摘要
在流媒体大数据调度中,因路径损耗较大,产生调度延时和频谱失真,需要对路径损耗进行优化评估,提高对流媒体大数据库的访问速度和检索定位。提出一种基于交叉集聚变异最小延时的流媒体大数据调度路径损耗评估模型,基于遗传算法设计流媒体数据集聚调度模型,得到流媒体数据调度的位置变换策略。确定流媒体数据编码方案,构建流媒体网络通信代价矩阵控制的数据调度路径损耗模型,实现算法改进。实验结果表明,该模型能使流媒体数据调度路径损耗有效降低,优化流媒体数据传输,最终达到最小传输时延,避免了调度延时和失真。
In the streaming media data scheduling, path loss is larger, the scheduling delay and distortion of the spectrumare produced, optimization evaluation of path loss is needed, improve the convection media database access speed and lo-calization. A evaluation model of streaming media data scheduling path loss is proposed base don cross cluster variationand minimum scheduling delay, genetic algorithm is used to design media data flow scheduling model, transform strategy ofstreaming media data scheduling is obtained. Determination of flow media data encoding scheme is achieved, data schedul-ing path loss model of stream media network communication costs matrix control is constructed, and improved algorithm isrealized. The experimental results show that new method can make the streaming media data scheduling path loss is re-duced effectively, optimize the streaming media data transmission, and finally reaches the minimum transmission delay, toavoid the scheduling delay and distortion.
出处
《科技通报》
北大核心
2014年第12期220-222,共3页
Bulletin of Science and Technology
基金
国家教师基金"十二五"规划重点课题(CTF120510)
关键词
流媒体
大数据
路径损耗
streaming media
large data
path loss
