IEEE Transactions on Cloud Computing最新文献_第2页

PHOENIX: Misconfiguration Detection for AWS Serverless Computing PHOENIX：用于AWS无服务器计算的错误配置检测

IF 5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-06-06 DOI: 10.1109/TCC.2025.3577211

Jinfeng Wen;Haodi Ping

{"title":"PHOENIX: Misconfiguration Detection for AWS Serverless Computing","authors":"Jinfeng Wen;Haodi Ping","doi":"10.1109/TCC.2025.3577211","DOIUrl":"https://doi.org/10.1109/TCC.2025.3577211","url":null,"abstract":"Serverless computing is a burgeoning cloud computing paradigm that allows developers to implement applications at the function level, known as serverless applications. Amazon Web Services (AWS), the leading provider in this field, offers Serverless Application Model (AWS SAM), a widely adopted configuration schema for configuring functions and managing resources. However, misconfigurations pose a major challenge during serverless application development, and existing methods are not applicable. To our knowledge, the configuration characteristics and misconfiguration detection for serverless applications have not been well explored. To address this gap, we collect and analyze 733 real-world serverless application configuration files using AWS SAM to understand their characteristics and challenges. Based on the insights, we design <italic>PHOENIX, a misconfiguration detection approach for serverless computing. <italic>PHOENIX learns configuration patterns from uniform representations of configurations and identifies potential misconfigurations that deviate from these patterns. To evaluate <italic>PHOENIX, we construct a dataset comprising 35 injected misconfigurations and 70 real-world misconfigurations with confirmed causes. Our results show that <italic>PHOENIX detects 100% of the injected misconfigurations and identifies 97.14% of real-world misconfigurations, significantly outperforming the state-of-the-art tool.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 3","pages":"922-934"},"PeriodicalIF":5.0,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998012","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}

引用次数: 0

A Cross-Workload Power Prediction Method Based on Transfer Gaussian Process Regression in Cloud Data Centers 基于传递高斯过程回归的云数据中心跨工作负载功率预测方法

IF 5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-06-02 DOI: 10.1109/TCC.2025.3575790

Ruichao Mo;Weiwei Lin;Haocheng Zhong;Minxian Xu;Keqin Li

{"title":"A Cross-Workload Power Prediction Method Based on Transfer Gaussian Process Regression in Cloud Data Centers","authors":"Ruichao Mo;Weiwei Lin;Haocheng Zhong;Minxian Xu;Keqin Li","doi":"10.1109/TCC.2025.3575790","DOIUrl":"https://doi.org/10.1109/TCC.2025.3575790","url":null,"abstract":"Nowadays, machine learning (ML)-based power prediction models for servers have shown remarkable performance, leveraging large volumes of labeled data for training. However, collecting extensive labeled power data from servers in cloud data centers incurs substantial costs. Additionally, varying resource demands across different workloads (e.g., CPU-intensive, memory-intensive, and I/O-intensive) lead to significant differences in power consumption behaviors, known as domain shift. Consequently, power data collected from one type of workload cannot effectively train power prediction models for other workloads, limiting the exploration of the collected power data. To tackle these challenges, we propose <italic>TGCP, a cross-workload power prediction method based on multi-source transfer Gaussian process regression. <italic>TGCP transfers knowledge from abundant power data across multiple source workloads to a target workload with limited power data. Furthermore, Continuous normalizing flows adjust the posterior prediction distribution of Gaussian process, making it locally non-Gaussian, enhancing <italic>TGCP’s ability to handle real-world power data distribution. This method enhances prediction accuracy for the target workload while reducing the expense of acquiring power data for real cloud data centers. Experimental results on a realistic power consumption dataset demonstrate that <italic>TGCP surpasses four traditional ML methods and three transfer learning methods in cross-workload power prediction.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 3","pages":"910-921"},"PeriodicalIF":5.0,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997970","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}

引用次数: 0

Optimizing Cloud Computing Performance Through Integration of a Threshold-Based Load Balancing Algorithm With Multiple Service Broker Policies 通过集成基于阈值的负载均衡算法和多个服务代理策略来优化云计算性能

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-04-24 DOI: 10.1109/TCC.2025.3563848

Shusmoy Chowdhury;Ajay Katangur

{"title":"Optimizing Cloud Computing Performance Through Integration of a Threshold-Based Load Balancing Algorithm With Multiple Service Broker Policies","authors":"Shusmoy Chowdhury;Ajay Katangur","doi":"10.1109/TCC.2025.3563848","DOIUrl":"https://doi.org/10.1109/TCC.2025.3563848","url":null,"abstract":"The triumph of cloud computing hinges upon the adept instantiation of infrastructure and the judicious utilization of available resources. Load balancing, a pivotal facet, substantiates the fulfillment of these imperatives, thereby augmenting the performance of the cloud environment for its users. Our research introduces a load balancing algorithm grounded in threshold principles devised to ensure equitable distribution of workloads among nodes. The main objective of the algorithm is to preclude the overburdening of virtual machines (VMs) within the cloud with tasks or their idleness due to task allocation deficiencies in the presence of active tasks. The threshold values embedded in our algorithm ascertain the judicious deployment of VMs, forestalling both task overload and idle states arising from task allocation inadequacies. Simulation outcomes manifest that our threshold-based algorithm markedly enhances response time for tasks/requests and data processing duration within datacenters, outperforming extant algorithms such as First Come First Serve, Round Robin, and the Equally Spread Current Execution Load Balancing algorithm. Our threshold algorithm attains superior results to alternative load balancing algorithms when coupled with an optimized response time service broker policy.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 2","pages":"751-768"},"PeriodicalIF":5.3,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232134","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}

引用次数: 0

Modeling Resource Scheduling in Optical Switching DCNs Under Bursty and Skewed Traffic 突发和倾斜流量下光交换DCNs资源调度建模

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-04-16 DOI: 10.1109/TCC.2025.3561281

Shuai Zhang;Baojun Chen;Weiqiang Sun;Weisheng Hu

引用次数: 0

Secure kNN for Distributed Cloud Environment Using Fully Homomorphic Encryption 基于全同态加密的分布式云环境安全kNN

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-04-16 DOI: 10.1109/TCC.2025.3561586

Yuuya Fukuchi;Sota Hashimoto;Kazuya Sakai;Satoshi Fukumoto;Min-Te Sun;Wei-Shinn Ku

{"title":"Secure kNN for Distributed Cloud Environment Using Fully Homomorphic Encryption","authors":"Yuuya Fukuchi;Sota Hashimoto;Kazuya Sakai;Satoshi Fukumoto;Min-Te Sun;Wei-Shinn Ku","doi":"10.1109/TCC.2025.3561586","DOIUrl":"https://doi.org/10.1109/TCC.2025.3561586","url":null,"abstract":"Privacy-preserving k-nearest neighbor (PPkNN) classification for multiple clouds enables categorizing queried data into a class in keeping with data privacy, where the database and key servers jointly perform cryptographic operations. The existing solutions, unfortunately, take a long time and incur a large amount of traffic between the database and key servers. Therefore, in this article, we propose a fast and secure kNN classification protocol, namely FSkNN, over distributed databases deployed in multiple clouds under the semi-honest model. Particularly, we focus on optimizing the network-related operations during kNN classification. That is, the proposed cryptographic protocol reduces the number of interactions between the servers by using a fully homomorphic encryption scheme and eliminates unnecessary traffic by applying mathematical techniques. In addition, the indistinguishability-based security of FSkNN is proven. We implemented FSkNN with C++ and the testbed experiments demonstrate that the proposed scheme significantly facilitates the query response time and reduces the communication cost.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 2","pages":"721-736"},"PeriodicalIF":5.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230588","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}

引用次数: 0

SOCT: Secure Outsourcing Computation Toolkit Using Threshold ElGamal Algorithm 基于阈值ElGamal算法的安全外包计算工具包

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-04-15 DOI: 10.1109/TCC.2025.3561313

Sen Hu;Shang Ci;Donghai Guan;Çetin Kaya Koç

{"title":"SOCT: Secure Outsourcing Computation Toolkit Using Threshold ElGamal Algorithm","authors":"Sen Hu;Shang Ci;Donghai Guan;Çetin Kaya Koç","doi":"10.1109/TCC.2025.3561313","DOIUrl":"https://doi.org/10.1109/TCC.2025.3561313","url":null,"abstract":"Cloud computing offers inexpensive and scalable solutions for data processing, however privacy concerns often hinder the outsourcing of sensitive information. Homomorphic encryption provides a promising approach for secure computations over encrypted data. However, existing models often rely on restrictive assumptions, such as semi-honest adversaries and inaccessible public data. To address these limitations, we introduce the Secure Outsourcing Computation Toolkit (SOCT), which is a novel framework based on the threshold ElGamal cryptosystem. The toolkit employs a dual-server decryption architecture using a (2,2) threshold additively homomorphic ElGamal (TAHEG) algorithm. This architecture ensures that ciphertexts can be decrypted only with the cooperation of both servers, mitigating the risk of data breaches. The TAHEG algorithm requires the input of a secret key for every decryption operation, preventing unauthorized access to plaintext data. Moreover, the key generation process does not burden users with generating or distributing partial secret keys. We provide rigorous security proofs for our threshold ElGamal cryptosystem and associated secure computation functions. Experimental results demonstrate that SOCT achieves significant efficiency gains compared to existing toolkits, making it a practical choice for privacy-preserving data outsourcing.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 2","pages":"711-720"},"PeriodicalIF":5.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232041","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}

引用次数: 0

Breaking the Edge: Enabling Efficient Neural Network Inference on Integrated Edge Devices 突破边缘：在集成边缘设备上实现高效神经网络推理

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-04-09 DOI: 10.1109/TCC.2025.3559346

Feng Zhang;Chenyang Zhang;Jiawei Guan;Qiangjun Zhou;Kuangyu Chen;Xiao Zhang;Bingsheng He;Jidong Zhai;Xiaoyong Du

{"title":"Breaking the Edge: Enabling Efficient Neural Network Inference on Integrated Edge Devices","authors":"Feng Zhang;Chenyang Zhang;Jiawei Guan;Qiangjun Zhou;Kuangyu Chen;Xiao Zhang;Bingsheng He;Jidong Zhai;Xiaoyong Du","doi":"10.1109/TCC.2025.3559346","DOIUrl":"https://doi.org/10.1109/TCC.2025.3559346","url":null,"abstract":"Edge computing has gained widespread attention in cloud computing due to the increasing demands of AIoT applications and the evolution of edge architectures. One prevalent application in this domain is neural network inference on edge for computing and processing. This article presents an in-depth exploration of inference on integrated edge devices and introduces EdgeNN, a groundbreaking solution for inference specifically designed for CPU-GPU integrated edge devices. EdgeNN offers three key innovations. First, EdgeNN adaptively employs <italic>zero-copy optimization by harnessing unified physical memory. Second, EdgeNN introduces an innovative approach to CPU-GPU hybrid execution tailored for inference tasks. This technique enables concurrent CPU and GPU operation, effectively leveraging edge platforms’ computational capabilities. Third, EdgeNN adopts a finely tuned adaptive inference tuning technique that analyzes complex inference structures. It divides computations into sub-tasks, intelligently assigning them to the two processors for better performance. Experimental results demonstrate EdgeNN's superiority across six popular neural network inference processing. EdgeNN delivers average speed improvements of 3.97×, 4.10×, 3.12×, and 8.80× when compared to inference on four distinct edge CPUs. Furthermore, EdgeNN achieves significant time advantages compared to the direct execution of original programs. This improvement is attributed to better unified memory utilization (44.37%) and the innovative CPU-GPU hybrid execution approach (17.91%). Additionally, EdgeNN exhibits superior energy efficiency, providing 29.14× higher energy efficiency than edge CPUs and 5.70× higher energy efficiency than discrete GPUs. EdgeNN is now open source at <uri>https://github.com/ChenyangZhang-cs/EdgeNN</uri>.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 2","pages":"694-710"},"PeriodicalIF":5.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232037","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}

引用次数: 0

PKEST: Public-Key Encryption With Similarity Test for Medical Consortia Cloud Computing 医疗联盟云计算的公钥加密与相似度测试

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-04-08 DOI: 10.1109/TCC.2025.3558858

Junsong Chen;Shengke Zeng;Song Han;Jin Yin;Peng Chen

{"title":"PKEST: Public-Key Encryption With Similarity Test for Medical Consortia Cloud Computing","authors":"Junsong Chen;Shengke Zeng;Song Han;Jin Yin;Peng Chen","doi":"10.1109/TCC.2025.3558858","DOIUrl":"https://doi.org/10.1109/TCC.2025.3558858","url":null,"abstract":"Cloud computing eliminates the limitations of local hardware architecture while also enabling rapid data sharing between healthcare institutions. Encryption of electronic medical records (EMRs) before uploading to cloud servers is necessary for privacy. However, encryption brings challenges for computation. Public Key Encryption with Equality Test (PKEET) allows cloud servers to test the underlying message equality without decryption. Therefore, it can be used to classify the encrypted EMRs corresponding to different medical symptoms. However, traditional PKEETs have limitations in testing the similarity between the ciphertexts. Undoubtedly, it can not handle EMR classification with similar medical symptoms efficiently. In this work, we propose a lightweight public key encryption with similarity test (PKEST) for the EMR classification shared in medical consortia. Our scheme can resist offline message recovery attacks, which may be launched by the insider manager, and the traditional paring computation is not necessary. Our experiment simulation shows that the similarity error between ciphertext and plaintext is tiny when the parameters are set properly. Compared to previous works, our scheme not only achieves the classification of similar encrypted EMRs but is also more efficient than traditional PKEETs since our construction does not need paring computation anymore.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 2","pages":"680-693"},"PeriodicalIF":5.3,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230585","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}

引用次数: 0

Privacy-Preserving and Traceable Functional Encryption for Inner Product in Cloud Computing 云计算中内积的隐私保护和可追踪功能加密

IF 5.3 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-04-01 DOI: 10.1109/TCC.2025.3556925

Muyao Qiu;Jinguang Han;Feng Hao;Chao Sun;Ge Wu

{"title":"Privacy-Preserving and Traceable Functional Encryption for Inner Product in Cloud Computing","authors":"Muyao Qiu;Jinguang Han;Feng Hao;Chao Sun;Ge Wu","doi":"10.1109/TCC.2025.3556925","DOIUrl":"https://doi.org/10.1109/TCC.2025.3556925","url":null,"abstract":"Cloud computing is a distributed infrastructure that centralizes server resources on a platform in order to provide services over the internet. Traditional public-key encryption protects data confidentiality in cloud computing, while functional encryption provides a more fine-grained decryption method, which only reveals a function of the encrypted data. However, functional encryption in cloud computing faces the problem of key sharing. In order to trace malicious users who share keys with others, traceable FE-IP (TFE-IP) schemes were proposed where the key generation center (KGC) knows users’ identities and binds them with different secret keys. Nevertheless, existing schemes fail to protect the privacy of users’ identities. The fundamental challenge to construct a privacy-preserving TFE-IP scheme is that KGC needs to bind a key with a user's identity without knowing the identity. To balance privacy and accountability in cloud computing, we propose the concept of privacy-preserving traceable functional encryption for inner product (PPTFE-IP) and give a concrete construction which offers the features: (1) To prevent key sharing, both a user's identity and a vector are bound together in the key; (2) The KGC and a user execute a two-party secure computing protocol to generate a key without the former knowing anything about the latter's identity; (3) Each user can ensure the integrity and correctness of his/her key through verification; (4) The inner product of the two vectors embedded in a ciphertext and in his/her key can be calculated by an authorized user; (5) Only the tracer can trace the identity embedded in a key. We formally reduce the security of the proposed PPTFE-IP to well-known complexity assumptions, and conduct an implementation to evaluate its efficiency. The novelty of our scheme is to protect the user's privacy and provide traceability if required.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 2","pages":"667-679"},"PeriodicalIF":5.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232000","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}

引用次数: 0

Robin: An Efficient Hierarchical Federated Learning Framework via a Learning-Based Synchronization Scheme Robin：基于学习同步方案的高效分层联邦学习框架

IF 5 2区计算机科学

IEEE Transactions on Cloud Computing Pub Date : 2025-03-29 DOI: 10.1109/TCC.2025.3574823

Tianyu Qi;Yufeng Zhan;Peng Li;Yuanqing Xia

{"title":"Robin: An Efficient Hierarchical Federated Learning Framework via a Learning-Based Synchronization Scheme","authors":"Tianyu Qi;Yufeng Zhan;Peng Li;Yuanqing Xia","doi":"10.1109/TCC.2025.3574823","DOIUrl":"https://doi.org/10.1109/TCC.2025.3574823","url":null,"abstract":"Hierarchical federated learning (HFL) extends traditional federated learning by introducing a cloud-edge-device framework to enhance scalability. However, the challenge of determining when devices and edges should aggregate models remains unresolved, making the design of an effective synchronization scheme crucial. Additionally, the heterogeneity in computing and communication capabilities, coupled with non-independent and identically distributed (non-IID) data distributions, makes synchronization particularly complex. In this article, we propose <italic>Robin, a learning-based synchronization scheme for HFL systems. By collecting data such as models’ parameters, CPU usage, communication time, etc., we design a deep reinforcement learning-based approach to decide the frequencies of cloud aggregation and edge aggregation, respectively. The proposed scheme well considers device heterogeneity, non-IID data and device mobility, to maximize the training model accuracy while minimizing the energy overhead. Meanwhile, we prove the convergence of <italic>Robin’s synchronization scheme. And we build an HFL testbed and conduct the experiments with real data obtained from Raspberry Pi and Alibaba Cloud. Extensive experiments under various settings are conducted to confirm the effectiveness of <italic>Robin, which can improve 31.2% in model accuracy while reducing energy consumption by 36.4%.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"13 3","pages":"895-909"},"PeriodicalIF":5.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998281","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}

引用次数: 0