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Research on Cloud-Based Mass Log Data Management Mechanism

机译:基于云的质量日志数据管理机制研究

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—In this paper, we study the file management mechanism of large-scale cloud-based log data. With the rise of big data, there are more and more the Hadoop-based applications. Log analysis is an important part of network security management, but the existing network log analysis system can’t deal with huge amounts of log data, or only use offline mode which with a longer response delay. Therefore, building the online Hadoop-based log processing system is necessary. However, how to effectively manage vast amounts of log data have become the key problems of such system. To this end, this paper puts forward a new hierarchical file archiving (HFA) mechanism which can realize the hierarchical and sorted storage of massive amounts of log data. In addition, some feasible methods for the mechanism are also proposed. Through the HFA mechanism, the traditional log analysis mode and Hadoopbased offline analysis mode can be combined to achieve the online Hadoop-based log analysis system, which have good scalability that can effectively store and handle the massive log data, and faster response speed for user request to meet the requirements of online processing. The feasibility and effectiveness of the HFA mechanism have been verified by the experiment of a small log process system.
机译:- 本文研究了基于大型云的日志数据的文件管理机制。随着大数据的兴起,基于Hadoop的应用程序越来越多。日志分析是网络安全管理的重要组成部分,但现有的网络日志分析系统无法处理大量的日志数据,或者仅使用具有更长响应延迟的离线模式。因此,建立基于在线的基于Hadoop的日志处理系统。但是,如何有效地管理大量日志数据已成为此类系统的关键问题。为此,本文提出了一种新的分层文件归档(HFA)机制,可以实现大量日志数据的分层和排序存储。此外,还提出了用于该机制的一些可行方法。通过HFA机制,传统的日志分析模式和Hadoop基础的离线分析模式可以组合,实现基于在线的基于Hadoop的日志分析系统,这具有良好的可扩展性,可以有效地存储和处理大量的日志数据,以及用户更快的响应速度要求满足在线处理的要求。通过小日志过程系统的实验验证了HFA机制的可行性和有效性。

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