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

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

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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
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Faculty of Engineering and Natural Sciences
Depositing User: IC-Cataloging
Date Deposited: 24 Oct 2020 12:33
Last Modified: 26 Apr 2022 10:34
URI: https://research.sabanciuniv.edu/id/eprint/41182

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