A budget-constrained combinatorial optimal facility investment problem is a problem that optimally locates multiple facilities on the vertices of a given undirected graph and allocates many different types of equipment to each facility under a budget constraint. The objective function is not given explicitly, and we can know the evaluation of our investment through a given noisy blackbox function. We propose a search method using Monte Carlo tree search (MCTS) for this problem with a noisy blackbox function. In our search through MCTS, we make use of the lattice structure of subspaces defined using a hierarchical range partition for each variable and reinforce to select good order of variable range partitions preferentially using the upper confidence bound score, into which rapid action value estimation method is incorporated, for selection operations. The effectiveness of our proposed method is demonstrated through the experiments for the layout optimization problem of distributed small power grids (nanogrids) using a public simulator and data that are based on real-world maps, population, and weather as a noisy blackbox function and another setting including an undirected graph, respectively.

A new control scheme for eliminating garbage collection during high-speed analysis of big-graph data stored in NAND flash memory is proposed and evaluated. During big-graph analysis, intermediate results of the analysis are stored in NAND flash memory and updated repeatedly. Under a conventional control scheme, excessive data copying, called “garbage collection,” occurs because overwriting data to NAND flash memory is prohibited. Such excessive data copying degrades performance of big-graph analysis. In contrast, under the proposed control scheme, the controller of NAND flash memory writes the intermediate results, which are updated at the same time, to the same block of NAND flash memory, and the excessive data copying is eliminated completely because all the data in the block can be erased at the same time before the intermediate results are updated. As a result, the proposed scheme shortens analysis time by 88 percent and increases analysis speed for big graphs 8.7 times. The proposed scheme can be applied to three-dimensional NAND flash memory and increases analysis speed 9.5 times. Also, the proposed scheme can be applied to an emerging high-density memory such as three-dimensional vertical chain-cell phase-change memory. These results show that the proposed control scheme enables high-speed analysis of big graphs.

A new control scheme for eliminating garbage collection during high-speed analysis of big-graph data stored in NAND flash memory is proposed and evaluated. During big-graph analysis, intermediate results of the analysis stored in NAND flash memory are updated repeatedly. When a conventional control scheme is applied, excessive data copying, called “garbage collection,” occurs because overwriting data to NAND flash memory is prohibited. Such excessive data copying degrades the performance of big-graph analysis. When the proposed control scheme is applied, the controller of NAND flash memory writes the intermediate results which are updated at the same time to the same block of NAND flash memory, and the excessive data copying is eliminated completely because all the data in the block can be erased at the same time before the intermediate results are updated. As a result, the proposed control scheme shortens analysis time by 88% and increases analysis speed for big graphs 8.7 times. These results show that the proposed control scheme enables high-speed analysis of big graphs.