{"title":"编码存储系统的灵活下载时间分析","authors":"Q. Shuai, V. Li","doi":"10.1145/2928275.2933278","DOIUrl":null,"url":null,"abstract":"Download time is a key performance metric in distributed storage systems since it greatly impacts user experience, especially for latency-sensitive applications such as Google Search and so on. Recently, plenty of research has pointed out that coding can reduce download time. Till now, almost all previous studies analyze download time when a user requires all the information in a codeword. However, in practical storage systems such as the Windows Azure Storage System (WAS), only when files reach a certain size (e.g., 1GB), will it be a candidate for erasure coding [1]. That is, in practice, files stored in a codeword are usually very large and users' requests may only desire part of these files. Therefore, it is significant to analyze the latency performance when users only request a subset of the erasure-coded content.","PeriodicalId":20607,"journal":{"name":"Proceedings of the 9th ACM International on Systems and Storage Conference","volume":"1 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2016-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Flexible Download Time Analysis of Coded Storage Systems\",\"authors\":\"Q. Shuai, V. Li\",\"doi\":\"10.1145/2928275.2933278\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Download time is a key performance metric in distributed storage systems since it greatly impacts user experience, especially for latency-sensitive applications such as Google Search and so on. Recently, plenty of research has pointed out that coding can reduce download time. Till now, almost all previous studies analyze download time when a user requires all the information in a codeword. However, in practical storage systems such as the Windows Azure Storage System (WAS), only when files reach a certain size (e.g., 1GB), will it be a candidate for erasure coding [1]. That is, in practice, files stored in a codeword are usually very large and users' requests may only desire part of these files. Therefore, it is significant to analyze the latency performance when users only request a subset of the erasure-coded content.\",\"PeriodicalId\":20607,\"journal\":{\"name\":\"Proceedings of the 9th ACM International on Systems and Storage Conference\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 9th ACM International on Systems and Storage Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2928275.2933278\",\"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 9th ACM International on Systems and Storage Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2928275.2933278","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Flexible Download Time Analysis of Coded Storage Systems
Download time is a key performance metric in distributed storage systems since it greatly impacts user experience, especially for latency-sensitive applications such as Google Search and so on. Recently, plenty of research has pointed out that coding can reduce download time. Till now, almost all previous studies analyze download time when a user requires all the information in a codeword. However, in practical storage systems such as the Windows Azure Storage System (WAS), only when files reach a certain size (e.g., 1GB), will it be a candidate for erasure coding [1]. That is, in practice, files stored in a codeword are usually very large and users' requests may only desire part of these files. Therefore, it is significant to analyze the latency performance when users only request a subset of the erasure-coded content.