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Replication and Workload Management ...

Qin, Dai.

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Replication and Workload Management for In-Memory OLTP Databases.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Replication and Workload Management for In-Memory OLTP Databases./
作者:	Qin, Dai.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	108 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
標題:	Computer engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28490437
ISBN:	9798522945411

Replication and Workload Management for In-Memory OLTP Databases.
Qin, Dai.

Replication and Workload Management for In-Memory OLTP Databases. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 108 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--University of Toronto (Canada), 2021.

This item must not be sold to any third party vendors.

Online transaction processing (OLTP) databases are a critical component of modern computing infrastructure services. As a result, they must be highly available, and they must process requests efficiently for a wide range of workloads. Databases provide high availability by using replication so that when a database replica fails, a backup can take over. They use workload management mechanisms for efficiently supporting different types of workloads with varying levels of skew and contention.This thesis revisits the challenges of database replication and workload management for in-memory databases. Unlike traditional disk-based databases, in-memory databases are designed for workloads whose entire dataset fits in DRAM memory. These databases are highly scalable, raising challenges for replication and workload management. For example, traditional database replication schemes suffer from the network, instead of storage, bottlenecks because in-memory databases have much higher throughput, and traditional workload management solutions significantly limit the performance of in-memory databases.In this thesis, we propose using deterministic concurrency control as the basis for replication and workload management. Deterministic concurrency control allows transactions to execute concurrently while guaranteeing equivalence to a predetermined serial ordering of transactions. For data replication, we propose a replay-based scheme that executes transactions concurrently and scalably on the backup database in the serial order predetermined by the primary database. Our solution reduces network bandwidth requirements to 10-15% of traditional database replication schemes. For workload management, we propose two optimizations to deterministic concurrency control that help parallelize internal database operations. These optimizations enable handling contention and skewed workloads efficiently and provide 30% to 6x performance improvements.

ISBN: 9798522945411Subjects--Topical Terms:

621879
Computer engineering.
Subjects--Index Terms:

Online transaction processing

Replication and Workload Management for In-Memory OLTP Databases.
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506 $a This item must not be sold to any third party vendors.
520 $a Online transaction processing (OLTP) databases are a critical component of modern computing infrastructure services. As a result, they must be highly available, and they must process requests efficiently for a wide range of workloads. Databases provide high availability by using replication so that when a database replica fails, a backup can take over. They use workload management mechanisms for efficiently supporting different types of workloads with varying levels of skew and contention.This thesis revisits the challenges of database replication and workload management for in-memory databases. Unlike traditional disk-based databases, in-memory databases are designed for workloads whose entire dataset fits in DRAM memory. These databases are highly scalable, raising challenges for replication and workload management. For example, traditional database replication schemes suffer from the network, instead of storage, bottlenecks because in-memory databases have much higher throughput, and traditional workload management solutions significantly limit the performance of in-memory databases.In this thesis, we propose using deterministic concurrency control as the basis for replication and workload management. Deterministic concurrency control allows transactions to execute concurrently while guaranteeing equivalence to a predetermined serial ordering of transactions. For data replication, we propose a replay-based scheme that executes transactions concurrently and scalably on the backup database in the serial order predetermined by the primary database. Our solution reduces network bandwidth requirements to 10-15% of traditional database replication schemes. For workload management, we propose two optimizations to deterministic concurrency control that help parallelize internal database operations. These optimizations enable handling contention and skewed workloads efficiently and provide 30% to 6x performance improvements.
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