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.
{"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":null,"pages":null},"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}
Pub Date : 2024-03-12DOI: 10.1109/tcc.2024.3375801
Khlood Jastaniah, Ning Zhang, Mustafa A. Mustafa
{"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":"https://doi.org/10.1109/tcc.2024.3375801","url":null,"abstract":"","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":6.5,"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}
Pub Date : 2024-03-11DOI: 10.1109/TCC.2024.3374798
Kewei Wang;Changzhen Hu;Chun Shan
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.
{"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":null,"pages":null},"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}
Pub Date : 2024-03-10DOI: 10.1109/TCC.2024.3399616
Xueyu Hou;Yongjie Guan;Nakjung Choi;Tao Han
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 $8.2 times$ 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.
{"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":null,"pages":null},"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}
Pub Date : 2024-03-10DOI: 10.1109/TCC.2024.3398609
Zerui Wang;Yan Liu;Jun Huang
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.
本文介绍了一种基于开放式 API 的可解释人工智能（XAI）服务的设计，该服务可为云人工智能服务提供特征贡献解释。云人工智能服务被广泛用于开发具有精确学习指标的特定领域应用程序。然而，底层的云人工智能服务在模型如何产生预测方面仍然不透明。我们认为，XAI 操作可以作为开放式 API 访问，以便将 XAI 操作整合到云人工智能服务评估中。我们提出了一种使用微服务架构的设计，该架构可为云人工智能服务提供特征贡献解释，而无需展开云模型的网络结构。我们还可以利用这种架构来评估模型性能和 XAI 一致性指标，从而显示云人工智能服务的可信度。我们从运行管道中收集出处数据，以便在 XAI 服务中实现可重现性。此外，我们还介绍了针对领先云视觉人工智能服务的模型性能和 XAI 一致性指标进行实验测试的发现场景。结果证实，基于开放式应用程序接口的架构与云无关。此外，数据增强还能显著改善云人工智能服务的 XAI 一致性指标。
{"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":null,"pages":null},"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}
Pub Date : 2024-03-05DOI: 10.1109/TCC.2024.3372598
Lin Wan;Zhiquan Liu;Yong Ma;Yudan Cheng;Yongdong Wu;Runchuan Li;Jianfeng Ma
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.
{"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":null,"pages":null},"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}
Pub Date : 2024-03-05DOI: 10.1109/TCC.2024.3372814
Yao Zhao;Youyang Qu;Yong Xiang;Feifei Chen;Longxiang Gao
Supported by cloud computing, F ederated L earning (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 B lockchain-empowered F ederated L earning (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 C ontext-aware P roof- o f- C ontribution 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.
{"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":null,"pages":null},"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}
Pub Date : 2024-03-03DOI: 10.1109/TCC.2024.3396476
Dongxiang Yan;Mo-Yuen Chow;Yue Chen
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.
由于云计算和互联网服务的激增，数据中心（DC）见证了快速增长。数据中心巨大的电力需求和相关的碳排放对电力系统的可靠性和环境可持续性产生了巨大影响。本文提出了一种通过碳整合区位边际价格（CLMP）实现直流电低碳运行的双层模型。在上层，电力系统运营商依次求解最优电力流和碳排放流问题，以确定 CLMPs。在下层，为直流电开发了一个联合工作量分担和碳交易模型，以最大限度地降低其总体运营成本，同时将每个直流电的碳足迹控制在其碳限额内。为了解决双层模型并保护 DC 的隐私，我们提出了一种分段嵌入迭代法。它可以解决振荡问题，从而确保收敛。此外，我们还提出了一种基于过滤机制的分布式算法，以分布式方式解决底层 DC 问题，大大减少了通信开销。对小型和大型系统的案例研究证明了所提方法的有效性和优势。
{"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":null,"pages":null},"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}
Pub Date : 2024-03-02DOI: 10.1109/TCC.2024.3393735
Yuchao Zhang;Haoqiang Huang;Ahmed M. Abdelmoniem;Gaoxiong Zeng;Chenyue Zheng;Xirong Que;Wendong Wang;Ke Xu
Due to the fast developments of 5G and IoT technologies, Inter-Datacenter (Inter-DC) networks are facing unprecedented pressure to duplicate large volumes of geographically distributed user data in a real-time manner. Meanwhile, with the expansion of Inter-DC networks scale, link/node failures also become increasingly frequent, negatively affecting the data transmission efficiency. Therefore, link failure recovery methods become of utmost importance. Many works investigated fast failure recovery, yet none of them consider the deployment overhead of such recovery schemes. While in this article, we found that the side-effect of deploying recovery strategies and the future availability of the recovered transmissions are also crucial for fast recovery. So we propose a fast and low-redundancy failure recovery framework, FLAIR, which consists of a fast recovery strategy FRAVaR and a redundancy removal algorithm ROSE. FRAVaR takes full consideration of deployment overhead by minimizing shuffle traffic. On its base, ROSE regularly eliminates the cumulative rerouting redundancy by removing unnecessary routing updates. The experiment results on 4 realistic network topologies show that FLAIR successfully reduces up to 48.2% deployment overhead compared with the state-of-the-art solutions, and thus reduces up to 70.2% recovery speed and improves up to 36% network utilization.
{"title":"FLAIR: A Fast and Low-Redundancy Failure Recovery Framework for Inter Data Center Network","authors":"Yuchao Zhang;Haoqiang Huang;Ahmed M. Abdelmoniem;Gaoxiong Zeng;Chenyue Zheng;Xirong Que;Wendong Wang;Ke Xu","doi":"10.1109/TCC.2024.3393735","DOIUrl":"10.1109/TCC.2024.3393735","url":null,"abstract":"Due to the fast developments of 5G and IoT technologies, Inter-Datacenter (Inter-DC) networks are facing unprecedented pressure to duplicate large volumes of geographically distributed user data in a real-time manner. Meanwhile, with the expansion of Inter-DC networks scale, link/node failures also become increasingly frequent, negatively affecting the data transmission efficiency. Therefore, link failure recovery methods become of utmost importance. Many works investigated fast failure recovery, yet none of them consider the deployment overhead of such recovery schemes. While in this article, we found that the side-effect of deploying recovery strategies and the future availability of the recovered transmissions are also crucial for fast recovery. So we propose a fast and low-redundancy failure recovery framework, FLAIR, which consists of a fast recovery strategy FRAVaR and a redundancy removal algorithm ROSE. FRAVaR takes full consideration of deployment overhead by minimizing shuffle traffic. On its base, ROSE regularly eliminates the cumulative rerouting redundancy by removing unnecessary routing updates. The experiment results on 4 realistic network topologies show that FLAIR successfully reduces up to 48.2% deployment overhead compared with the state-of-the-art solutions, and thus reduces up to 70.2% recovery speed and improves up to 36% network utilization.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140836776","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}
Pub Date : 2024-03-01DOI: 10.1109/TCC.2024.3372370
Bishakh Chandra Ghosh;Sandip Chakraborty
Multi-cloud environments such as OnApp and Cloudflare have turned the cloud marketplace towards a new horizon where end-users can host applications transparently over different cloud service providers (CSPs) simultaneously by taking the best from each. Existing cloud federations are typically driven by a broker service which provides a trusted interface allowing the participant CSPs and end-users to coordinate. However, such a broker has the limitations of any centralized trusted authority like risk of manipulation, bias, censorship, single point of failure, etc. In this paper, we propose a decentralized trustless cloud federation architecture called CollabCloud which eliminates any central mediator while addressing the challenges introduced by byzantine participants. CollabCloud utilizes blockchain, and introduces a novel interoperability protocol bridging a permissionless blockchain as an open interface for the end-users, and a permissioned blockchain as a coordination platform for the CSPs. We have implemented CollabCloud with Ethereum, Hyperledger Fabric and Burrow platforms. Experiments with a proof-of-concept testbed emulating 3 CSPs show that CollabCloud can operate within an acceptable response latency for resource allocation, while scaling upto 64 parallel requests per second. Scalability analysis over Mininet emulation platform indicates that the platform can scale well with minimal impact on the response latency as the number of participating CSPs increases.
{"title":"Trustless Collaborative Cloud Federation","authors":"Bishakh Chandra Ghosh;Sandip Chakraborty","doi":"10.1109/TCC.2024.3372370","DOIUrl":"10.1109/TCC.2024.3372370","url":null,"abstract":"Multi-cloud environments such as OnApp and Cloudflare have turned the cloud marketplace towards a new horizon where end-users can host applications transparently over different cloud service providers (CSPs) simultaneously by taking the best from each. Existing cloud federations are typically driven by a broker service which provides a trusted interface allowing the participant CSPs and end-users to coordinate. However, such a broker has the limitations of any centralized trusted authority like risk of manipulation, bias, censorship, single point of failure, etc. In this paper, we propose a decentralized trustless cloud federation architecture called \u0000<italic>CollabCloud</i>\u0000 which eliminates any central mediator while addressing the challenges introduced by byzantine participants. \u0000<italic>CollabCloud</i>\u0000 utilizes blockchain, and introduces a novel interoperability protocol bridging a permissionless blockchain as an open interface for the end-users, and a permissioned blockchain as a coordination platform for the CSPs. We have implemented \u0000<italic>CollabCloud</i>\u0000 with Ethereum, Hyperledger Fabric and Burrow platforms. Experiments with a proof-of-concept testbed emulating 3 CSPs show that \u0000<italic>CollabCloud</i>\u0000 can operate within an acceptable response latency for resource allocation, while scaling upto 64 parallel requests per second. Scalability analysis over Mininet emulation platform indicates that the platform can scale well with minimal impact on the response latency as the number of participating CSPs increases.","PeriodicalId":13202,"journal":{"name":"IEEE Transactions on Cloud Computing","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140018441","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：