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。
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.
出处
《计算机工程》
CAS
CSCD
北大核心
2017年第12期30-37,共8页
Computer Engineering
基金
国家自然科学基金(61272195)
重庆市教委科学技术研究项目(KJ12057
KJ1402801)
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
Quality of Service （QoS）
parallel cloud computing
MapReduce model
on-board cloud resource
heuristic scheduling algorithm
