A LNS-based data placement strategy for data-intensive e-science applications
Tiantian Zhang, Lizhen Cui, Meng Xu · International Journal of Grid and Utility Computing · 2014
With the growth of data, the era of big data comes. Big data requires a huge amount of storage and new data placement methods. For data-intensive e-science applications in grid and utility computing, they need to process large data sets resided in different data centres. The data transfer across data centres leads to bandwidth cost and time delay. It is more important to solve the data placement problem for these applications. The existing data placement approaches can only reduce the data transfer frequency, but not improve overall performance. In this paper, we propose a data placement strategy based on large neighbourhood search (LNS). The strategy has initial strategy generation stage and relaxation and re-insertion stage. Our strategy offers the improvements in overall performance, especially in reducing time delay.