基于PageRank和基准测试的异构集群节点性能评价算法研究

为了提高集群效率,需要根据集群节点的性能来进行集群的数据部署和任务调度。在异构集群中,节点性能存在很大差异,如何评价节点的性能非常具有挑战性。可以使用基准测试来评价节点的性能,而不同的基准测试对节点评价的角度不尽相同。PageRank算法被谷歌用来对网站进行排名,现在它也被应用于评价书籍的影响力或用户行为等等。提出一种新颖的基于PageRank的节点性能评价算法,以充分利用不同基准测试的评价结果。首先对每个节点使用LINPACK、NPB、IOzone等主流基准测试进行评价;然后采用PageRank算法处理每个基准测试的执行结果,从而得到节点的性能。为了使用PageRank算法,建立了1个图模型,并计算了性能向量和概率转移矩阵。该算法具有计算复杂度低、综合评价效果好等优点。

A node performance evaluation method for heterogeneous clusters based on PageRank and benchmarks

For a cluster to achieve its maximum throughput, data placement and task scheduling should be handled according to the performance of the cluster nodes. In a heterogeneous cluster, each node has quite different performance, and how to evaluate nodes’ performance is a challenge issue. Normally, nodes are evaluated by benchmarks, and different benchmarks evaluate the nodes from different aspects. PageRank algorithm is used by Google to rank web sites and now it is also applied to evaluate the influence of books or users' behavior, etc. A novel PageRank based node performance evaluation algorithm is proposed to take advantage of the evaluation results from different benchmarks. Firstly, each node is evaluated by mainstream benchmarks, such as LINPACK, NPB and IOzone. Secondly, PageRank algorithm is applied to calculate the nodes’ performance according to the execution results from each benchmark. In order to use PageRank algorithm, a graph model is established, and performance vectors and probability transition matrix are also calculated. The proposed algorithm can produce comprehensive evaluation results with low computational complexity.