中文摘要：

数据网格已逐步在科学研究领域得到应用．提高数据网格的性能以适应分布式数据管理已经成为研究数据网格的一个热点．提出了网格局部性的概念，分析了网格局部性对数据网格性能的影响，并从增强网格局部性的角度对数据网格的性能进行优化，提出了综合跳一扩散副本替换策略（jump-DRP）和参考生物外激素的任务调度策略（JARIP）．实验结果表明，考虑了网格局部性因素的jump-DRP与JARIP的策略组合提高了网格平台的任务处理性能，并对各类应用背景及任务的复杂程度具有鲁棒性．

英文摘要：

Data grid is playing an important role in scientific research work. Essentially, data grid is an infrastructure that manages large scale data sets and provides computational resources across widely distributed communities. It has been a research hotspot to improve the performance of data grid platform used to handle and manage large distributed data files. Data file replacements due to limited local storage and grid job assignments are key elements to the efficient data grid platform. Presented here is the concept of grid locality, which involves in job locality and file locality. And the impact on the performance of data grid produced by the grid locality is analyzed. Further, the performance improvement of data grid platform is studied in the perspective of grid locality. Considering the enhancement of grid locality, a composite policy focusing on file replacement and job assignment is put forward, which is jump-DRP （jump-diffusion replacement policy）, as well as JARIP （job assignment referencing to insect pheromone）. Jump-DRP is based on jump-diffusion features and JARIP is based on insect pheromone characteristics. Application simulation is carried out at the file access patterns of sequential, unitary random walk and Gaussian random walk, whereas job submission is in accordance with Gaussian distribution. Experiment results show the integration of jump-DRP and JARIP is robust for both various applications and diverse jobs on data grid platform.