云计算中相似驱动的并行任务划分方法

云计算是并行计算、分布式计算和网格计算等高性能计算的进一步发展,它的异构性、按需等特征对高性能计算提出了新的挑战。针对云计算的典型特征,提出了基于并行任务和云环境相似驱动的任务划分方法。首先用图刻画了并行任务和云环境,建立了图的相似关系及其相似度计算方法;其次给出云计算中拟解决的问题,通过图局部相似和全局相似度偏差最小来实现并行任务和体系结构的异构匹配及按需要求;接着利用F度标号方法给出相似驱动的任务划分算法;最后通过实验和其他划分方法进行比较,阐明了该方法的优点。

Similarity-Driven Parallel Task Partitioning Method for Cloud Computing

Cloud computing is further developed by parallel computing, distributed computing and grid computing. Its characters of heterogeneity and on-demand are new challenge for high performance computing. Based on typical features of cloud computing,this paper proposes a similarity-driven parallel task partitioning method. Firstly, it describes parallel tasks and cloud architecture by graph and builds a graph-similarity definition. Secondly, it provides the problem intended to solve in cloud computing, gives the method by local similarity and minimizing global similarity deviation, and realizes heterogeneous matching between parallel task and architecture and on-demand require. Thirdly, by F-degree labels it proposes a similarity-driven task partitioning method. Finally, compared with other methods, the experimental results verify the advantages of the proposed method.