Pub Date : 2024-12-11DOI: 10.1109/TSUSC.2024.3415951
Zhongwen Guo;Hui Xia;Yu Wang;Radhouane Chouchane
{"title":"Guest Editorial of the Special Section on AI Powered Edge Computing for IoT","authors":"Zhongwen Guo;Hui Xia;Yu Wang;Radhouane Chouchane","doi":"10.1109/TSUSC.2024.3415951","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3415951","url":null,"abstract":"","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 6","pages":"814-816"},"PeriodicalIF":3.0,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10791341","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anomaly detection plays a vital role as a crucial security measure for edge devices in Artificial Intelligence and Internet of Things (AIoT). With the rapid development of IoT (Internet of Things), changes in system configurations and the introduction of new devices can lead to significant alterations in device relationships and data flows within the IoT, thereby triggering concept drift. Previously trained anomaly detection models fail to adapt to the changed distribution of streaming data, resulting in a high number of false positive events. This paper aims to address the issue of concept drift in IoT anomaly detection by proposing a comprehensive Concept Drift Detection, Interpretation, and Adaptation framework (CDDIA). We focus on accurately capturing the concept drift of normal data in unsupervised scenarios. To interpret drift samples, we integrate a search optimization algorithm and the SHAP method, providing a comprehensive interpretation of drift samples at both the sample and feature levels. Simultaneously, by utilizing the sample-level interpretation results for filtering new and old samples, we retrain the anomaly detection model to mitigate the impact of concept drift and reduce the false positive rate. This integrated strategy demonstrates significant advantages in maintaining model stability and reliability. The experimental results indicate that our method outperforms five baseline methods in adaptability across three datasets and provides interpretability for samples experiencing concept drift.
{"title":"Addressing Concept Drift in IoT Anomaly Detection: Drift Detection, Interpretation, and Adaptation","authors":"Lijuan Xu;Ziyu Han;Dawei Zhao;Xin Li;Fuqiang Yu;Chuan Chen","doi":"10.1109/TSUSC.2024.3386667","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3386667","url":null,"abstract":"Anomaly detection plays a vital role as a crucial security measure for edge devices in Artificial Intelligence and Internet of Things (AIoT). With the rapid development of IoT (Internet of Things), changes in system configurations and the introduction of new devices can lead to significant alterations in device relationships and data flows within the IoT, thereby triggering concept drift. Previously trained anomaly detection models fail to adapt to the changed distribution of streaming data, resulting in a high number of false positive events. This paper aims to address the issue of concept drift in IoT anomaly detection by proposing a comprehensive Concept Drift Detection, Interpretation, and Adaptation framework (CDDIA). We focus on accurately capturing the concept drift of normal data in unsupervised scenarios. To interpret drift samples, we integrate a search optimization algorithm and the SHAP method, providing a comprehensive interpretation of drift samples at both the sample and feature levels. Simultaneously, by utilizing the sample-level interpretation results for filtering new and old samples, we retrain the anomaly detection model to mitigate the impact of concept drift and reduce the false positive rate. This integrated strategy demonstrates significant advantages in maintaining model stability and reliability. The experimental results indicate that our method outperforms five baseline methods in adaptability across three datasets and provides interpretability for samples experiencing concept drift.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 6","pages":"913-924"},"PeriodicalIF":3.0,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810508","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 : 2024-04-19DOI: 10.1109/TSUSC.2024.3391733
Guhui Li;Zidong Wang;Xingzhen Bai;Zhongyi Zhao
This paper is concerned with the set-membership state estimation problem for power harmonics under quantization effects by using the dynamic event-triggered mechanism. The underlying system is subject to unknown but bounded noises that are confined to a sequence of zonotopes. The data transmissions are realized over a digital communication channel, where the measurement signals are quantized by a logarithmic-uniform quantizer before being transmitted from the sensors to the remote estimator. Moreover, a dynamic event-triggered mechanism is introduced to reduce the number of unnecessary data transmissions, thereby relieving the communication burden. The objective of this paper is to design a zonotopic set-membership estimator for power harmonics with guaranteed estimation performance in the simultaneous presence of: 1) unknown but bounded noises; 2) quantization effects; and 3) dynamic event-triggered executions. By resorting to the mathematical induction method, a unified set-membership estimation framework is established, within which a family of zonotopic sets is first derived that contains the estimation errors and, subsequently, the estimator gain matrices are designed by minimizing the $F$ -radii of these zonotopic sets. The effectiveness of the proposed estimation scheme is verified by a series of simulation experiments.
{"title":"Dynamic Event-Triggered State Estimation for Power Harmonics With Quantization Effects: A Zonotopic Set-Membership Approach","authors":"Guhui Li;Zidong Wang;Xingzhen Bai;Zhongyi Zhao","doi":"10.1109/TSUSC.2024.3391733","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3391733","url":null,"abstract":"This paper is concerned with the set-membership state estimation problem for power harmonics under quantization effects by using the dynamic event-triggered mechanism. The underlying system is subject to unknown but bounded noises that are confined to a sequence of zonotopes. The data transmissions are realized over a digital communication channel, where the measurement signals are quantized by a logarithmic-uniform quantizer before being transmitted from the sensors to the remote estimator. Moreover, a dynamic event-triggered mechanism is introduced to reduce the number of unnecessary data transmissions, thereby relieving the communication burden. The objective of this paper is to design a zonotopic set-membership estimator for power harmonics with guaranteed estimation performance in the simultaneous presence of: 1) unknown but bounded noises; 2) quantization effects; and 3) dynamic event-triggered executions. By resorting to the mathematical induction method, a unified set-membership estimation framework is established, within which a family of zonotopic sets is first derived that contains the estimation errors and, subsequently, the estimator gain matrices are designed by minimizing the \u0000<inline-formula><tex-math>$F$</tex-math></inline-formula>\u0000-radii of these zonotopic sets. The effectiveness of the proposed estimation scheme is verified by a series of simulation experiments.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 5","pages":"803-813"},"PeriodicalIF":3.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397226","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}
Federated learning (FL) is a distributed machine learning paradigm that addresses the challenges of privacy leakage and data silos by collaboratively training the global model through parameter exchange, rather than data, between the central server and local clients. However, recent researches highlight the vulnerability of FL to gradient leakage attacks where adversaries exploit shared parameters from clients to reconstruct sensitive training data. Differential privacy (DP) effectively mitigates this threat by adding noise to shared parameters, yet introduces a trade-off between privacy and accuracy in FL. To better balance the privacy and accuracy, in this paper we propose a staged noise perturbation strategy, called alternating noise permutation (ANP), from a novel perspective. ANP adds Gaussian-distributed random noise to model parameters during the critical learning period of FL, following DP principles. While in non-critical learning period, ANP alternately permutes the noise during odd and even communication rounds, achieving near mutual cancellation and mitigating the negative impact. Experimental results across three datasets and two neural networks under both independent identical distribution (IID) and NonIID scenarios demonstrate that ANP significantly improves classification accuracy and exhibits robustness against gradient leakage attack, ensuring the effectiveness of FL for secure and accurate collaborative model training.
{"title":"Staged Noise Perturbation for Privacy-Preserving Federated Learning","authors":"Zhe Li;Honglong Chen;Yudong Gao;Zhichen Ni;Huansheng Xue;Huajie Shao","doi":"10.1109/TSUSC.2024.3381812","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3381812","url":null,"abstract":"Federated learning (FL) is a distributed machine learning paradigm that addresses the challenges of privacy leakage and data silos by collaboratively training the global model through parameter exchange, rather than data, between the central server and local clients. However, recent researches highlight the vulnerability of FL to gradient leakage attacks where adversaries exploit shared parameters from clients to reconstruct sensitive training data. Differential privacy (DP) effectively mitigates this threat by adding noise to shared parameters, yet introduces a trade-off between privacy and accuracy in FL. To better balance the privacy and accuracy, in this paper we propose a staged noise perturbation strategy, called alternating noise permutation (ANP), from a novel perspective. ANP adds Gaussian-distributed random noise to model parameters during the critical learning period of FL, following DP principles. While in non-critical learning period, ANP alternately permutes the noise during odd and even communication rounds, achieving near mutual cancellation and mitigating the negative impact. Experimental results across three datasets and two neural networks under both independent identical distribution (IID) and NonIID scenarios demonstrate that ANP significantly improves classification accuracy and exhibits robustness against gradient leakage attack, ensuring the effectiveness of FL for secure and accurate collaborative model training.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 6","pages":"936-947"},"PeriodicalIF":3.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810512","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 : 2024-03-27DOI: 10.1109/TSUSC.2024.3382157
Xian Zhang;Guoqing Xiao;Mingxing Duan;Yuedan Chen;Kenli Li
Convolutional neural networks (CNNs) are widely utilized in intelligent edge computing applications such as computational vision and image processing. However, as the number of layers of the CNN model increases, the number of parameters and computations gets larger, making it increasingly challenging to accelerate in edge computing applications. To effectively adapt to the tradeoff between the speed and accuracy of CNNs inference for smart applications. This paper proposes an FPGA-based adaptive CNNs inference accelerator synergistically utilizing filter pruning, fixed-point parameter quantization, and multi-computing unit parallelism called APPQ-CNN. First, the article devises a hybrid pruning algorithm based on the L1-norm and APoZ to measure the filter impact degree and a configurable parameter quantization fixed-point computing architecture instead of floating-point architecture. Then, design a cascade of the CNN pipelined kernel architecture and configurable multiple computation units. Finally, conduct extensive performance exploration and comparison experiments on various real and synthetic datasets. With negligible accuracy loss, the speed performance of our accelerator APPQ-CNN compares with current state-of-the-art FPGA-based accelerators PipeCNN and OctCNN by 2.15× and 1.91×, respectively. Furthermore, APPQ-CNN provides settable fixed-point quantization bit-width parameters, filter pruning rate, and multiple computation unit counts to cope with practical application performance requirements in edge computing.
{"title":"APPQ-CNN: An Adaptive CNNs Inference Accelerator for Synergistically Exploiting Pruning and Quantization Based on FPGA","authors":"Xian Zhang;Guoqing Xiao;Mingxing Duan;Yuedan Chen;Kenli Li","doi":"10.1109/TSUSC.2024.3382157","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3382157","url":null,"abstract":"Convolutional neural networks (CNNs) are widely utilized in intelligent edge computing applications such as computational vision and image processing. However, as the number of layers of the CNN model increases, the number of parameters and computations gets larger, making it increasingly challenging to accelerate in edge computing applications. To effectively adapt to the tradeoff between the speed and accuracy of CNNs inference for smart applications. This paper proposes an FPGA-based adaptive CNNs inference accelerator synergistically utilizing filter pruning, fixed-point parameter quantization, and multi-computing unit parallelism called APPQ-CNN. First, the article devises a hybrid pruning algorithm based on the L1-norm and APoZ to measure the filter impact degree and a configurable parameter quantization fixed-point computing architecture instead of floating-point architecture. Then, design a cascade of the CNN pipelined kernel architecture and configurable multiple computation units. Finally, conduct extensive performance exploration and comparison experiments on various real and synthetic datasets. With negligible accuracy loss, the speed performance of our accelerator APPQ-CNN compares with current state-of-the-art FPGA-based accelerators PipeCNN and OctCNN by 2.15× and 1.91×, respectively. Furthermore, APPQ-CNN provides settable fixed-point quantization bit-width parameters, filter pruning rate, and multiple computation unit counts to cope with practical application performance requirements in edge computing.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 6","pages":"874-888"},"PeriodicalIF":3.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810536","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}
The rapid advancement of mobile edge computing (MEC) has revolutionized the distributed computing landscape. With the help of MEC, the traditional centralized cloud computing architecture can be extended to the edge of networks, enabling real-time processing of resources and time-sensitive applications. Nevertheless, the problem of efficiently assigning the services to the computing resources is a challenging and prevalent issue due to the dynamic and distributed nature of the edge network's architecture. Thus, we require intelligent real-time decision-making and effective optimization algorithms to allocate resources, such as network bandwidth, memory, and CPU. This paper proposes an MEC architecture to allocate the resources in the network to optimize the quality of services (QoS). In this regard, the resource allocation problem is formulated as a bi-objective optimization problem, including minimizing cost and energy with quality and deadline constraints. A hybrid cascading-based meta-heuristic called GA-PSO is embedded with the proposed MEC architecture to achieve these objectives. Finally, it is compared with three existing approaches to establish its efficacy. The experimental results report statistically better cost and energy in all the considered instances, making it practical and validating its effectiveness.
{"title":"Deadline-Aware Cost and Energy Efficient Offloading in Mobile Edge Computing","authors":"Mohit Kumar;Avadh Kishor;Pramod Kumar Singh;Kalka Dubey","doi":"10.1109/TSUSC.2024.3381841","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3381841","url":null,"abstract":"The rapid advancement of mobile edge computing (MEC) has revolutionized the distributed computing landscape. With the help of MEC, the traditional centralized cloud computing architecture can be extended to the edge of networks, enabling real-time processing of resources and time-sensitive applications. Nevertheless, the problem of efficiently assigning the services to the computing resources is a challenging and prevalent issue due to the dynamic and distributed nature of the edge network's architecture. Thus, we require intelligent real-time decision-making and effective optimization algorithms to allocate resources, such as network bandwidth, memory, and CPU. This paper proposes an MEC architecture to allocate the resources in the network to optimize the quality of services (QoS). In this regard, the resource allocation problem is formulated as a bi-objective optimization problem, including minimizing cost and energy with quality and deadline constraints. A hybrid cascading-based meta-heuristic called GA-PSO is embedded with the proposed MEC architecture to achieve these objectives. Finally, it is compared with three existing approaches to establish its efficacy. The experimental results report statistically better cost and energy in all the considered instances, making it practical and validating its effectiveness.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 5","pages":"778-789"},"PeriodicalIF":3.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397224","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 : 2024-03-22DOI: 10.1109/TSUSC.2024.3395350
Na Hu;Wei Liang;Dafang Zhang;Kun Xie;Kuanching Li;Albert Y. Zomaya
Traffic prediction is crucial for intelligent transportation systems, assisting in making travel decisions, minimizing traffic congestion, and improving traffic operation efficiency. Although effective, existing centralized traffic prediction methods have privacy leakage risks. Federated learning-based traffic prediction methods keep raw data local and train the global model in a distributed way, thus preserving data privacy. Nevertheless, the spatial correlations between local clients will be broken as data exchange between local clients is not allowed in federated learning, leading to missing spatial information and inferior prediction accuracy. To this end, we propose a federated graph neural network with spatial information completion (FedGCN) for privacy-preserving traffic prediction by adopting a federated learning scheme to protect confidentiality and presenting a mending graph convolutional neural network to mend the missing spatial information during capturing spatial dependency to improve prediction accuracy. To complete the missing spatial information efficiently and capture the client-specific spatial pattern, we design a personalized training scheme for the mending graph neural network, reducing communication overhead. The experiments on four public traffic datasets demonstrate that the proposed model outperforms the best baseline with a ratio of 3.82%, 1.82%, 2.13%, and 1.49% in terms of absolute mean error while preserving privacy.
{"title":"FedGCN: A Federated Graph Convolutional Network for Privacy-Preserving Traffic Prediction","authors":"Na Hu;Wei Liang;Dafang Zhang;Kun Xie;Kuanching Li;Albert Y. Zomaya","doi":"10.1109/TSUSC.2024.3395350","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3395350","url":null,"abstract":"Traffic prediction is crucial for intelligent transportation systems, assisting in making travel decisions, minimizing traffic congestion, and improving traffic operation efficiency. Although effective, existing centralized traffic prediction methods have privacy leakage risks. Federated learning-based traffic prediction methods keep raw data local and train the global model in a distributed way, thus preserving data privacy. Nevertheless, the spatial correlations between local clients will be broken as data exchange between local clients is not allowed in federated learning, leading to missing spatial information and inferior prediction accuracy. To this end, we propose a federated graph neural network with spatial information completion (FedGCN) for privacy-preserving traffic prediction by adopting a federated learning scheme to protect confidentiality and presenting a mending graph convolutional neural network to mend the missing spatial information during capturing spatial dependency to improve prediction accuracy. To complete the missing spatial information efficiently and capture the client-specific spatial pattern, we design a personalized training scheme for the mending graph neural network, reducing communication overhead. The experiments on four public traffic datasets demonstrate that the proposed model outperforms the best baseline with a ratio of 3.82%, 1.82%, 2.13%, and 1.49% in terms of absolute mean error while preserving privacy.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 6","pages":"925-935"},"PeriodicalIF":3.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810511","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}
Federated Learning (FL), as a distributed machine learning technique, has promise for training models with distributed data in Artificial Intelligence of Things (AIoT). However, FL is vulnerable to Byzantine attacks from diverse participants. While numerous Byzantine-robust FL solutions have been proposed, most of them involve deploying defenses at either the aggregation server or the participant level, significantly impacting the original FL process. Moreover, it will bring extra computational burden to the server or the participant, which is especially unsuitable for the resource-constrained AIoT domain. To resolve the aforementioned concerns, we propose FL-Auditor, a Byzantine-robust FL approach based on public auditing. Its core idea is to use a Third-Party Auditor (TPA) to audit samples from the FL training process, analyzing the trustworthiness of different participants, thereby helping FL obtain a more robust global model. In addition, we also design a lazy update mechanism to reduce the negative impact of sampling audit on the performance of the global model. Extensive experiments have demonstrated the effectiveness of our FL-Auditor in terms of accuracy, robustness against attacks, and flexibility. In particular, compared to the existing method, our FL-Auditor significantly reduces the computation time on the aggregation server by 8×-17×.
{"title":"Using Third-Party Auditor to Help Federated Learning: An Efficient Byzantine-Robust Federated Learning","authors":"Zhuangzhuang Zhang;Libing Wu;Debiao He;Jianxin Li;Na Lu;Xuejiang Wei","doi":"10.1109/TSUSC.2024.3379440","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3379440","url":null,"abstract":"Federated Learning (FL), as a distributed machine learning technique, has promise for training models with distributed data in Artificial Intelligence of Things (AIoT). However, FL is vulnerable to Byzantine attacks from diverse participants. While numerous Byzantine-robust FL solutions have been proposed, most of them involve deploying defenses at either the aggregation server or the participant level, significantly impacting the original FL process. Moreover, it will bring extra computational burden to the server or the participant, which is especially unsuitable for the resource-constrained AIoT domain. To resolve the aforementioned concerns, we propose FL-Auditor, a Byzantine-robust FL approach based on public auditing. Its core idea is to use a Third-Party Auditor (TPA) to audit samples from the FL training process, analyzing the trustworthiness of different participants, thereby helping FL obtain a more robust global model. In addition, we also design a lazy update mechanism to reduce the negative impact of sampling audit on the performance of the global model. Extensive experiments have demonstrated the effectiveness of our FL-Auditor in terms of accuracy, robustness against attacks, and flexibility. In particular, compared to the existing method, our FL-Auditor significantly reduces the computation time on the aggregation server by 8×-17×.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 6","pages":"848-861"},"PeriodicalIF":3.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810544","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 : 2024-03-20DOI: 10.1109/TSUSC.2024.3379550
Duc Van Le;Jing Zhou;Rongrong Wang;Rui Tan;Fei Duan
Data centers (DCs) are power-intensive facilities which use a significant amount of energy for cooling the servers. Increasing the temperature and relative humidity (RH) setpoints is a rule-of-thumb approach to reducing the DC energy usage. However, the high temperature and RH may undermine the server's reliability. Before we can choose the proper temperature and RH settings, it is essential to understand how the temperature and RH setpoints affect the DC power usage and server's reliability. To this end, we constructed and experimented with an air-cooled DC testbed in Singapore, which consists of a direct expansion cooling system and 521 servers running real-world application workloads. This paper presents the key measurement results and observations from our 11-month experiments. Our results suggest that by operating at a supply air temperature setpoints of 29 $^{circ }$ C, our testbed achieves substantial cooling power saving with little impact on the server's reliability. Furthermore, we present a total cost of ownership (TCO) analysis framework which guides settings of the temperature and RH for a DC. Our observations and TCO analysis framework will be useful to future efforts in building and operating air-cooled DCs in tropics and beyond.
数据中心（DC）是电力密集型设施，需要消耗大量能源来冷却服务器。提高温度和相对湿度（RH）设定值是减少 DC 能源消耗的一个常用方法。然而，高温和相对湿度可能会降低服务器的可靠性。在选择合适的温度和相对湿度设置之前，我们必须了解温度和相对湿度设置点如何影响直流电能使用和服务器的可靠性。为此，我们在新加坡建造了一个风冷直流试验台并进行了实验，该试验台由直接膨胀冷却系统和运行实际应用工作负载的 521 台服务器组成。本文介绍了为期 11 个月实验的主要测量结果和观察结果。我们的结果表明，通过在 29$^{circ }$C 的供气温度设定值下运行，我们的测试平台实现了大量的制冷节能，而对服务器的可靠性影响很小。此外，我们还提出了一个总拥有成本（TCO）分析框架，用于指导直流电的温度和相对湿度设置。我们的观察结果和总拥有成本分析框架将有助于今后在热带地区及其他地区建造和运行风冷直流电。
{"title":"Impacts of Increasing Temperature and Relative Humidity in Air-Cooled Tropical Data Centers","authors":"Duc Van Le;Jing Zhou;Rongrong Wang;Rui Tan;Fei Duan","doi":"10.1109/TSUSC.2024.3379550","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3379550","url":null,"abstract":"Data centers (DCs) are power-intensive facilities which use a significant amount of energy for cooling the servers. Increasing the temperature and relative humidity (RH) setpoints is a rule-of-thumb approach to reducing the DC energy usage. However, the high temperature and RH may undermine the server's reliability. Before we can choose the proper temperature and RH settings, it is essential to understand how the temperature and RH setpoints affect the DC power usage and server's reliability. To this end, we constructed and experimented with an air-cooled DC testbed in Singapore, which consists of a direct expansion cooling system and 521 servers running real-world application workloads. This paper presents the key measurement results and observations from our 11-month experiments. Our results suggest that by operating at a supply air temperature setpoints of 29\u0000<inline-formula><tex-math>$^{circ }$</tex-math></inline-formula>\u0000C, our testbed achieves substantial cooling power saving with little impact on the server's reliability. Furthermore, we present a total cost of ownership (TCO) analysis framework which guides settings of the temperature and RH for a DC. Our observations and TCO analysis framework will be useful to future efforts in building and operating air-cooled DCs in tropics and beyond.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"9 5","pages":"790-802"},"PeriodicalIF":3.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397225","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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