The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
확장성과 가용성은 병렬 데이터베이스 시스템의 주요 기능입니다. 확장성을 실현하기 위해 비공유 병렬 인프라에 데이터 배치 및 병렬 인덱스 구조를 갖춘 동적 로드 밸런싱 방법이 많이 제안되었습니다. 병렬 Btree를 사용한 범위 분할 배치를 통한 데이터 마이그레이션은 하나의 솔루션입니다. 범위 파티셔닝과 체인으로 연결된 디클러스터형 복제본의 조합은 확장성을 유지하면서 고가용성(HA)을 제공합니다. 그러나 각 노드의 기본 데이터와 백업 데이터를 독립적으로 처리하려면 오랜 장애 조치 시간이 필요합니다. 우리는 Fat-Btree라고 불리는 병렬 Btree를 사용하여 체인으로 연결된 디클러스터형 복제본의 복합 처리를 위한 새로운 방법을 제안합니다. 제안된 방법에서 단일 Fat-Btree는 모든 프로세서 요소(PE)의 기본 데이터와 백업 데이터 모두에 대한 액세스 경로를 제공하여 장애 조치 시간을 크게 줄입니다. 또한 이러한 액세스 경로는 인접한 두 PE 간에 겹치므로 액세스 경로를 동적으로 리디렉션하여 물리적 데이터 마이그레이션 없이 동적 로드 밸런싱을 가능하게 합니다. 또한, 이 복합 처리는 메모리 공간 활용도를 향상시켜 확장성이 좋은 인덱스 처리를 가능하게 합니다. 160노드 PC 클러스터에서 PostgreSQL을 사용한 실험은 제안된 방법의 높은 확장성과 가용성의 효율성을 보여줍니다.
병렬 B트리, 확장 성, 유효성, 체인화된 디클러스터링
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Min LUO, Akitsugu WATANABE, Haruo YOKOTA, "A Compound Parallel Btree for High Scalability and Availability on Chained Declustering Parallel Systems" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 3, pp. 587-601, March 2011, doi: 10.1587/transinf.E94.D.587.
Abstract: Scalability and availability are the key features of parallel database systems. To realize scalability, many dynamic load-balancing methods with data placement and parallel index structures on shared-nothing parallel infrastructure have been proposed. Data migration with range-partitioned placement using a parallel Btree is one solution. The combination of range partitioning and chained declustered replicas provides high availability (HA) while preserving scalability. However, independent treatment of the primary and backup data in each node requires long failover times. We propose a novel method for the compound treatment of chained declustered replicas using a parallel Btree, termed the Fat-Btree. In the proposed method, a single Fat-Btree provides access paths to both the primary and backup data of all processor elements (PEs), which greatly reduces failover time. Moreover, these access paths overlap between two neighboring PEs, which enables dynamic load balancing without physical data migration by dynamically redirecting the access paths. In addition, this compound treatment improves memory space utilization to enable index processing with good scalability. Experiments using PostgreSQL on a 160-node PC cluster demonstrate the effectiveness of the high scalability and availability of our proposed method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.587/_p
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@ARTICLE{e94-d_3_587,
author={Min LUO, Akitsugu WATANABE, Haruo YOKOTA, },
journal={IEICE TRANSACTIONS on Information},
title={A Compound Parallel Btree for High Scalability and Availability on Chained Declustering Parallel Systems},
year={2011},
volume={E94-D},
number={3},
pages={587-601},
abstract={Scalability and availability are the key features of parallel database systems. To realize scalability, many dynamic load-balancing methods with data placement and parallel index structures on shared-nothing parallel infrastructure have been proposed. Data migration with range-partitioned placement using a parallel Btree is one solution. The combination of range partitioning and chained declustered replicas provides high availability (HA) while preserving scalability. However, independent treatment of the primary and backup data in each node requires long failover times. We propose a novel method for the compound treatment of chained declustered replicas using a parallel Btree, termed the Fat-Btree. In the proposed method, a single Fat-Btree provides access paths to both the primary and backup data of all processor elements (PEs), which greatly reduces failover time. Moreover, these access paths overlap between two neighboring PEs, which enables dynamic load balancing without physical data migration by dynamically redirecting the access paths. In addition, this compound treatment improves memory space utilization to enable index processing with good scalability. Experiments using PostgreSQL on a 160-node PC cluster demonstrate the effectiveness of the high scalability and availability of our proposed method.},
keywords={},
doi={10.1587/transinf.E94.D.587},
ISSN={1745-1361},
month={March},}
부
TY - JOUR
TI - A Compound Parallel Btree for High Scalability and Availability on Chained Declustering Parallel Systems
T2 - IEICE TRANSACTIONS on Information
SP - 587
EP - 601
AU - Min LUO
AU - Akitsugu WATANABE
AU - Haruo YOKOTA
PY - 2011
DO - 10.1587/transinf.E94.D.587
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2011
AB - Scalability and availability are the key features of parallel database systems. To realize scalability, many dynamic load-balancing methods with data placement and parallel index structures on shared-nothing parallel infrastructure have been proposed. Data migration with range-partitioned placement using a parallel Btree is one solution. The combination of range partitioning and chained declustered replicas provides high availability (HA) while preserving scalability. However, independent treatment of the primary and backup data in each node requires long failover times. We propose a novel method for the compound treatment of chained declustered replicas using a parallel Btree, termed the Fat-Btree. In the proposed method, a single Fat-Btree provides access paths to both the primary and backup data of all processor elements (PEs), which greatly reduces failover time. Moreover, these access paths overlap between two neighboring PEs, which enables dynamic load balancing without physical data migration by dynamically redirecting the access paths. In addition, this compound treatment improves memory space utilization to enable index processing with good scalability. Experiments using PostgreSQL on a 160-node PC cluster demonstrate the effectiveness of the high scalability and availability of our proposed method.
ER -