面向自动参数调优的动态负载匹配方法

针对OtterTune等自动参数调优方法采用静态负载描述不够准确的问题,提出了一种动态负载的时序描述方式和动态负载匹配方法。首先,提出一种动态负载描述方法来更准确地刻画负载变化;接着,对于负载匹配中序列不规则、欧氏距离算法不再适用等问题,基于动态时间规整(DTW)算法提出了一种使用数据对齐思想的动态负载匹配算法;最后,将所提出的方法应用于OtterTune上从而形成了基于动态负载的调优工具D-OtterTune,并对其进行若干实验。实验结果表明,和OtterTune原方法相比,动态负载匹配方法对自动参数调优的负载匹配的准确度有稳定幅度的提升,使D-OtterTune准确度提升达到3%。可见在数据密集型应用中,D-OtterTune对整体业务性能可以产生明显影响。

Ontology-Driven Discovering Model for Geographical Information Services

This paper proposes an ontology-driven discovering model for the geographical information services to improve their recall ratio and precision ratio. This model uses the geographical information service ontology. In this paper, first we study the multilevel matching arithmetic of geographical information services. This arithmetic is used for filtering and matching the services in the service register center according to the similarity between services selected and services requested from the definition of the function similarity and credit standing similarity. The matching arithmetic, geographical information service ontology and semantic description constitute the discovering model. Finally, we test and analyze the model from the recall ratio, precision ratio, responsivity and load balance. The result indicates that the ontology-driven discovering model is excellent in recall ratio and precision ratio, and can maintain the dynamic load balance of service copy.