随着智算中心数据流量和业务需求的快速增长,高效、灵活的网络解决方案成为关键。细颗粒光传送网(fine grain optical transport network,fgOTN)作为同步数字体系(synchronous digital hierarchy,SDH)技术的接续与光传送网(optical tran...随着智算中心数据流量和业务需求的快速增长,高效、灵活的网络解决方案成为关键。细颗粒光传送网(fine grain optical transport network,fgOTN)作为同步数字体系(synchronous digital hierarchy,SDH)技术的接续与光传送网(optical transport network,OTN)技术的扩展,被应用于智算中心互联,以满足其灵活调度、高效传输、严格安全隔离和低时延等多重需求。首先,介绍了fgOTN的基本概念、技术架构及应用场景,随后,阐述了智算中心的相关概念、体系架构、关键技术及应用场景。在此基础上,重点探讨了fgOTN在智算中心互联中的应用,旨在促进智算中心间数据传输的高效、可靠。最后,论述了fgOTN应用于智算中心互联的研究方向和发展趋势。展开更多
Fault detection and diagnosis are essential to the air conditioning system of the data center for elevating reliability and reducing energy consumption.This study proposed a convolutional neural network(CNN)based data...Fault detection and diagnosis are essential to the air conditioning system of the data center for elevating reliability and reducing energy consumption.This study proposed a convolutional neural network(CNN)based data-driven fault detection and diagnosis model considering temporal dependency for composite air conditioning system that is capable of cooling the high heat flux in data centers.The input of fault detection and diagnosis model was an unsteady dataset generated by the experimentally validated transient mathematical model.The dataset concerned three typical faults,including refrigerant leakage,evaporator fan breakdown,and condenser fouling.Then,the CNN model was trained to construct a map between the input and system operating conditions.Further,the performance of the CNN model was validated by comparing it with the support vector machine and the neural network.Finally,the score-weighted class mapping activation method was utilized to interpret model diagnosis mechanisms and to identify key input features in various operating modes.The results demonstrated in the pump-driven heat pipe mode,the accuracy of the CNN model was 99.14%,increasing by around 8.5%compared with the other two methods.In the vapor compression mode,the accuracy of the CNN model achieved 99.9%and declined the miss rate of refrigerant leakage by at least 61%comparatively.The score-weighted class mapping activation results indicated the ambient temperature and the actuator-related parameters,such as compressor frequency in vapor compression mode and condenser fan frequency in pump-driven heat pipe mode,were essential features in system fault detection and diagnosis.展开更多
文摘随着智算中心数据流量和业务需求的快速增长,高效、灵活的网络解决方案成为关键。细颗粒光传送网(fine grain optical transport network,fgOTN)作为同步数字体系(synchronous digital hierarchy,SDH)技术的接续与光传送网(optical transport network,OTN)技术的扩展,被应用于智算中心互联,以满足其灵活调度、高效传输、严格安全隔离和低时延等多重需求。首先,介绍了fgOTN的基本概念、技术架构及应用场景,随后,阐述了智算中心的相关概念、体系架构、关键技术及应用场景。在此基础上,重点探讨了fgOTN在智算中心互联中的应用,旨在促进智算中心间数据传输的高效、可靠。最后,论述了fgOTN应用于智算中心互联的研究方向和发展趋势。
基金the support from the National Natural Science Foundation of China(Grant number 52176180)the support from“the open competition mechanism to select the best candidates”key technology project of Liaoning(Grant 2022JH1/10800008).
文摘Fault detection and diagnosis are essential to the air conditioning system of the data center for elevating reliability and reducing energy consumption.This study proposed a convolutional neural network(CNN)based data-driven fault detection and diagnosis model considering temporal dependency for composite air conditioning system that is capable of cooling the high heat flux in data centers.The input of fault detection and diagnosis model was an unsteady dataset generated by the experimentally validated transient mathematical model.The dataset concerned three typical faults,including refrigerant leakage,evaporator fan breakdown,and condenser fouling.Then,the CNN model was trained to construct a map between the input and system operating conditions.Further,the performance of the CNN model was validated by comparing it with the support vector machine and the neural network.Finally,the score-weighted class mapping activation method was utilized to interpret model diagnosis mechanisms and to identify key input features in various operating modes.The results demonstrated in the pump-driven heat pipe mode,the accuracy of the CNN model was 99.14%,increasing by around 8.5%compared with the other two methods.In the vapor compression mode,the accuracy of the CNN model achieved 99.9%and declined the miss rate of refrigerant leakage by at least 61%comparatively.The score-weighted class mapping activation results indicated the ambient temperature and the actuator-related parameters,such as compressor frequency in vapor compression mode and condenser fan frequency in pump-driven heat pipe mode,were essential features in system fault detection and diagnosis.
