数据网格环境下一种动态自适应的副本定位方法

在数据网格中，数据常常会由于性能和可用性等原因进行复制，如何有效地定位数据的一个或多个副本的物理位置是数据网格系统需要解决的重要问题，提出了一种可扩展、动态自适应的分布副本定位方法——DSRL，DSRL使用宿主结点来支持对同一数据多个副本的同时高效定位，使用本地副本定位结点来支持对副本的本地查询。DSRL提出了一种动态均衡映射方法，将全局副本定位信息均衡分布在多个宿主结点上，并且能够自适应宿主结点的动态加人或退出，详细描述了DSRL的组成，并对DSRL方法的正确性和负载平衡等特性进行了证明，分析和实验表明，DSRL方法有着良好的可扩展性、可靠性、自适应性和性能，并且实现简单，有着较好的实用性。

Dynamic Self-Adaptive Replica Location Method in Data Grids

Data replication is a general mechanism to improve performance and availabilityfor distributed applications. However, it is a challenging problem to find thephysical locations of one or more replicas of desired data efficiently in large-scale data grid systems. To solve the problem, a new dynamic self-adaptive distributed replica location method (DSRL) is proposed. In DSRL each data element has a home node, which maintains the index of the location information of replicas. Home nodes are used to accelerate the process of locating multiple replicas of the same data element. Meanwhile DSRL employs local location nodes which maintain the local replica information of data elements to support local query for replicas. A dynamic balancing technique that can adapt to the joining or departing of home nodes is proposed to spread global replica location information evenly on location nodes. The correctness and properties of DSRL are presented and proved. Analysis and experiments show that DSRL can achieve good scalability, low latency, reliability, adaptability and ease of implementation.