Pub Date : 2024-01-09DOI: 10.1109/TSUSC.2024.3351684
Jie Li;Yuhui Deng;Zhifeng Fan;Zijie Zhong;Geyong Min
The explosion of large-scale data has increased the scale and capacity of storage clusters in data centers, leading to huge power consumption issues. Cloud providers can effectively promote the energy efficiency of data centers by employing energy-aware data placement techniques, which primarily encompass storage cluster's power and cooling power. Traditional data placement approaches do not diminish the overall power consumption of the data center due to the heat recirculation effect between storage nodes. To fill this gap, we build an elaborate thermal-aware data center model. Then we propose two energy-efficient thermal-aware data placement strategies, ETDP-I and ETDP-II, to reduce the overall power consumption of the data center. The principle of our proposed algorithm is to utilize a greedy algorithm to calculate the optimal disk sequence at the minimum total power of the data center and then place the data into the optimal disk sequence. We implement these two strategies in a cloud computing simulation platform based on CloudSim. Experimental results unveil that ETDA-I and ETDP-II outperform MinTin-G and MinTout-G in terms of the supplied temperature of CRAC, storage nodes power, cooling cost, and total power consumption of the data center. In particular, ETDP-I and ETDP-II algorithms can save about 9.46$%$-38.93$%$ of the overall power consumption compared to MinTout-G and MinTin-G algorithms.
{"title":"Towards Energy-Efficient and Thermal-Aware Data Placement for Storage Clusters","authors":"Jie Li;Yuhui Deng;Zhifeng Fan;Zijie Zhong;Geyong Min","doi":"10.1109/TSUSC.2024.3351684","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3351684","url":null,"abstract":"The explosion of large-scale data has increased the scale and capacity of storage clusters in data centers, leading to huge power consumption issues. Cloud providers can effectively promote the energy efficiency of data centers by employing energy-aware data placement techniques, which primarily encompass storage cluster's power and cooling power. Traditional data placement approaches do not diminish the overall power consumption of the data center due to the heat recirculation effect between storage nodes. To fill this gap, we build an elaborate thermal-aware data center model. Then we propose two energy-efficient thermal-aware data placement strategies, ETDP-I and ETDP-II, to reduce the overall power consumption of the data center. The principle of our proposed algorithm is to utilize a greedy algorithm to calculate the optimal disk sequence at the minimum total power of the data center and then place the data into the optimal disk sequence. We implement these two strategies in a cloud computing simulation platform based on CloudSim. Experimental results unveil that ETDA-I and ETDP-II outperform MinTin-G and MinTout-G in terms of the supplied temperature of CRAC, storage nodes power, cooling cost, and total power consumption of the data center. In particular, ETDP-I and ETDP-II algorithms can save about 9.46\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000-38.93\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000 of the overall power consumption compared to MinTout-G and MinTin-G algorithms.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 4","pages":"631-647"},"PeriodicalIF":3.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-01-09DOI: 10.1109/TSUSC.2024.3351282
Tao Liu;Peng Li;Zhou Su;Mianxiong Dong
Graph neural networks (GNNs) have attracted significant research attention because of their impressive capability in dealing with graph-structure data, such as energy networks, that are crucial for sustainable computing. We find that the communication of data loading from main memory to GPUs is the main bottleneck of GNN inference because of redundant data loading. In this paper, we propose RAIN, an efficient GNN inference system for graph learning. There are two key designs. First, we explore the opportunity of conducting similar inference batches sequentially and reusing repeated nodes among adjacent batches to reduce redundant data loading. This method requires reordering the batches based on their similarity. However, comparing the similarity across a large number of inference batches is a difficult task with a high computational cost. Thus, we propose a local sensitive hash (LSH)-based clustering scheme to group similar batches together quickly without pair-wise comparison. Second, RAIN contains an efficient adaptive sampling strategy, allowing users to sample target nodes’ neighbors according to their degree. The number of sampled neighbors is proportional to the size of the node's degree. We conduct extensive experiments with various baselines. RAIN can achieve up to 6.8X acceleration, and the accuracy decrease is smaller than 0.1%.
{"title":"Efficient Inference of Graph Neural Networks Using Local Sensitive Hash","authors":"Tao Liu;Peng Li;Zhou Su;Mianxiong Dong","doi":"10.1109/TSUSC.2024.3351282","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3351282","url":null,"abstract":"Graph neural networks (GNNs) have attracted significant research attention because of their impressive capability in dealing with graph-structure data, such as energy networks, that are crucial for sustainable computing. We find that the communication of data loading from main memory to GPUs is the main bottleneck of GNN inference because of redundant data loading. In this paper, we propose RAIN, an efficient GNN inference system for graph learning. There are two key designs. First, we explore the opportunity of conducting similar inference batches sequentially and reusing repeated nodes among adjacent batches to reduce redundant data loading. This method requires reordering the batches based on their similarity. However, comparing the similarity across a large number of inference batches is a difficult task with a high computational cost. Thus, we propose a local sensitive hash (LSH)-based clustering scheme to group similar batches together quickly without pair-wise comparison. Second, RAIN contains an efficient adaptive sampling strategy, allowing users to sample target nodes’ neighbors according to their degree. The number of sampled neighbors is proportional to the size of the node's degree. We conduct extensive experiments with various baselines. RAIN can achieve up to 6.8X acceleration, and the accuracy decrease is smaller than 0.1%.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"548-558"},"PeriodicalIF":3.9,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141264414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-01-05DOI: 10.1109/TSUSC.2024.3350343
Yuan Su;Yuheng Wang;Jiliang Li;Zhou Su;Witold Pedrycz;Qinnan Hu
The Public Key Infrastructure (PKI) system is the cornerstone of today’s security communications. All users in the service domain covered by the same PKI system are able to authenticate each other before exchanging messages. However, there is identity isolation in different domains, making the identity of users in different domains cannot be recognized by PKI systems in other domains. To achieve cross-domain authentication, the consortium blockchain system is leveraged in the existing schemes. Unfortunately, the consortium blockchain-based authentication schemes have the following challenges: high cost, privacy concerns, scalability and economic unsustainability. To solve these challenges, we propose a scalable and privacy-preserving cross-domain authentication scheme called Bifrost-Auth. Firstly, Bifrost-Auth is designed to use a decentralized oracle to directly interact with blockchains in different domains instead of maintaining a consortium blockchain and enables mutual authentication for users lying in different domains. Secondly, users can succinctly authenticate their membership of the domain by the accumulator technique, where the membership proof is turned into zero knowledge to protect users’ privacy. Finally, Bifrost-Auth is proven to be secure against various attacks, and thorough experiments are carried out and demonstrate the security and efficiency of Bifrost-Auth.
{"title":"Oracle Based Privacy-Preserving Cross-Domain Authentication Scheme","authors":"Yuan Su;Yuheng Wang;Jiliang Li;Zhou Su;Witold Pedrycz;Qinnan Hu","doi":"10.1109/TSUSC.2024.3350343","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3350343","url":null,"abstract":"The Public Key Infrastructure (PKI) system is the cornerstone of today’s security communications. All users in the service domain covered by the same PKI system are able to authenticate each other before exchanging messages. However, there is identity isolation in different domains, making the identity of users in different domains cannot be recognized by PKI systems in other domains. To achieve cross-domain authentication, the consortium blockchain system is leveraged in the existing schemes. Unfortunately, the consortium blockchain-based authentication schemes have the following challenges: high cost, privacy concerns, scalability and economic unsustainability. To solve these challenges, we propose a scalable and privacy-preserving cross-domain authentication scheme called Bifrost-Auth. Firstly, Bifrost-Auth is designed to use a decentralized oracle to directly interact with blockchains in different domains instead of maintaining a consortium blockchain and enables mutual authentication for users lying in different domains. Secondly, users can succinctly authenticate their membership of the domain by the accumulator technique, where the membership proof is turned into zero knowledge to protect users’ privacy. Finally, Bifrost-Auth is proven to be secure against various attacks, and thorough experiments are carried out and demonstrate the security and efficiency of Bifrost-Auth.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 4","pages":"602-614"},"PeriodicalIF":3.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
5G vehicular networks provide abundant multimedia services among mobile vehicles. However, due to the mobility of vehicles, large-scale mobile traffic poses a challenge to the core network load and transmission latency. It is difficult for existing solutions to guarantee the quality of service (QoS) of vehicular networks. Besides, the sensitivity of vehicle trajectories also brings privacy concerns in vehicular networks. To address these problems, we propose a privacy-preserving and trajectory clustering-based framework for cooperative caching optimization (PTCC) in vehicular networks, which includes two tasks. Specifically, in the first task, we first apply differential privacy technologies to add noise to vehicle trajectories. In addition, a data aggregation model is provided to make the trade-off between aggregation accuracy and privacy protection. In order to analyze similar behavioral vehicles, trajectory clustering is then achieved by utilizing machine learning algorithms. In the second task, we construct a cooperative caching objective function with the transmission latency. Afterwards, the multi-agent deep Q network (MADQN) is leveraged to obtain the goal of caching optimization, which can achieve low delay. Finally, extensive simulation results verify that our framework respectively improves the QoS up to 9.8% and 12.8% with different file numbers and caching capacities, compared with other state-of-the-art solutions.
{"title":"PTCC: A Privacy-Preserving and Trajectory Clustering-Based Approach for Cooperative Caching Optimization in Vehicular Networks","authors":"Tengfei Cao;Zizhen Zhang;Xiaoying Wang;Han Xiao;Changqiao Xu","doi":"10.1109/TSUSC.2024.3350386","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3350386","url":null,"abstract":"5G vehicular networks provide abundant multimedia services among mobile vehicles. However, due to the mobility of vehicles, large-scale mobile traffic poses a challenge to the core network load and transmission latency. It is difficult for existing solutions to guarantee the quality of service (QoS) of vehicular networks. Besides, the sensitivity of vehicle trajectories also brings privacy concerns in vehicular networks. To address these problems, we propose a privacy-preserving and trajectory clustering-based framework for cooperative caching optimization (PTCC) in vehicular networks, which includes two tasks. Specifically, in the first task, we first apply differential privacy technologies to add noise to vehicle trajectories. In addition, a data aggregation model is provided to make the trade-off between aggregation accuracy and privacy protection. In order to analyze similar behavioral vehicles, trajectory clustering is then achieved by utilizing machine learning algorithms. In the second task, we construct a cooperative caching objective function with the transmission latency. Afterwards, the multi-agent deep Q network (MADQN) is leveraged to obtain the goal of caching optimization, which can achieve low delay. Finally, extensive simulation results verify that our framework respectively improves the QoS up to 9.8% and 12.8% with different file numbers and caching capacities, compared with other state-of-the-art solutions.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 4","pages":"615-630"},"PeriodicalIF":3.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-01-05DOI: 10.1109/TSUSC.2024.3350040
Meng Chen;Hengzhu Liu;Huanhuan Chi;Ping Xiong
Multi-party collaborative learning has become a paradigm for large-scale knowledge discovery in the era of Big Data. As a typical form of collaborative learning, federated learning (FL) has received widespread research attention in recent years. In practice, however, FL faces a range of challenges such as objective inconsistency, communication and synchronization issues, due to the heterogeneity in the clients’ local datasets and devices. In this paper, we propose EnsembleFed, a novel ensemble framework for heterogeneous FL. The proposed framework first allows each client to train a local model with full autonomy and without having to consider the heterogeneity of local datasets. The confidence scores of training samples output by each local model are then perturbed to defend against membership inference attacks, after which they are submitted to the server for use in constructing the global model. We apply a GAN-based method to generate calibrated noise for confidence perturbation. Benefiting from the ensemble framework, EnsembleFed disengages from the restriction of real-time synchronization and achieves collaborative learning with lower communication costs than traditional FL. Experiments on real-world datasets demonstrate that the proposed EnsembleFed can significantly improve the performance of the global model while also effectively defending against membership inference attacks.
{"title":"Heterogeneous Ensemble Federated Learning With GAN-Based Privacy Preservation","authors":"Meng Chen;Hengzhu Liu;Huanhuan Chi;Ping Xiong","doi":"10.1109/TSUSC.2024.3350040","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3350040","url":null,"abstract":"Multi-party collaborative learning has become a paradigm for large-scale knowledge discovery in the era of Big Data. As a typical form of collaborative learning, federated learning (FL) has received widespread research attention in recent years. In practice, however, FL faces a range of challenges such as objective inconsistency, communication and synchronization issues, due to the heterogeneity in the clients’ local datasets and devices. In this paper, we propose EnsembleFed, a novel ensemble framework for heterogeneous FL. The proposed framework first allows each client to train a local model with full autonomy and without having to consider the heterogeneity of local datasets. The confidence scores of training samples output by each local model are then perturbed to defend against membership inference attacks, after which they are submitted to the server for use in constructing the global model. We apply a GAN-based method to generate calibrated noise for confidence perturbation. Benefiting from the ensemble framework, EnsembleFed disengages from the restriction of real-time synchronization and achieves collaborative learning with lower communication costs than traditional FL. Experiments on real-world datasets demonstrate that the proposed EnsembleFed can significantly improve the performance of the global model while also effectively defending against membership inference attacks.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 4","pages":"591-601"},"PeriodicalIF":3.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
During the winter season in India, the AQI experiences a decrease due to the limited dispersion of APs caused by MFs. Therefore, we developed a sophisticated green predictive model GAP, which utilizes our designed green technique and a customized big dataset. This dataset is derived from weather research and tailored to forecast future AQI levels in the Indian subcontinent during winter. This dataset has been meticulously curated by amalgamating samples of APs and MFs concentrations, further adjusted to reflect the yearly activity data across various Indian states. The dataset reveals an amplified national emissions rate for $boldsymbol {PM_{2.5}}$, $boldsymbol {NO_{2}}$, and $boldsymbol {CO}$ pollutants, exhibiting an increase of 3.6%, 1.3%, and 2.5% in gigagrams per day. ML/DL regressors are then applied to this dataset, with the most effective ML/DL regressors being selected based on their performance. Our paper encompasses an exhaustive examination of existing literature within the realm of air pollution epidemiology. The evaluation results demonstrate that the prediction accuracy of GAP when utilizing LSTM, CNN, MLP, and RNN achieve accuracies of 98.53%, 95.9222%, 96.1555%, and 97.344% in predicting the $boldsymbol {PM_{2.5}}$, $boldsymbol {NO_{2}}$, and $boldsymbol {CO}$ concentrations. In contrast, RF, KNN, and SVR yield lower accuracies of 92.511%, 90.333%, and 93.566% for the same AQIs.
{"title":"Apict:Air Pollution Epidemiology Using Green AQI Prediction During Winter Seasons in India","authors":"Sweta Dey;Kalyan Chatterjee;Ramagiri Praveen Kumar;Anjan Bandyopadhyay;Sujata Swain;Neeraj Kumar","doi":"10.1109/TSUSC.2023.3343922","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3343922","url":null,"abstract":"During the winter season in India, the AQI experiences a decrease due to the limited dispersion of APs caused by MFs. Therefore, we developed a sophisticated green predictive model GAP, which utilizes our designed green technique and a customized big dataset. This dataset is derived from weather research and tailored to forecast future AQI levels in the Indian subcontinent during winter. This dataset has been meticulously curated by amalgamating samples of APs and MFs concentrations, further adjusted to reflect the yearly activity data across various Indian states. The dataset reveals an amplified national emissions rate for \u0000<inline-formula><tex-math>$boldsymbol {PM_{2.5}}$</tex-math></inline-formula>\u0000, \u0000<inline-formula><tex-math>$boldsymbol {NO_{2}}$</tex-math></inline-formula>\u0000, and \u0000<inline-formula><tex-math>$boldsymbol {CO}$</tex-math></inline-formula>\u0000 pollutants, exhibiting an increase of 3.6%, 1.3%, and 2.5% in gigagrams per day. ML/DL regressors are then applied to this dataset, with the most effective ML/DL regressors being selected based on their performance. Our paper encompasses an exhaustive examination of existing literature within the realm of air pollution epidemiology. The evaluation results demonstrate that the prediction accuracy of GAP when utilizing LSTM, CNN, MLP, and RNN achieve accuracies of 98.53%, 95.9222%, 96.1555%, and 97.344% in predicting the \u0000<inline-formula><tex-math>$boldsymbol {PM_{2.5}}$</tex-math></inline-formula>\u0000, \u0000<inline-formula><tex-math>$boldsymbol {NO_{2}}$</tex-math></inline-formula>\u0000, and \u0000<inline-formula><tex-math>$boldsymbol {CO}$</tex-math></inline-formula>\u0000 concentrations. In contrast, RF, KNN, and SVR yield lower accuracies of 92.511%, 90.333%, and 93.566% for the same AQIs.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"559-570"},"PeriodicalIF":3.9,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141264521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-12-29DOI: 10.1109/TSUSC.2023.3348157
Muhammed Golec;Sukhpal Singh Gill;Felix Cuadrado;Ajith Kumar Parlikad;Minxian Xu;Huaming Wu;Steve Uhlig
Serverless edge computing decreases unnecessary resource usage on end devices with limited processing power and storage capacity. Despite its benefits, serverless edge computing's zero scalability is the major source of the cold start delay, which is yet unsolved. This latency is unacceptable for time-sensitive Internet of Things (IoT) applications like autonomous cars. Most existing approaches need containers to idle and use extra computing resources. Edge devices have fewer resources than cloud-based systems, requiring new sustainable solutions. Therefore, we propose an AI-powered, sustainable resource management framework called ATOM for serverless edge computing. ATOM utilizes a deep reinforcement learning model to predict exactly when cold start latency will happen. We create a cold start dataset using a heart disease risk scenario and deploy using Google Cloud Functions. To demonstrate the superiority of ATOM, its performance is compared with two different baselines, which use the warm-start containers and a two-layer adaptive approach. The experimental results showed that although the ATOM required more calculation time of 118.76 seconds, it performed better in predicting cold start than baseline models with an RMSE ratio of 148.76. Additionally, the energy consumption and $CO_{2}$ emission amount of these models are evaluated and compared for the training and prediction phases.
{"title":"ATOM: AI-Powered Sustainable Resource Management for Serverless Edge Computing Environments","authors":"Muhammed Golec;Sukhpal Singh Gill;Felix Cuadrado;Ajith Kumar Parlikad;Minxian Xu;Huaming Wu;Steve Uhlig","doi":"10.1109/TSUSC.2023.3348157","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3348157","url":null,"abstract":"Serverless edge computing decreases unnecessary resource usage on end devices with limited processing power and storage capacity. Despite its benefits, serverless edge computing's zero scalability is the major source of the cold start delay, which is yet unsolved. This latency is unacceptable for time-sensitive Internet of Things (IoT) applications like autonomous cars. Most existing approaches need containers to idle and use extra computing resources. Edge devices have fewer resources than cloud-based systems, requiring new sustainable solutions. Therefore, we propose an AI-powered, sustainable resource management framework called ATOM for serverless edge computing. ATOM utilizes a deep reinforcement learning model to predict exactly when cold start latency will happen. We create a cold start dataset using a heart disease risk scenario and deploy using Google Cloud Functions. To demonstrate the superiority of ATOM, its performance is compared with two different baselines, which use the warm-start containers and a two-layer adaptive approach. The experimental results showed that although the ATOM required more calculation time of 118.76 seconds, it performed better in predicting cold start than baseline models with an RMSE ratio of 148.76. Additionally, the energy consumption and \u0000<inline-formula><tex-math>$CO_{2}$</tex-math></inline-formula>\u0000 emission amount of these models are evaluated and compared for the training and prediction phases.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 6","pages":"817-829"},"PeriodicalIF":3.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-12-25DOI: 10.1109/TSUSC.2023.3346332
Jianpeng Lin;Weiwei Lin;Huikang Huang;Wenjun Lin;Keqin Li
Constructing energy-efficient cloud data centers (CDCs) is an essential path for the further expansion of cloud computing. As one of the core subsystems of a data center, the cooling system provides a reliable thermal environment for the safe operation of IT equipment while posing a huge energy consumption and carbon emission problem. Thus, it is evident that optimizing energy management of cooling systems with considerable energy-saving potential will be essential to realize the green and low-carbon development of CDCs. Therefore, to track the research progress of data center thermal management technologies, this review focuses on two research efforts: thermal modeling and thermal-aware energy saving methods. First, various thermal modeling approaches are reviewed for air-cooled and liquid-cooled data centers. Secondly, a comprehensive review of existing advanced thermal management approaches is conducted from three perspectives: thermal-aware IT load scheduling, cooling system control optimization, and joint optimization of the IT and cooling systems. Finally, we put forward some open issues and future research directions for thermal management that have not been completely solved. This review aims to provide reasonable suggestions to enhance cooling energy efficiency and further promote the transformation of CDCs to lower energy consumption and sustainable direction.
建设高能效的云数据中心（CDC）是云计算进一步发展的必由之路。作为数据中心的核心子系统之一，冷却系统在为 IT 设备的安全运行提供可靠热环境的同时，也带来了巨大的能耗和碳排放问题。由此可见，要实现数据中心的绿色低碳发展，优化具有巨大节能潜力的冷却系统的能源管理至关重要。因此，为跟踪数据中心热管理技术的研究进展，本综述将重点关注热建模和热感知节能方法这两项研究工作。首先，综述了风冷和液冷数据中心的各种热建模方法。其次，从热感知 IT 负载调度、冷却系统控制优化以及 IT 和冷却系统联合优化这三个角度，对现有的先进热管理方法进行了全面回顾。最后，我们提出了一些尚未完全解决的热管理开放问题和未来研究方向。本综述旨在为提高冷却能效提供合理建议，进一步推动 CDC 向低能耗和可持续方向转型。
{"title":"Thermal Modeling and Thermal-Aware Energy Saving Methods for Cloud Data Centers: A Review","authors":"Jianpeng Lin;Weiwei Lin;Huikang Huang;Wenjun Lin;Keqin Li","doi":"10.1109/TSUSC.2023.3346332","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3346332","url":null,"abstract":"Constructing energy-efficient cloud data centers (CDCs) is an essential path for the further expansion of cloud computing. As one of the core subsystems of a data center, the cooling system provides a reliable thermal environment for the safe operation of IT equipment while posing a huge energy consumption and carbon emission problem. Thus, it is evident that optimizing energy management of cooling systems with considerable energy-saving potential will be essential to realize the green and low-carbon development of CDCs. Therefore, to track the research progress of data center thermal management technologies, this review focuses on two research efforts: thermal modeling and thermal-aware energy saving methods. First, various thermal modeling approaches are reviewed for air-cooled and liquid-cooled data centers. Secondly, a comprehensive review of existing advanced thermal management approaches is conducted from three perspectives: thermal-aware IT load scheduling, cooling system control optimization, and joint optimization of the IT and cooling systems. Finally, we put forward some open issues and future research directions for thermal management that have not been completely solved. This review aims to provide reasonable suggestions to enhance cooling energy efficiency and further promote the transformation of CDCs to lower energy consumption and sustainable direction.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"571-590"},"PeriodicalIF":3.9,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141264413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-12-12DOI: 10.1109/TSUSC.2023.3341836
Yongqi Jiang;Siguang Chen;Xiangwen Bao
In practical applications, federated learning (FL) suffers from slow convergence rate and inferior performance resulting from the statistical heterogeneity of distributed data. Personalized FL (pFL) has been proposed to overcome this problem. However, existing pFL approaches mainly focus on measuring differences between entire model dimensions across clients, ignore the layer-wise differences in convolutional neural networks (CNNs), which may lead to inaccurate personalization. Additionally, two potential threats in FL are that malicious clients may attempt to poison the entire federation by tampering with local labels, and the model information uploaded by clients makes them vulnerable to inference attacks. To tackle these issues, 1) we propose a novel pFL approach in which clients minimize local classification errors and align the local and global prototypes for data from the class that is shared with other clients. This method adopts layer-wise collaborative training to achieve more granular personalization and converts local prototypes to the frequency domain to prevent source data leakage; 2) To prevent the FL model from misclassifying certain test samples as expected by poisoners, we design a robust aggregation method to ensure that benign clients who provide trustworthy model predictions for its local data are weighted far more heavily in the aggregation process than malicious clients. Experiments show that our scheme, especially in the data heterogeneity situation, can produce robust performance and more stable convergence while preserving privacy.
{"title":"Amplitude-Aligned Personalization and Robust Aggregation for Federated Learning","authors":"Yongqi Jiang;Siguang Chen;Xiangwen Bao","doi":"10.1109/TSUSC.2023.3341836","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3341836","url":null,"abstract":"In practical applications, federated learning (FL) suffers from slow convergence rate and inferior performance resulting from the statistical heterogeneity of distributed data. Personalized FL (pFL) has been proposed to overcome this problem. However, existing pFL approaches mainly focus on measuring differences between entire model dimensions across clients, ignore the layer-wise differences in convolutional neural networks (CNNs), which may lead to inaccurate personalization. Additionally, two potential threats in FL are that malicious clients may attempt to poison the entire federation by tampering with local labels, and the model information uploaded by clients makes them vulnerable to inference attacks. To tackle these issues, 1) we propose a novel pFL approach in which clients minimize local classification errors and align the local and global prototypes for data from the class that is shared with other clients. This method adopts layer-wise collaborative training to achieve more granular personalization and converts local prototypes to the frequency domain to prevent source data leakage; 2) To prevent the FL model from misclassifying certain test samples as expected by poisoners, we design a robust aggregation method to ensure that benign clients who provide trustworthy model predictions for its local data are weighted far more heavily in the aggregation process than malicious clients. Experiments show that our scheme, especially in the data heterogeneity situation, can produce robust performance and more stable convergence while preserving privacy.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"535-547"},"PeriodicalIF":3.9,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141264311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2023-12-12DOI: 10.1109/TSUSC.2023.3341440
Rui Hao;Xiaohai Dai;Weiqi Dai
Blockchain technology has gained prominence for its potential to address security and privacy challenges in Internet-of-Things (IoT) services and Cyber-Physical Systems (CPS) due to its decentralized, traceable, and immutable nature. However, the considerable energy consumption associated with blockchain, exemplified by Bitcoin, has raised sustainability concerns. This paper introduces BitFT, a consensus protocol that combines the strengths of both lottery-based and voting-based mechanisms to offer a sustainable, comprehensible, and high-performance solution. BitFT dissects the block lifecycle into three phases: dissemination, and commitment phases, which correspond to the Bitcoin framework. It leverages a multiple-round sortition algorithm, a Reliable Broadcast (Rbc) protocol, and a Quorum Certificate (QC) mechanism to facilitate efficient protocol operation. The sortition algorithm functions like a lottery algorithm, while the Rbc protocol and $QC$ mechanism are implemented based on votes. In order to maximize network utilization and enhance system throughput, we further introduce a layered architecture to BitFT, which allows for concurrent commitment of multiple blocks at the same height. We perform a comprehensive analysis to verify the correctness of BitFT and conduct various experiments to demonstrate its high performance.
{"title":"BitFT: An Understandable, Performant and Resource-Efficient Blockchain Consensus","authors":"Rui Hao;Xiaohai Dai;Weiqi Dai","doi":"10.1109/TSUSC.2023.3341440","DOIUrl":"https://doi.org/10.1109/TSUSC.2023.3341440","url":null,"abstract":"Blockchain technology has gained prominence for its potential to address security and privacy challenges in Internet-of-Things (IoT) services and Cyber-Physical Systems (CPS) due to its decentralized, traceable, and immutable nature. However, the considerable energy consumption associated with blockchain, exemplified by Bitcoin, has raised sustainability concerns. This paper introduces BitFT, a consensus protocol that combines the strengths of both lottery-based and voting-based mechanisms to offer a sustainable, comprehensible, and high-performance solution. BitFT dissects the block lifecycle into three phases: dissemination, and commitment phases, which correspond to the Bitcoin framework. It leverages a multiple-round sortition algorithm, a Reliable Broadcast (Rbc) protocol, and a Quorum Certificate (QC) mechanism to facilitate efficient protocol operation. The sortition algorithm functions like a lottery algorithm, while the \u0000<small>Rbc</small>\u0000 protocol and \u0000<inline-formula><tex-math>$QC$</tex-math></inline-formula>\u0000 mechanism are implemented based on votes. In order to maximize network utilization and enhance system throughput, we further introduce a layered architecture to BitFT, which allows for concurrent commitment of multiple blocks at the same height. We perform a comprehensive analysis to verify the correctness of BitFT and conduct various experiments to demonstrate its high performance.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 3","pages":"522-534"},"PeriodicalIF":3.9,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141264515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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