计算机工程 ›› 2017, Vol. 43 ›› Issue (12): 30-37.doi: 10.3969/j.issn.1000-3428.2017.12.006

所属专题: 云计算专题

• 云计算专题 • 上一篇    下一篇

考虑服务质量的并行MapReduce启发式车载云资源调度

罗小波,王超   

  1. (重庆邮电大学 计算机科学与技术学院,重庆 400065)
  • 收稿日期:2016-08-19 出版日期:2017-12-15 发布日期:2017-12-15
  • 作者简介:罗小波(1976—),男,副教授、博士,主研方向为云计算、遥感信息反演;王超,硕士研究生。
  • 基金项目:
    国家自然科学基金(61272195);重庆市教委科学技术研究项目(KJ12057,KJ1402801)。

Parallel MapReduce Heuristics On-board Cloud Resource Scheduling Considering Quality of Service

LUO Xiaobo,WANG Chao   

  1. (School of Computer Science and Technology,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)
  • Received:2016-08-19 Online:2017-12-15 Published:2017-12-15

摘要: 为提高车载云计算资源调度的可靠性,减少数据处理时间,提出一种服务质量感知的并行MapReduce启发式车载云资源调度算法。在MapReduce并行计算模型的基础上,设计云计算环境中以车载单元为基础的车辆并行检测服务框架,利用相对优先级因子构建车载云计算调度模型,并通过启发式并行优化算法对模型进行优化,降低算法复杂度。在NS-3中的仿真结果表明,该算法可有效缩短作业执行时间,并具有较高的可靠性。

关键词: 服务质量, 并行云计算, MapReduce模型, 车载云资源, 启发式调度算法

Abstract: In order to improve the reliability of on-board cloud computing resource scheduling and reduce the computation time of data processing,a parallel MapReduce heuristics on-board cloud resource scheduling algorithm with Quality of Service(QoS) perception is proposed.Based on the MapReduce parallel computing model,the On-Board Unit(OBU)-based vehicle parallel detection service framework in cloud computing environment is designed,and the relative priority factor is used to construct the on-board cloud computing scheduling model.Then the cloud resource scheduling model is optimized by using heuristic parallel optimization algorithm to reduce the computational complexity of the proposed algorithm.The simulation results in NS-3 show that the proposed algorithm can shorten the job execution time effectively and has higher reliability.

Key words: Quality of Service(QoS), parallel cloud computing, MapReduce model, on-board cloud resource, heuristic scheduling algorithm

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