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Improving the Data Placement Algorithm of Randomization in SAN

机译:改进SAN中随机化数据放置算法

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Using the randomization as the data placement algorithm has many advantages such as simple computation, long term load balancing, and little costs. Especially, some latest works have improved it to make it scale well while adding or deleting disks in large storage systems such as SAN (Storage Area Network). But it still has a shortcoming that it can not ensure load balancing in the short term when there are some very hot data blocks accessed frequently. This situation can often be met in Web environments. To solve the problem, based on the algorithm of randomization, an algorithm to select the hot-spot data blocks and a data placement scheme based on the algorithm are presented in this paper. The difference is that it redistributes a few very hot data blocks to make load balanced in any short time. Using this method, we only need to maintain a few blocks status information about their access frequency and more than that it is easy to implement and costs little. A simulation model is implemented to test the data placement methods of our new one and the one just using randomization. The real Web log is used to simulate the load and the results show that the new distributing method can make disks' load more balanced and get a performance increased by at most 100 percent. The new data placement algorithm will be more efficient in the storage system of a busy Web server.
机译:使用随机化作为数据放置算法具有许多优点,如简单的计算,长期负载平衡以及成本不大。特别是,一些最新作品已经改进,使其在加入或删除大型存储系统中的磁盘(如SAN(存储区域网络)等方面进行比例。但它仍然存在缺点,即当经常访问一些非常热的数据块时,它仍然无法确保在短期内进行负载平衡。在Web环境中通常可以满足这种情况。为了解决问题,基于随机化算法,本文介绍了选择热点数据块的算法和基于算法的数据放置方案。不同之处在于它重新分配了一些非常热的数据块,以在任何短时间内使负载平衡。使用此方法,我们只需要维护一些关于其接入频率的块状态信息,而且比它易于实现和成本很少。实施模拟模型以测试我们新的数据放置方法,即使用随机化。真正的Web日志用于模拟负载,结果表明,新的分布方法可以使磁盘的负载更加平衡,并且可以最多100%提高性能。新的数据放置算法在繁忙的Web服务器的存储系统中将更有效。

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