{"title":"Adaptive strength geo-replication strategy","authors":"Amadeo Ascó Signes, Annette Bieniusa","doi":"10.1145/2745947.2745950","DOIUrl":null,"url":null,"abstract":"The amount of data being processed in Data Centres (DCs) keeps growing at an enormous rate so that full replication may start being impractical. The application of replication between DCs is used to increase data availability in the presence of site failures and to reduce latency by accessing the data closely located, if possible. This means that replicating the data only in some of the DCs is becoming more critical in order to reduce the costs of keeping the data (weakly) consistent while maintaining high availability (scalability) and low access costs. When data read and write request patterns change, then deciding which data should be replicated and where needs to be made dynamically. Given that the problem of finding an optimal replication schema in a general network has been shown to be NP-complete for the static case, so it is unlikely that there exists a general algorithm for an optimal solution to the dynamic problem. We present here a new adaptive bio--inspired replication strategy, which is completely decentralised, adaptive, and event-driven, inspired on the Ant Colony algorithm.","PeriodicalId":332245,"journal":{"name":"Proceedings of the First Workshop on Principles and Practice of Consistency for Distributed Data","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the First Workshop on Principles and Practice of Consistency for Distributed Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2745947.2745950","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
The amount of data being processed in Data Centres (DCs) keeps growing at an enormous rate so that full replication may start being impractical. The application of replication between DCs is used to increase data availability in the presence of site failures and to reduce latency by accessing the data closely located, if possible. This means that replicating the data only in some of the DCs is becoming more critical in order to reduce the costs of keeping the data (weakly) consistent while maintaining high availability (scalability) and low access costs. When data read and write request patterns change, then deciding which data should be replicated and where needs to be made dynamically. Given that the problem of finding an optimal replication schema in a general network has been shown to be NP-complete for the static case, so it is unlikely that there exists a general algorithm for an optimal solution to the dynamic problem. We present here a new adaptive bio--inspired replication strategy, which is completely decentralised, adaptive, and event-driven, inspired on the Ant Colony algorithm.