{"title":"基于熵权法和改进理想点法的网络设备推荐放置方案设计","authors":"Size Liu, Zhenxing Qi, Xinpei Liu, Fangke Lu","doi":"10.1145/3609703.3609720","DOIUrl":null,"url":null,"abstract":"At present, data center security and energy consumption have been continuously concerned and discussed. There are some new technology to reduce energy consumption of data center, but few studies focus on make full use of the resources of the existing data center during routine maintenance to solve urgent problems such as low interest rate of distributed power supply resources, mismatch of power supply resources and space resources in cabinet, and unequal distribution of AC power supply systems. This paper focus on problems existing in data center, design a general distribution scheme to recommend network equipment optimal cabinet and location based on entropy weight method ,which combined with entropy weight method to optimize important attributes such as cabinet power utilization, cabinet space utilization, cabinet resource imbalance, and three-phase imbalance, recommend network equipment optimal cabinet and location totally depending on objective data, avoid the problem maintenance staff lack of experience or inconsiderate to make wrong decision effectively, achieve full utilization of data center power system resources and operation optimization. The scheme is verified to be effective, has certain guiding significance for network equipment location selected management in data center.","PeriodicalId":101485,"journal":{"name":"Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Network equipment recommended placement based on entropy weight method and improved ideal point method distribution scheme design\",\"authors\":\"Size Liu, Zhenxing Qi, Xinpei Liu, Fangke Lu\",\"doi\":\"10.1145/3609703.3609720\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"At present, data center security and energy consumption have been continuously concerned and discussed. There are some new technology to reduce energy consumption of data center, but few studies focus on make full use of the resources of the existing data center during routine maintenance to solve urgent problems such as low interest rate of distributed power supply resources, mismatch of power supply resources and space resources in cabinet, and unequal distribution of AC power supply systems. This paper focus on problems existing in data center, design a general distribution scheme to recommend network equipment optimal cabinet and location based on entropy weight method ,which combined with entropy weight method to optimize important attributes such as cabinet power utilization, cabinet space utilization, cabinet resource imbalance, and three-phase imbalance, recommend network equipment optimal cabinet and location totally depending on objective data, avoid the problem maintenance staff lack of experience or inconsiderate to make wrong decision effectively, achieve full utilization of data center power system resources and operation optimization. The scheme is verified to be effective, has certain guiding significance for network equipment location selected management in data center.\",\"PeriodicalId\":101485,\"journal\":{\"name\":\"Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3609703.3609720\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3609703.3609720","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Network equipment recommended placement based on entropy weight method and improved ideal point method distribution scheme design
At present, data center security and energy consumption have been continuously concerned and discussed. There are some new technology to reduce energy consumption of data center, but few studies focus on make full use of the resources of the existing data center during routine maintenance to solve urgent problems such as low interest rate of distributed power supply resources, mismatch of power supply resources and space resources in cabinet, and unequal distribution of AC power supply systems. This paper focus on problems existing in data center, design a general distribution scheme to recommend network equipment optimal cabinet and location based on entropy weight method ,which combined with entropy weight method to optimize important attributes such as cabinet power utilization, cabinet space utilization, cabinet resource imbalance, and three-phase imbalance, recommend network equipment optimal cabinet and location totally depending on objective data, avoid the problem maintenance staff lack of experience or inconsiderate to make wrong decision effectively, achieve full utilization of data center power system resources and operation optimization. The scheme is verified to be effective, has certain guiding significance for network equipment location selected management in data center.