基于量化特征的云资源平台自适应调度方法

发布时间：2018-05-14 23:02

本文选题：云资源 + 量化特征　；参考：《科技通报》2015年12期

【摘要】：通过对云资源平台的自适应调度设计,优化整合云存储空间资源,提高系统的运行效率和资源利用率。在云资源平台任务执行中收到用户行为特征干扰因素较多,调度响应出现时滞,传统方法采用基于时间尺度分析的云资源调度方法,当资源出现随机性干扰时受到网络延迟的影响较大,性能不好。提出一种基于量化特征提取的云资源平台自适应调度方法。构建资源调度平台的总体框架结构,进行资源信息特征提取,采用云资源量化特征提取结果作为调度系统的输入函数,对多源资源信息系统访问特征进行最小方差估计,整个调度过程是一个严平稳的随机过程,通过量化特征提取,能保证对云资源各个调度节点的遍历历经性,提高调度准确性。仿真结果表明,采用个算法能有效实现对云资源平台的自适应调度,抗干扰能力强,资源利用率较高。
[Abstract]:Through the adaptive scheduling design of cloud resource platform, the cloud storage space resources are optimized and integrated to improve the system running efficiency and resource utilization. In the cloud resource platform task execution, there are many interference factors of user behavior characteristics and delay in scheduling response. Traditional methods adopt cloud resource scheduling method based on time scale analysis. When the resources appear random interference, the network delay has a great influence, and the performance is not good. An adaptive scheduling method for cloud resource platform based on quantization feature extraction is proposed. The general framework of resource scheduling platform is constructed and the feature extraction of resource information is carried out. The result of quantization feature extraction of cloud resource is used as the input function of scheduling system to estimate the minimum variance of the access feature of multi-source resource information system. The whole scheduling process is a strictly stationary stochastic process. By quantifying the feature extraction the ergodic of each scheduling node of cloud resources can be guaranteed and the scheduling accuracy can be improved. The simulation results show that the proposed algorithm can effectively implement adaptive scheduling of cloud resource platform with strong anti-jamming ability and high resource utilization.
【作者单位】：重庆工程职业技术学院;
【基金】：高职计算机专业基础课程资源共享平台建设研究与实践——以JAVA为例(课题编号:142079)
【分类号】：TP333

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