit multi-item Double Auction for Cloud computing (TCMDAC) and Combinatorial Double Auction Resource Allocation (CDARA), the proposed approach achieves at least 15% and 30% improvements in social welfare, respectively, highlighting its advantages in transaction fairness, incentive compatibility, and allocation efficiency.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"14 2","pages":"643-654"},"PeriodicalIF":5.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508562","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":"TR-GAN: Data-Augmentation-Aware Transformer-Rectification-Based Generative Adversarial Networks for Long-Term Cloud Workload Forecasting","authors":"Zekun Sun;Fuxiang Chen;Yao Lu;John Panneerselvam;Haowen Zheng;Lu Liu","doi":"10.1109/TCC.2026.3679330","DOIUrl":"https://doi.org/10.1109/TCC.2026.3679330","url":null,"abstract":"Maximum utilisation of minimal amount of resources is pivotal for achieving a sustainable operation in large-scale Cloud Data Centres. Prediction driven resource provisioning in Cloud Data Centres is a potential approach to execute Cloud workloads in a sustianable way. Traditional prediction models often struggle to deliver accurate predictions under dynamic and heterogeneous cloud workloads, as capturing long-range dependencies and sudden workload spikes is often challenging in Cloud environments. In addition, recent time series models such as the Adversarial Error Correction Generative Adversarial Network (AEC-GAN) characterise shortcomings when applied to cloud workload datasets, particularly whilst managing volatility and learning irregular patterns. To address such challenges, this paper proposes a novel prediction model using Data Augment Aware Transformer Rectification-based Generative Adversarial Networks (TR-GAN), which incorporates a continuous and conditional learning Transformer block in the GAN’s generator module to serve both as a data distribution moderator and as a data augmentation generator, ultimately to deliver accurate predictions. TR-GAN is the first GAN-based model tailored for long-term cloud workload forecasting that explicitly couples data augmentation with sequence rectification. Unlike discriminative forecasters, Its generative formulation allows it to model the intrinsic variability and uncertainty of cloud workloads,generate context-aware synthetic data to improve generalization under sparse or irregular patterns, and iteratively refine predictions through an adversarial learning process, thereby reducing error accumulation in long-horizon forecasts. The prediction performance of the proposed model is evaluated with two widely-used cloud workload datasets, namely the Google clusters and Alibaba traces. Experimental results demonstrate that the proposed TR-GAN model can deliver a prediction improvement of around 15% than notable state-of-the-art models, including Informer, Autoformer and AEC-GAN, for long forecasting horizons.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"14 2","pages":"999-1014"},"PeriodicalIF":5.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504499","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":"AquaFed: Ascending Quantized Federated Learning on Heterogeneous Devices","authors":"Juntao Hu;Hong Huang;Kuan Liu;Huimin Lu;Bingyi Liu;Dapeng Wu","doi":"10.1109/TCC.2026.3680955","DOIUrl":"https://doi.org/10.1109/TCC.2026.3680955","url":null,"abstract":"Federated Learning (FL) enables privacy-preserving collaboration across distributed edge devices but faces severe challenges in cross-device settings due to stringent memory and computation constraints. While quantization offers a natural remedy, existing techniques primarily target inference acceleration and fail to improve training efficiency. Consequently, current quantized FL frameworks only mitigate communication overhead while suffering from <italic>memory inefficiency</i> during on-device training. Moreover, they exhibit <italic>Heterogeneity-induced optimization instability</i>, as devices with varying bit-widths jointly participate in federated learning, introducing inconsistent quantization noise that hinders convergence and degrades final accuracy. In this work, we propose <italic>AquaFed</i>, a memory-efficient <italic>A</i>scending <italic>qua</i>ntized <italic>Fed</i>erated learning framework tailored for heterogeneous edge environments. We first introduce Memory-Efficient Quantization (MEQ), which quantizes both weights and activation caches during training to drastically reduce peak memory usage and memory I/O. To address instability arising from heterogeneous precision, we theoretically establish the <italic>Quantization Noise Equivalence (QNE) Hypothesis</i>: <italic>Quantization error exhibits a behavior analogous to the stochastic noise induced by a large learning rate in quantized federated learning.</i> Guided by QNE, AquaFed adopts an ascending quantization schedule, allowing low-bit devices to lead early coarse-grained learning, while high-bit devices refine the model in later stages for stable convergence. Extensive experiments on vision and language benchmarks demonstrate that AquaFed consistently delivers superior accuracy, generalization, and memory efficiency compared to state-of-the-art methods. On CIFAR-10 with ResNet-18, AquaFed achieves a 20% accuracy improvement and 39% memory saving over the best existing framework.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"14 2","pages":"1069-1082"},"PeriodicalIF":5.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148505170","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":"BPO-CBS: A Data-Driven Blockchain Performance Optimization Framework for Cloud Blockchain Services","authors":"Jishu Wang;Xuan Zhang;Linfeng Liu;Xuekun Yang;Tao Zhou;Chen Miao;Rui Zhu;Zhi Jin","doi":"10.1109/TCC.2026.3677471","DOIUrl":"https://doi.org/10.1109/TCC.2026.3677471","url":null,"abstract":"Recently, blockchain has been widely used in important scenarios (e.g., finance and auditing). To fully meet the needs of various business scenarios and reduce deployment costs, cloud blockchain services (CBS) are now being offered by cloud computing providers. However, in high-frequency and large-scale transaction scenarios, blockchain performance faces serious challenges, limiting its further application. Therefore, blockchain performance optimization (BPO) has become a key field. Recent BPO methods that adjust blockchain configuration parameters like block size, offer benefits such as low cost and easy deployment. However, these methods face challenges including unsuitability for dynamic environments, high optimization overhead, and failure to consider marginal utility (MU) in BPO. MU describes the decreasing effectiveness of BPO as transaction arrival rates increases, eventually leading to limited BPO benefits. This paper proposes a data-driven BPO framework (BPO-CBS) for CBS. First, a blockchain performance prediction model is trained using ensemble learning. Second, a performance scoring and adjustment mechanism is designed to identify optimal configuration parameters and adjust them to enhance BPO. Finally, extensive quantitative and qualitative comparisons with related works show that BPO-CBS achieves more effective BPO with low optimization overhead.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"14 2","pages":"949-966"},"PeriodicalIF":5.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148506360","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 Novel Balanced Binary Whale Optimization Algorithm for Dynamic Feature Selection in Green Cloud Computing","authors":"Mateusz Smendowski;Mateusz Wojtulewicz;Piotr Nawrocki;Leszek Rutkowski","doi":"10.1109/TCC.2026.3671450","DOIUrl":"https://doi.org/10.1109/TCC.2026.3671450","url":null,"abstract":"This paper introduces a novel Balanced Binary Whale Optimization Algorithm (BB-WOA) designed specifically for dynamic feature selection in Green Cloud Computing (GCC). Traditional feature selection methods used in cloud resource forecasting often suffer from either suboptimal predictive performance or excessive computational complexity. To address this, we propose significant algorithmic enhancements over the standard Binary Whale Optimization Algorithm (B-WOA), including dynamic binary transition functions, progressive scaling, diversified population initialization via Sobol sequences, balanced exploration-exploitation strategies, and an activation-based recovery mechanism. Our comprehensive experimental evaluation using real-world cloud resource data demonstrates that BB-WOA outperforms existing methods. Specifically, BB-WOA reduces predictive error (RMSE) by up to 7.45% compared to B-WOA and 1.55% compared to Genetic Algorithm (GA). Moreover, BB-WOA achieves computational improvements, reducing execution time by approximately 38.30%, 64.13%, and 78.53% over B-WOA, Random Search (RS), and GA, respectively. Environmentally, the proposed method reduces energy consumption by 38.48% relative to B-WOA, 64.18% compared to RS, and 52.55% relative to GA, while simultaneously selecting significantly fewer features (a reduction of up to 70.77%). These results underscore the effectiveness and sustainability of BB-WOA, positioning it as a highly competitive and environmentally friendly solution.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"14 2","pages":"731-743"},"PeriodicalIF":5.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507734","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":"Hela: A System Call Restriction Framework for Protecting the Entire Containers Lifecycle","authors":"Shaohu Li;Jin Zhou;Xinxin Li;Weizhi Meng;Bei Gong;Jun Hu","doi":"10.1109/TCC.2026.3677796","DOIUrl":"https://doi.org/10.1109/TCC.2026.3677796","url":null,"abstract":"Limiting the number of system calls used by container processes can effectively reduce the kernel attack surface. Existing container system call restriction schemes only focus on the minimum system call set of applications in containers, and lack restrictions on the container runtime runc and other container components that create containers. To solve these problems, this paper proposes Hela, a system call restriction framework that can limit container runtimes and container applications. Hela introduces the Attack Surface Exposure Score (ASES), defined as the dot product of a container's system call usage vector and a risk-weight vector, to quantify exposure. Hela calculates and compares the ASES indicators of various partitioning schemes and selects the best partitioning boundary in the common hook nodes of runc. Hela divides the container creation phase into two phases and generates a minimum set of system calls for each phase. The advantage of Hela is that it combines seccomp with eBPF to achieve accurate parameter checking and efficient system call access listswitching. Experimental results show that Hela can reduce the kernel attack surface of runc in container runtime compared to traditional schemes. Security experiments prove that our method can mitigate vulnerabilities involving runc and system call parameters.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"14 2","pages":"967-984"},"PeriodicalIF":5.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11456734","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508995","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":"A Microservice Assisted Multi-Level DDoS Defense Mechanism in Containerized Cloud Environments","authors":"Anmol Kumar;Shitharth Selvarajan;Mayank Agarwal","doi":"10.1109/TCC.2026.3675747","DOIUrl":"https://doi.org/10.1109/TCC.2026.3675747","url":null,"abstract":"Cloud computing revolutionized the delivery of IT services by providing unparalleled scalability, flexibility, and cost savings. The expansion of cloud computing also attracts Distributed Denial of Service (DDoS) attackers, causing them to shift their targets from traditional server systems to cloud infrastructure. DDoS attacks bombard systems with malicious traffic, creating a significant threat to the availability of cloud services. In the state-of-the-art solutions, we found that resource isolation for legitimate users plays a crucial role in maintaining the service availability under DDoS attacks. By isolating resources, target services are able to maintain their functionality for legitimate users without experiencing substantial interruption, even in the presence of a DDoS attack. In this work, we proposed a robust defense system against DDoS attacks that employs three strategies: categorizing incoming requests based on the frequency of their submissions to different services, allocating resources for distinct services, and implementing a microservice architecture within a cloud infrastructure based on containers. The incoming requests are categorized into four distinct categories: red, orange, yellow, and green. Each category was determined by the number of requests made for a specific service in comparison to threshold values. Subsequently, the requests were served in separate containers. To implement microservice architecture, we deploy each web service on distinct containers. This implies that requests from various users for distinct services get served in separate containers. We tested this approach in three distinct scenarios (E1, E2, and E3) by varying the number of web services at the target infrastructure (2 services on E1, 3 services on E2, and 5 services on E3). By this, we test the scalability of the proposed defense system in the presence of DDoS attacks. The experimental results show that the proposed defense system is highly effective, maintaining service availability up to 90% even under DDoS attacks. This result demonstrates the system's ability to keep services running smoothly for legitimate users, even in the presence of DDoS attacks.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"14 2","pages":"919-930"},"PeriodicalIF":5.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504495","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":"Quantifying the Performance Gap for Simple Versus Optimal Dynamic Server Allocation Policies","authors":"Niklas Carlsson;Derek Eager","doi":"10.1109/TCC.2026.3672446","DOIUrl":"https://doi.org/10.1109/TCC.2026.3672446","url":null,"abstract":"Cloud computing enables the dynamic provisioning of server resources. To exploit this opportunity, a policy is needed for dynamically allocating (and deallocating) servers in response to the current load conditions. In this paper we describe several simple policies for dynamic server allocation and develop analytic models for their analysis. We also design semi-Markov decision models that enable determination of the performance achieved with optimal policies, allowing us to quantify the performance gap between simple, easily implemented policies, and optimal policies. Finally, we apply our models to study the potential performance benefits of state-dependent routing in multi-site systems when using dynamic server allocation at each site. Insights from our results are valuable to service providers wanting to balance cloud service costs and delays.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"14 2","pages":"791-805"},"PeriodicalIF":5.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504501","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":"Are There Manufacturer Differences in Hard-Drive Reliability?","authors":"Christoph Siemroth;Yeomyung Park","doi":"10.1109/TCC.2026.3679404","DOIUrl":"https://doi.org/10.1109/TCC.2026.3679404","url":null,"abstract":"Based on the Backblaze hard disk drive (HDD) dataset, we analyze whether the four major HDD manufacturers represented in the dataset—HGST, Seagate, Toshiba, Western Digital (WD)—show differences in short- to medium-term HDD failure rates. Using two different duration regression models, we find—holding constant drive age, capacity, form-factor, and drive temperature—that Toshiba’s failure rate is slightly above Seagate’s. HGST HDD failure rates are the lowest, about 41% of Seagate’s. WD HDD failure rates are significantly above HGST’s, but still only about 52% of Seagate’s. We also document the effects of age, capacity, temperature and drive location on failure rates.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"14 2","pages":"1015-1024"},"PeriodicalIF":5.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504592","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}