IEEE Transactions on Cloud Computing最新文献

{"title":"Enhancing Long-Term Cloud Workload Forecasting Framework: Anomaly Handling and Ensemble Learning in Multivariate Time Series","authors":"Yeong-Min Kim;Seunghwan Song;Byoung-Mo Koo;Jeena Son;Yeseul Lee;Jun-Geol Baek","doi":"10.1109/TCC.2024.3400859","DOIUrl":"10.1109/TCC.2024.3400859","url":null,"abstract":"Forecasting workloads and responding promptly with resource scaling and migration is critical to optimizing operations and enhancing resource management in cloud environments. However, the diverse and dynamic nature of devices within cloud environments complicates workload forecasting. These challenges often lead to service level agreement violations or inefficient resource usage. Hence, this paper proposes an Enhanced Long-Term Cloud Workload Forecasting (E-LCWF) framework designed specifically for efficient resource management in these heterogeneous and dynamic environments. The E-LCWF framework processes individual resource workloads as multivariate time series and enhances model performance through anomaly detection and handling. Additionally, the E-LCWF framework employs an error-based ensemble approach, using transformer-based models and Long-Term Time Series Forecasting (LTSF) linear models, each of which has demonstrated exceptional performance in LTSF. Experimental results obtained using virtual machine data from real-world management information systems and manufacturing execution systems show that the E-LCWF framework outperforms state-of-the-art models in forecasting accuracy.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 2","pages":"789-799"},"PeriodicalIF":6.5,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063993","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":"Comments on “Privacy Aware Data Deduplication for Side Channel in Cloud Storage”","authors":"Xin Tang;Yudan Zhu;Mingjun Fu","doi":"10.1109/TCC.2024.3376996","DOIUrl":"10.1109/TCC.2024.3376996","url":null,"abstract":"Cross-user deduplication is an emerging technique to eliminate uploading of redundant data in cloud storage. Even though it is able to improve storage and communication efficiency simultaneously, it suffers from the problem of privacy leakage by side channel attack, which is a major obstacle to the practical application of this technique. In order to achieve a secure cross-user deduplication, Yu et al. recently proposed a zero-knowledge response (ZEUS) scheme, together with an advanced countermeasure ZEUS\u0000<inline-formula><tex-math>$^mathrm{+}$</tex-math></inline-formula>\u0000 by combining ZEUS and the random threshold solution, each of which is claimed to be secure against side channel attack. However, in this paper we show that both ZEUS and ZEUS\u0000<inline-formula><tex-math>$^mathrm{+}$</tex-math></inline-formula>\u0000 are easily subject to a random chunk generation attack, which in turn undermines the claimed security. Furthermore, we also propose a simple but effective method to improve the existing schemes.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 2","pages":"814-817"},"PeriodicalIF":6.5,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140129398","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":"Achieving Privacy-Preserving Online Multi-Layer Perceptron Model in Smart Grid","authors":"Chunqiang Hu;Huijun Zhuang;Jiajun Chen;Pengfei Hu;Tao Xiang;Jiguo Yu","doi":"10.1109/TCC.2024.3399771","DOIUrl":"10.1109/TCC.2024.3399771","url":null,"abstract":"With the development of Big Data technology, the power industry has also entered the data-driven intelligence era. Cloud computing-based smart grids give the power industry stronger capabilities in data analytics. Electricity load forecasting in the cloud helps smart grids allocate resources appropriately. However, the users’ privacy is easily compromised in the load forecasting process with cloud computing. The electricity usage data collected by the system may contain sensitive information about the users, which could lead to serious privacy leakage. In order to solve the issues, we propose a novel privacy-preserving cloud-aided load forecasting scheme for the cloud computing-based smart grid. It contains a secure online training algorithm and an efficient real-time forecasting algorithm. Meanwhile, the two-party interaction security scheme is more suitable for real-world applications. Before being sent to the cloud server, the control center of the smart grids encrypts the data using homomorphic encryption. During the process of model training and forecasting, the data remains securely encrypted at all times to avoid the risk of data privacy breaches. Finally, security and experimental analyses show that our scheme effectively avoids privacy leakage while reducing resource consumption.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 2","pages":"777-788"},"PeriodicalIF":6.5,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140934347","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":"Efficient User-Centric Privacy-Friendly and Flexible Wearable Data Aggregation and Sharing","authors":"Khlood Jastaniah;Ning Zhang;Mustafa A. Mustafa","doi":"10.1109/TCC.2024.3375801","DOIUrl":"10.1109/TCC.2024.3375801","url":null,"abstract":"Wearable devices can offer services to individuals and the public. However, wearable data collected by cloud providers may pose privacy risks. To reduce these risks while maintaining full functionality, healthcare systems require solutions for privacy-friendly data processing and sharing that can accommodate three main use cases: (i) data owners requesting processing of their own data, and multiple data requesters requesting data processing of (ii) a single or (iii) multiple data owners. Existing work lacks data owner access control and does not efficiently support these cases, making them unsuitable for wearable devices. To address these limitations, we propose a novel, efficient, user-centric, privacy-friendly, and flexible data aggregation and sharing scheme, named SAMA. SAMA uses a multi-key partial homomorphic encryption scheme to allow flexibility in accommodating the aggregation of data originating from a single or multiple data owners while preserving privacy during the processing. It also uses ciphertext-policy attribute-based encryption scheme to support fine-grain sharing with multiple data requesters based on user-centric access control. Formal security analysis shows that SAMA supports data confidentiality and authorisation. SAMA has also been analysed in terms of computational and communication overheads. Our experimental results demonstrate that SAMA supports privacy-preserving flexible data aggregation more efficiently than the relevant state-of-the-art solutions.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 4","pages":"967-982"},"PeriodicalIF":5.3,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140115315","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":"Evaluation of Application Layer DDoS Attack Effect in Cloud Native Applications","authors":"Kewei Wang;Changzhen Hu;Chun Shan","doi":"10.1109/TCC.2024.3374798","DOIUrl":"10.1109/TCC.2024.3374798","url":null,"abstract":"Cloud native application is especially susceptible to application layer DDoS attack. This attributes to the internal service calls, by which microservices cooperate and communicate with each other, amplifying the effect of application layer DDoS attack. Since different services have varying degrees of sensitivity to an attack, a sophisticated attacker can take advantage of those especially expensive API calls to produce serious damage to the availability of services and applications with ease. To better analyze the severity of and mitigate application layer DDoS attacks in cloud native applications, we propose a novel method to evaluate the effect of application layer DDoS attack, that is able to quantitatively characterize the amplifying effect introduced by the complex structure of application system. We first present the descriptive model of the scenario. Then, Riemannian manifolds are constructed as the state spaces of the attack scenarios, in which attacks are described as homeomorphisms. Finally, we apply differential geometry principles to quantitatively calculate the attack effect, which is derived from the action of an attack and the movement it produces in the state spaces. The proposed method is validated in various application scenarios. We show that our approach provides accurate evaluation results, and outperforms existing solutions.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 2","pages":"522-538"},"PeriodicalIF":6.5,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140105636","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":"BPS: Batching, Pipelining, Surgeon of Continuous Deep Inference on Collaborative Edge Intelligence","authors":"Xueyu Hou;Yongjie Guan;Nakjung Choi;Tao Han","doi":"10.1109/TCC.2024.3399616","DOIUrl":"10.1109/TCC.2024.3399616","url":null,"abstract":"Users on edge generate deep inference requests continuously over time. Mobile/edge devices located near users can undertake the computation of inference locally for users, e.g., the embedded edge device on an autonomous vehicle. Due to limited computing resources on one mobile/edge device, it may be challenging to process the inference requests from users with high throughput. An attractive solution is to (partially) offload the computation to a remote device in the network. In this paper, we examine the existing inference execution solutions across local and remote devices and propose an adaptive scheduler, a BPS scheduler, for continuous deep inference on collaborative edge intelligence. By leveraging data parallel, neurosurgeon, reinforcement learning techniques, BPS can boost the overall inference performance by up to \u0000<inline-formula><tex-math>$8.2 times$</tex-math></inline-formula>\u0000 over the baseline schedulers. A lightweight compressor, FF, specialized in compressing intermediate output data for neurosurgeon, is proposed and integrated into the BPS scheduler. FF exploits the operating character of convolutional layers and utilizes efficient approximation algorithms. Compared to existing compression methods, FF achieves up to 86.9% lower accuracy loss and up to 83.6% lower latency overhead.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 3","pages":"830-843"},"PeriodicalIF":5.3,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140933079","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 Open API Architecture to Discover the Trustworthy Explanation of Cloud AI Services","authors":"Zerui Wang;Yan Liu;Jun Huang","doi":"10.1109/TCC.2024.3398609","DOIUrl":"10.1109/TCC.2024.3398609","url":null,"abstract":"This article presents the design of an open-API-based explainable AI (XAI) service to provide feature contribution explanations for cloud AI services. Cloud AI services are widely used to develop domain-specific applications with precise learning metrics. However, the underlying cloud AI services remain opaque on how the model produces the prediction. We argue that XAI operations are accessible as open APIs to enable the consolidation of the XAI operations into the cloud AI services assessment. We propose a design using a microservice architecture that offers feature contribution explanations for cloud AI services without unfolding the network structure of the cloud models. We can also utilize this architecture to evaluate the model performance and XAI consistency metrics showing cloud AI services’ trustworthiness. We collect provenance data from operational pipelines to enable reproducibility within the XAI service. Furthermore, we present the discovery scenarios for the experimental tests regarding model performance and XAI consistency metrics for the leading cloud vision AI services. The results confirm that the architecture, based on open APIs, is cloud-agnostic. Additionally, data augmentations result in measurable improvements in XAI consistency metrics for cloud AI services.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 2","pages":"762-776"},"PeriodicalIF":6.5,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140933071","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":"Lightweight and Privacy-Preserving Dual Incentives for Mobile Crowdsensing","authors":"Lin Wan;Zhiquan Liu;Yong Ma;Yudan Cheng;Yongdong Wu;Runchuan Li;Jianfeng Ma","doi":"10.1109/TCC.2024.3372598","DOIUrl":"10.1109/TCC.2024.3372598","url":null,"abstract":"Incentive plays an important role in mobile crowdsensing (MCS), as it impels mobile users to participate in sensing tasks and provide high-quality sensing data. However, considering the privacy (including identity privacy, sensing data privacy, and reputation value privacy) and practicality (including reliability, quality awareness, and efficiency) issues in practice, it is a challenge to design such an effective incentive scheme for MCS applications. Existing studies either fail to provide adequate privacy-preserving capabilities or have low practicality. To address these issues, we propose a scheme called BRRV in MCS which relies on two rounds of range reliability assessment to guarantee the reliability of data while achieving privacy preservation. In addition, we also present a lightweight scheme called LRRV in MCS which relies on a single round of range reliability assessment to guarantee the reliability of data while achieving lightweight and privacy preservation. Moreover, to fairly stimulate participants, constrain participants’ malicious behavior, and improve the probability of high-quality data, we design a quality-aware reputation-based reward and penalty strategy to achieve dual incentives (including money incentives and reputation incentives) for participants. Furthermore, comprehensive theoretical analysis and experimental evaluation demonstrate that our proposed schemes are significantly superior to the existing schemes in several aspects.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 2","pages":"504-521"},"PeriodicalIF":6.5,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140045412","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":"Context-Aware Consensus Algorithm for Blockchain-Empowered Federated Learning","authors":"Yao Zhao;Youyang Qu;Yong Xiang;Feifei Chen;Longxiang Gao","doi":"10.1109/TCC.2024.3372814","DOIUrl":"10.1109/TCC.2024.3372814","url":null,"abstract":"Supported by cloud computing, \u0000<underline>F</u>\u0000ederated \u0000<underline>L</u>\u0000earning (FL) has experienced rapid advancement, as a promising technique to motivate clients to collaboratively train models without sharing local data. To improve the security and fairness of FL implementation, numerous \u0000<underline>B</u>\u0000lockchain-empowered \u0000<underline>F</u>\u0000ederated \u0000<underline>L</u>\u0000earning (BFL) frameworks have emerged accordingly. Among them, consensus algorithms play a pivotal role in determining the scalability, security, and consistency of BFL systems. Existing consensus solutions to block producer selection and reward allocation either focus on well-resourced scenarios or accommodate BFL based on clients’ contributions to model training. However, these approaches limit consensus efficiency and undermine reward fairness, due to involving intricate consensus processes, disregarding clients’ contributions during blockchain consensus, and failing to address lazy client problems (malicious clients plagiarizing local model updates from others to reap rewards). Given the aforementioned challenges, we make the first attempt to design a joint solution for efficient consensus and fair reward allocation in heterogeneous BFL systems with lazy clients. Specifically, we introduce a generalizable BFL workflow that can address lazy client problems well. Based on it, the global contribution of BFL clients is decoupled into five dominant metrics, and the block producer selection problem is formulated as a reward-constraint contribution maximization problem. By addressing this problem, the optimal block producer that maximizes global contribution can be identified to orchestrate consensus processes, and rewards are distributed to clients in proportion to their respective global contributions. To achieve it, we develop a \u0000<underline>C</u>\u0000ontext-aware \u0000<underline>P</u>\u0000roof-\u0000<underline>o</u>\u0000f-\u0000<underline>C</u>\u0000ontribution consensus algorithm named CPoC to reach consensus and incentive simultaneously, followed by theoretical analysis of lazy client problems and privacy issues. Empirical results on widely-used datasets demonstrate the effectiveness of our design in improving consensus efficiency and maximizing global contribution.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 2","pages":"491-503"},"PeriodicalIF":6.5,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140045414","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":"Low-Carbon Operation of Data Centers With Joint Workload Sharing and Carbon Allowance Trading","authors":"Dongxiang Yan;Mo-Yuen Chow;Yue Chen","doi":"10.1109/TCC.2024.3396476","DOIUrl":"10.1109/TCC.2024.3396476","url":null,"abstract":"Data centers (DCs) have witnessed rapid growth due to the proliferation of cloud computing and internet services. The huge electricity demand and the associated carbon emissions of DCs have great impacts on power system reliability and environmental sustainability. This paper proposes a bilevel model for low-carbon operation of DCs via carbon-integrated locational marginal prices (CLMPs). In the upper level, the power system operator sequentially solves the optimal power flow and the carbon emission flow problems to determine the CLMPs. In the lower level, a joint workload sharing and carbon trading model for DCs is developed to minimize their overall operation cost while keeping each DC's carbon footprint within its carbon allowance. To solve the bilevel model and preserve the privacy of DCs, we propose a bisection-embedded iterative method. It can tackle the issue of oscillation, thereby ensuring convergence. In addition, a filtering mechanism-based distributed algorithm is proposed to solve the lower-level DC problem in a distributed manner with much reduced communication overhead. Case studies on both small-scale and large-scale systems demonstrate the effectiveness and benefits of the proposed method.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":"12 2","pages":"750-761"},"PeriodicalIF":6.5,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140836754","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}