摘要：

随着基于MapReduce模型的应用程序越来越多，Hadoop性能取决于应用程序。针对上述特性，从应用着手剖析Hadoop存在的局限和不足，提出解决方案，利用作业和任务的多重并发平衡磁盘和网络带宽，减小瓶颈出现的可能性，提高系统性能。

关键词: 分布式计算, 存储, 并发I/O, 性能优化

Abstract:

As applications programs based on MapReduce model growing up, Hadoop’s performance depends on applications. From applications, this paper analyzes the limitations and shortcomings of Hadoop architecture and storage technology, and proposes a feasible and cost-effective solution to address the problem. It builds a series tests to convince the idea. The solution is multi-level parallel, both on job and I/O levels, which makes full use of disk and network bandwidth, reduces I/O bottlenecks and improves performance.

Key words: distributed computing, storage, concurrent I/O, performance optimization

中图分类号: 

• TP302