一种基于HSFC的云资源定位算法

An HSFC-based Cloud Source Discovery Algorithm

针对云计算环境下,云资源的模糊查询问题,提出了一种云资源定位算法。该算法建立在双层Chord环模型上,同时结合Hilbert空间填充曲线(HSFC),实现多维属性的降维,进而完成云资源的定位。另外,该算法将整个资源空间划分成多个资源区间,并提出邻居区间的概念,通过邻居区间,可较好地实现云资源的模糊查询,此外该算法还为每个属性设置属性权值,以此减少网络请求数量。实验表明,该算法不但能有效解决云资源的模糊查询,且能降低查询时延,提高查询效率。

A cloud resource discovery algorithm is proposed for the fuzzy query of cloud resource in cloud computing. Based on a hierarchical Chord model and combined with Hilbert Space Filling Curve（ HSFC）,this algorithm realizes dimensionality reduction of multidimensional attributes and then completes cloud source discovery. In addition,the source space is divided into several multidimensional intervals,and the concept of neighbor interval is put forward,with which the fuzzy query of cloud resource is resolved. Every attribute is given a weight to reduce requests in the network. The experimental results show that the algorithm not only resolves the fuzzy query of cloud resource,but also reduces delay as much as possible to improve query efficiency.