A Study on The Conversion Factor between Heterogeneous DBMS for Cloud Migration

• Joonyoung Ahn,
• Kijung Ryu,
• Changik Oh,
• Taekryong Han,
• Heewon Kim,
• Dongho Kim,

Vol. 18, No. 8, August 31, 2024
10.3837/tiis.2024.08.022, Download Paper (Free):

• cloud migration
• Scale Calculation
• DBMS
• Conversion Factor
• TPC-H

Abstract

Many legacy information systems are currently being clouded. This is due to the advantage of being able to respond flexibly to the changes in user needs and system environment while reducing the initial investment cost of IT infrastructure such as servers and storage. The infrastructure of the information system migrated to the cloud is being integrated through the API connections, while being subdivided by using MSA (Micro Service Architecture) internally. DBMS (Database Management System) is also becoming larger after cloud migration. Scale calculation in most layers of the application architecture can be measured and calculated from auto-scaling perspective, but the method of hardware scale calculation for DBMS has not been established as standardized methodology. If there is an error in hardware scale calculation of DBMS, problems such as poor performance of the information system or excessive auto-scaling may occur. In addition, evaluating hardware size is more crucial because it also affects the financial cost of the migration. CPU is the factor that has the greatest influence on hardware scale calculation of DBMS. Therefore, this paper aims to calculate the conversion factor for CPU scale calculation that will facilitate the cloud migration between heterogeneous DBMS. In order to do that, we utilize the concept and definition of hardware capacity planning and scale calculation in the on-premise information system. The methods to calculate the conversion factor using TPC-H tests are proposed and verified. In the future, further research and testing should be conducted on the size of the segmented CPU and more heterogeneous DBMS to demonstrate the effectiveness of the proposed test model.

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