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Load balancing by using machine learning in CPU-GPU heterogeneous database management system

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Elakaş, Anıl (2020) Load balancing by using machine learning in CPU-GPU heterogeneous database management system. [Thesis]

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Official URL: https://risc01.sabanciuniv.edu/record=b2486360 _(Table of contents)

Abstract

Conventional OLTP systems are slow in performance for analytical queries. In the existing heterogeneous architecture OLAP database management systems, no system distributes work using machine learning. In this study, the DOLAP architecture, which is a high-performance column-based database management system developed for shared memory architectures, is explained. Also, job distribution algorithms based on heuristic and machine learning methods have been developed for computing hardware with different characters such as CPU and GPU on the server on which the database is running, and their performance has been analyzed

Item Type:Thesis
Uncontrolled Keywords:load balancing. -- database management systems. -- machine learning. -- high performance computing. -- query optimization. -- yük dengeleme. -- veritabanı yönetim sistemleri. -- makine ögrenmesi. -- yüksek performansla hesaplama. -- sorgu eniyilemesi.
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics > TK7885-7895 Computer engineering. Computer hardware
ID Code:41182
Deposited By:IC-Cataloging
Deposited On:24 Oct 2020 12:33
Last Modified:24 Oct 2020 12:33

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