一种自适应文件系统元数据服务负载均衡策略

随着大数据时代的到来，全球信息存储量呈现爆发式的增长，传统的存储系统在存储性能、存储容量、数据可靠性和成本等方面存在诸多不足。近年来，以云计算平台为依托的存储技术得到了飞速的发展，成为了处理海量数据的重要工具。本文针对分布式文件系统元数据管理的问题，提出了一种自适应元数据服务负载均衡策略。该策略主要包括以下三点内容：第一，介绍了一种实时的元数据服务器的性能评价模型；第二，提出了一种基于服务器负载变化的检测周期自适应调整机制；第三，提出了一种基于元数据服务器性能指标的自适应负载均衡算法。实验证明了该方法的可行性，有效性和稳定性。

Adaptive Load Balancing Strategy for File-System Metadata Service

With the coming of big data era, the amount of global storage is experiencing an explosive growth. Traditional storage systems have several drawbacks in storage performance, storage capacity, data security and device cost. In order to deal with big scale amount of data, the storage technology for cloud computing platform has a rapid development in the recent years, and become an important tool to deal with big data. In this paper, we analyze the drawbacks of metadata management of some distributed file system and propose a novel adaptive metadata load balancing mechanism. The contributions of this paper are summarized as:firstly, a real-time server performance evaluation model is introduced; secondly, a period adaptive mechanism based on change of server load is built; finally, an adaptive load balancing algorithm based on server performance is proposed. The experimental results demonstrate the practicability, availability and stability of the mechanism.