针对当前云网络超宽带存储难以实现流量的分流,且存储过程中存在严重的存储效率低下及存储时延过高等问题,提出了一种基于海量存储云调度机制的云网络数据超带宽存储算法。采用周期调度及梯度优化方式,且综合考虑数据存储中使用强度,最...针对当前云网络超宽带存储难以实现流量的分流,且存储过程中存在严重的存储效率低下及存储时延过高等问题,提出了一种基于海量存储云调度机制的云网络数据超带宽存储算法。采用周期调度及梯度优化方式,且综合考虑数据存储中使用强度,最小传输粒度等数字特征,对数据存储过程中的指纹梯度进行优化,且将该梯度引入到数据传输过程中,成功地实现了数据的流量分离,提高了数据存储效率。仿真实验表明,与当前广泛使用的超线性存储调度算法(super linear memory scheduling algorithm,SLMS)相比,本文算法的存储效率更高,能够在流量分离的前提下显著降低存储时延。展开更多
To explore the spatial heterogeneity of plankton communities in a deep artificial lake （Songhua Lake, China）, samples were collected at seven sites. Samples were investigated by denaturing gradient gel electrophores...To explore the spatial heterogeneity of plankton communities in a deep artificial lake （Songhua Lake, China）, samples were collected at seven sites. Samples were investigated by denaturing gradient gel electrophoresis （DGGE） analysis of the PCR-amplified 16S and 18S rRNA genes and specific bands were sequenced. Cluster analysis of the DGGE profiles revealed that all of the samples grouped into two distinct clusters, in accordance with sampling site; while in each cluster, the divergence of sub-clusters correlated with sampling depth. Sequence analysis of selected dominant DGGE bands revealed that most sequenced phylotypes （84%） exhibited 〉97% similarity to the closest sequences in GenBank, and were affiliated with ten common freshwater plankton phyla （Proteobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria, Bacillariophyta, Pyrrophyta, Cryptophyta, Ciliophora, Stramenopiles, and Rotifera）. Several of these groups are also found worldwide, indicating the cosmopolitan distribution of the phylotypes. The relationships between DGGE patterns and environmental factors were analyzed by redundancy analysis （RDA）. The results suggested that, total nitrogen, nitrate, nitrite, temperature were strongly correlated with the variation ammonia, and CODMn concentrations, and water in plankton composition.展开更多
文摘针对当前云网络超宽带存储难以实现流量的分流,且存储过程中存在严重的存储效率低下及存储时延过高等问题,提出了一种基于海量存储云调度机制的云网络数据超带宽存储算法。采用周期调度及梯度优化方式,且综合考虑数据存储中使用强度,最小传输粒度等数字特征,对数据存储过程中的指纹梯度进行优化,且将该梯度引入到数据传输过程中,成功地实现了数据的流量分离,提高了数据存储效率。仿真实验表明,与当前广泛使用的超线性存储调度算法(super linear memory scheduling algorithm,SLMS)相比,本文算法的存储效率更高,能够在流量分离的前提下显著降低存储时延。
基金Supported by the National Natural Science Foundation of China(No.51178090)the National Key Technology R&D Program of China(No.2012BAJ21B02-02)the National Water Pollution Control and Management Technology Major Projects(No.2009ZX07106-001)
文摘To explore the spatial heterogeneity of plankton communities in a deep artificial lake （Songhua Lake, China）, samples were collected at seven sites. Samples were investigated by denaturing gradient gel electrophoresis （DGGE） analysis of the PCR-amplified 16S and 18S rRNA genes and specific bands were sequenced. Cluster analysis of the DGGE profiles revealed that all of the samples grouped into two distinct clusters, in accordance with sampling site; while in each cluster, the divergence of sub-clusters correlated with sampling depth. Sequence analysis of selected dominant DGGE bands revealed that most sequenced phylotypes （84%） exhibited 〉97% similarity to the closest sequences in GenBank, and were affiliated with ten common freshwater plankton phyla （Proteobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria, Bacillariophyta, Pyrrophyta, Cryptophyta, Ciliophora, Stramenopiles, and Rotifera）. Several of these groups are also found worldwide, indicating the cosmopolitan distribution of the phylotypes. The relationships between DGGE patterns and environmental factors were analyzed by redundancy analysis （RDA）. The results suggested that, total nitrogen, nitrate, nitrite, temperature were strongly correlated with the variation ammonia, and CODMn concentrations, and water in plankton composition.
