摘要
针对海洋大数据环境的数据量大和实时动态变化的特点,提出了一种基于云存储的海洋大数据迁移算法。首先,对海洋大数据进行了表示;设计了一种灰色模型的服务器负载预测算法,该算法能根据服务器历史负载信息来预测下一个时刻的负载。基于服务器的负载预测信息,提出了一种对服务器的负载进行实时迁移的数据迁移算法,通过设定最大负载阈值和最小负载阈值来实现服务器负载的均衡分配。在Cloud Sim环境下进行实验,实验结果表明文中方法能有效地实现海洋大数据环境的云环境的负载均衡,具有负载均衡高和负载均衡效率高的优点,与其他方法相比,具有更好的负载均衡能力。
Aiming at the big amount of data in the big data environment and the dynamical change in time, a migration algorithm for data based on cloud memory is proposed. Firstly, the big data in ocean environment is represented; the load prediction algorithm based on the prediction of the gray model, where the algorithm can predict the load for the next time based on the historical load information. The data migration algorithm for migrating the load among the servers is proposed on the condition of the setting the smallest and biggest load threshold. In the environment of the Cloudsim, the simulated result shows this method can balance the load in the ocean big data, with the high load balance and load balance efficiency. Compared with the other methods, it has a better load balance ability.
作者
陈作聪
Chen Zuo-cong(School of Marine Information Engineering, Hainan Tropical Ocean University, Sanya 572022, Chin)
出处
《广东工业大学学报》
CAS
2018年第3期95-99,共5页
Journal of Guangdong University of Technology
基金
海南省重大科技计划项目(ZDKJ2016021)
关键词
云存储
灰色模型
迁移算法
负载均衡
预测
cloud memory
gray model
migration algorithm
load balance
prediction
