Adaptive strength geo-replication strategy

Amadeo Ascó Signes, Annette Bieniusa
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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.
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自适应强度两地复制策略
数据中心(dc)中处理的数据量以惊人的速度增长,因此完全复制可能开始变得不切实际。数据中心之间的复制应用程序用于在站点出现故障时提高数据可用性,并通过访问位于较近位置的数据(如果可能)来减少延迟。这意味着,为了降低保持数据(弱)一致性的成本,同时保持高可用性(可伸缩性)和低访问成本,仅在某些数据中心复制数据变得越来越重要。当数据读写请求模式发生变化时,然后动态地决定应该复制哪些数据以及需要在何处进行复制。鉴于在一般网络中寻找最优复制模式的问题已被证明在静态情况下是np完全的,因此不太可能存在用于动态问题的最优解的通用算法。我们提出了一种新的自适应生物启发复制策略,它是完全分散的,自适应的,事件驱动的,灵感来自蚁群算法。
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