| 牟式标,金伟健.基于支持向量机的任务调度模型[J].南华大学学报(自然科学版),2017,31(1):81~84, 95.[MU Shi-biao,JIN Wei-jian.The Tasks Scheduling Model Based on Support Vector Machine[J].Journal of University of South China(Science and Technology),2017,31(1):81~84, 95.] |
| 基于支持向量机的任务调度模型 |
| The Tasks Scheduling Model Based on Support Vector Machine |
| 投稿时间：2016-09-15 |
| DOI： |
| 中文关键词: 云计算 MapReduce 支持向量机 Hadoop |
| 英文关键词:cloud computing MapReduce SVM Hadoop |
| 基金项目:浙江省2016年度高校国内访问工程师“校企合作项目”(FG2016128) |
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| 中文摘要: |
| 云计算框架大大改进了并行算法的实现难度,但是大部分算法有其局限性.介绍了MapReduce(映射化简)的基本实现原理和调度模型的缺陷,提出了基于支持向量机的的MapReduce进化算法,并给出了基本模型及实现.运用Hadoop云计算平台进行了仿真验证,实验结果表明,基于支持向量机的MapReduce计算框架在候选云节点的调度分配的准确性上有明显提高,并且加快了数据迭代的效率. |
| 英文摘要: |
| The cloud framework reduced the difficulty to realize parallel algorithm.But most of the algorithms have the defects.The fundamental and scheduling model of MapReduce are introduced.The evolving algorithm is proposed based on Support Vector Machine.The basis model and realization are built.By simulation realization on Hadoop cloud computing platform,compared with the traditional scheduling algorithm,experimental results show the new theory based on the SVM improve the accuracy of cloud candidate point scheduling and improve the speed of data iteration. |
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