Disk Allocation Methods Using Genetic Algorithm

Dae-Young AHN; Kyu-Ho PARK

Disk Allocation Methods Using Genetic Algorithm

Dae-Young AHN, Kyu-Ho PARK

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The disk allocation problem examined in this paper is finding a method to distribute a Binary Cartesian Product File on multiple disks to maximize parallel disk I/O accesses for partial match retrieval. This problem is known to be NP-hard, and heuristic approaches have been applied to obtain suboptimal solutions. Recently, efficient methods such as Binary Disk Modulo (BDM) and Error Correcting Code (ECC) methods have been proposed along with the restrictions that the number of disks in which files are stored should be a power of 2. In this paper, a new Disk Allocation method based on Genetic Algorithm (DAGA) is proposed. The DAGA does not place restrictions on the number of disks to be applied and it can allocate the disks adaptively by taking into account the data access patterns. Using the schema theory, it is proven that the DAGA can realize a near-optimal solution with high probability. Comparing the quality of solution derived by the DAGA with the General Disk Modulo (GDM), BDM, and ECC methods through the simulation, shows that 1) the DAGA is superior to the GDM method in all the cases and 2) with the restrictions being placed on the number of disks, the average response time of the DAGA is always less than that of the BDM method and greater than that of the ECC method in the absence of data skew and 3) when data skew is considered, the DAGA performs better than or equal to both BDM and ECC methods, even when restrictions on the number of disks are enforced.

Publication: IEICE TRANSACTIONS on Information Vol.E82-D No.1 pp.291-300

Publication Date: 1999/01/25

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Type of Manuscript: PAPER

Category: Computer Systems

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Dae-Young AHN, Kyu-Ho PARK, "Disk Allocation Methods Using Genetic Algorithm" in IEICE TRANSACTIONS on Information, vol. E82-D, no. 1, pp. 291-300, January 1999, doi: .
Abstract: The disk allocation problem examined in this paper is finding a method to distribute a Binary Cartesian Product File on multiple disks to maximize parallel disk I/O accesses for partial match retrieval. This problem is known to be NP-hard, and heuristic approaches have been applied to obtain suboptimal solutions. Recently, efficient methods such as Binary Disk Modulo (BDM) and Error Correcting Code (ECC) methods have been proposed along with the restrictions that the number of disks in which files are stored should be a power of 2. In this paper, a new Disk Allocation method based on Genetic Algorithm (DAGA) is proposed. The DAGA does not place restrictions on the number of disks to be applied and it can allocate the disks adaptively by taking into account the data access patterns. Using the schema theory, it is proven that the DAGA can realize a near-optimal solution with high probability. Comparing the quality of solution derived by the DAGA with the General Disk Modulo (GDM), BDM, and ECC methods through the simulation, shows that 1) the DAGA is superior to the GDM method in all the cases and 2) with the restrictions being placed on the number of disks, the average response time of the DAGA is always less than that of the BDM method and greater than that of the ECC method in the absence of data skew and 3) when data skew is considered, the DAGA performs better than or equal to both BDM and ECC methods, even when restrictions on the number of disks are enforced.
URL: https://global.ieice.org/en_transactions/information/10.1587/e82-d_1_291/_p

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@ARTICLE{e82-d_1_291,
author={Dae-Young AHN, Kyu-Ho PARK, },
journal={IEICE TRANSACTIONS on Information},
title={Disk Allocation Methods Using Genetic Algorithm},
year={1999},
volume={E82-D},
number={1},
pages={291-300},
abstract={The disk allocation problem examined in this paper is finding a method to distribute a Binary Cartesian Product File on multiple disks to maximize parallel disk I/O accesses for partial match retrieval. This problem is known to be NP-hard, and heuristic approaches have been applied to obtain suboptimal solutions. Recently, efficient methods such as Binary Disk Modulo (BDM) and Error Correcting Code (ECC) methods have been proposed along with the restrictions that the number of disks in which files are stored should be a power of 2. In this paper, a new Disk Allocation method based on Genetic Algorithm (DAGA) is proposed. The DAGA does not place restrictions on the number of disks to be applied and it can allocate the disks adaptively by taking into account the data access patterns. Using the schema theory, it is proven that the DAGA can realize a near-optimal solution with high probability. Comparing the quality of solution derived by the DAGA with the General Disk Modulo (GDM), BDM, and ECC methods through the simulation, shows that 1) the DAGA is superior to the GDM method in all the cases and 2) with the restrictions being placed on the number of disks, the average response time of the DAGA is always less than that of the BDM method and greater than that of the ECC method in the absence of data skew and 3) when data skew is considered, the DAGA performs better than or equal to both BDM and ECC methods, even when restrictions on the number of disks are enforced.},
keywords={},
doi={},
ISSN={},
month={January},}

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TY - JOUR
TI - Disk Allocation Methods Using Genetic Algorithm
T2 - IEICE TRANSACTIONS on Information
SP - 291
EP - 300
AU - Dae-Young AHN
AU - Kyu-Ho PARK
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E82-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 1999
AB - The disk allocation problem examined in this paper is finding a method to distribute a Binary Cartesian Product File on multiple disks to maximize parallel disk I/O accesses for partial match retrieval. This problem is known to be NP-hard, and heuristic approaches have been applied to obtain suboptimal solutions. Recently, efficient methods such as Binary Disk Modulo (BDM) and Error Correcting Code (ECC) methods have been proposed along with the restrictions that the number of disks in which files are stored should be a power of 2. In this paper, a new Disk Allocation method based on Genetic Algorithm (DAGA) is proposed. The DAGA does not place restrictions on the number of disks to be applied and it can allocate the disks adaptively by taking into account the data access patterns. Using the schema theory, it is proven that the DAGA can realize a near-optimal solution with high probability. Comparing the quality of solution derived by the DAGA with the General Disk Modulo (GDM), BDM, and ECC methods through the simulation, shows that 1) the DAGA is superior to the GDM method in all the cases and 2) with the restrictions being placed on the number of disks, the average response time of the DAGA is always less than that of the BDM method and greater than that of the ECC method in the absence of data skew and 3) when data skew is considered, the DAGA performs better than or equal to both BDM and ECC methods, even when restrictions on the number of disks are enforced.
ER -