基于LSM树的云存储数据差异性存储节能优化算法

Optimization of Cloud Storage Data Based on LSM Tree

为解决因大数据环境不断扩大而导致的信息储存高功耗、低效率问题,提出一种基于LSM(Log Structured Merge)树的数据云储存节能优化算法。根据数据存储的数量、大小、网络带宽及链路长度等信息差异性特点,建立数据分片储存判定模型,计算数据在发送和接收时的时间延迟,对比既定参数判定是否需要分片储存。对需要分片储存的数据,通过时间延迟阈值明确在各个节点下所需的服务器功耗、静态功耗以及动态功耗,对平均功耗较大的数据实施分类传输,完成存储节能优化。仿真实验证明,采取所提方法后的云储存环境中冗余数据量明显减少,且处理稳定性较强,平均耗用低于设定阈值,整体算法性能较为优异。

In order to solve the problem of high power consumption and low efficiency of information storage caused by the enlargement of large data environment, a data cloud storage optimization algorithm based on LSM(Log Structured Merge) tree is proposed. According to the difference of the quantity and size of data storage, network bandwidth and link length, the judgement model of data fragmentation storage is established, and the time delay of data sending and receiving is calculated. For the data that need to be stored in segments, the server power consumption, static power consumption and dynamic power consumption under each node are determined by the time delay threshold, and the data with high average power consumption are transmitted by category to achieve storage and energy saving optimization. Simulation results show that the proposed method can reduce the amount of redundant data in cloud storage environment, and the processing stability is strong, the average consumption is lower than the set threshold.