R. Mortier, D. Narayanan, Austin Donnelly, A. Rowstron
{"title":"海藻:分布式可伸缩的自组织查询","authors":"R. Mortier, D. Narayanan, Austin Donnelly, A. Rowstron","doi":"10.1109/ICDEW.2006.132","DOIUrl":null,"url":null,"abstract":"Many emerging applications such as wide-area network management need to query large, structured, highly distributed datasets. Seaweed is a distributed scalable infrastructure for querying such datasets. In this paper we describe its architecture and design features, using the Anemone network management system as a motivating example. The main contribution is a design supporting accurate query planning and efficient execution across a large number of unreliable endsystems. In contrast to prior work, Seaweed supports ad hoc querying in addition to continuous querying. The paper describes the solutions adopted by Seaweed: latency-based cost estimation, availability-based scheduling, and meta-data aggregation.","PeriodicalId":331953,"journal":{"name":"22nd International Conference on Data Engineering Workshops (ICDEW'06)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2006-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Seaweed: Distributed Scalable Ad Hoc Querying\",\"authors\":\"R. Mortier, D. Narayanan, Austin Donnelly, A. Rowstron\",\"doi\":\"10.1109/ICDEW.2006.132\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many emerging applications such as wide-area network management need to query large, structured, highly distributed datasets. Seaweed is a distributed scalable infrastructure for querying such datasets. In this paper we describe its architecture and design features, using the Anemone network management system as a motivating example. The main contribution is a design supporting accurate query planning and efficient execution across a large number of unreliable endsystems. In contrast to prior work, Seaweed supports ad hoc querying in addition to continuous querying. The paper describes the solutions adopted by Seaweed: latency-based cost estimation, availability-based scheduling, and meta-data aggregation.\",\"PeriodicalId\":331953,\"journal\":{\"name\":\"22nd International Conference on Data Engineering Workshops (ICDEW'06)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"22nd International Conference on Data Engineering Workshops (ICDEW'06)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDEW.2006.132\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"22nd International Conference on Data Engineering Workshops (ICDEW'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDEW.2006.132","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Many emerging applications such as wide-area network management need to query large, structured, highly distributed datasets. Seaweed is a distributed scalable infrastructure for querying such datasets. In this paper we describe its architecture and design features, using the Anemone network management system as a motivating example. The main contribution is a design supporting accurate query planning and efficient execution across a large number of unreliable endsystems. In contrast to prior work, Seaweed supports ad hoc querying in addition to continuous querying. The paper describes the solutions adopted by Seaweed: latency-based cost estimation, availability-based scheduling, and meta-data aggregation.