PERFORMANCE STUDY OF THE DTU MODEL FOR RELATIONAL DATABASES ON THE AZURE PLATFORM
DOI:
https://doi.org/10.30837/ITSSI.2022.19.027Keywords:
cloud platform;, relational database;, DTU purchase model;, indicators of time and workload;, data generator;, test data;, request complexityAbstract
When solving problems of working with relational databases on cloud platforms, the problem arises of choosing a specific model to ensure the performance of executing queries of varying complexity. The object of research is the processes of implementing various types of queries to relational databases within the framework of the DTU purchase model of the MS Azure platform. The subject is methods for evaluating the performance of work with relational databases based on the timing of query execution and indicators of the load on the resources of the cloud platform. The aim of the study is to develop a system of indicators for monitoring the current state of work with the database for reasonable decision-making on the choice of a certain price category of the DTU model of the MS Azure cloud service, which will optimize the results of working with the database. platforms Achieving the set goals involves the following tasks: to analyze modern tools and services for working with databases, in particular relational databases, on Azure and AWS cloud platforms, the features of their application and implementation; develop software for generating test relational databases of different sizes; test the generated databases on a local resource; taking into account the characteristics of the levels of the Azure DTU model, develop a new system of performance indicators, which includes 2 groups - time indicators and indicators of the load on existing platform resources; develop and implement queries of varying complexity for the generated test database for different levels of the DTU model and analyze the results. Methods. The following methods were used in the research: methods of relational database design; methods of creating queries in SQL-oriented databases with any number of tables; methods of creating and migrating data to cloud platforms; methods of monitoring the results of queries based on time and resource indicators; methods of generating test data for relational databases; system approach for complex assessment and analysis of productivity of work with relational databases. Results. On the basis of the developed scorecard used for the current analysis of the processes of working with relational databases of the MS Azure platform, numerous experiments were carried out for different levels of the model for simple and complex queries to a database with a total volume of 20 GB: loading of DTU model levels when executing various queries, the influence of model levels DTU Azure SQL database on the performance of simple and complex queries, the dependence of the execution time of various queries on the load of the CPU and the speed of write/read operations for different levels of the model. Conclusions. The results of the experiments allow us to conclude that the levels of the DTU model - S3 and S7 - are used to generate test data of various sizes (up to 20 GB) and execute database queries. The practical use of the proposed indicators to evaluate the results of applying the DTU model will improve the efficiency of decision-making on choosing the model level when implementing various queries and generating test data on the Azure cloud platform. The developed set of indicators for working with relational databases on the Azure cloud platform expands the basis of the methodological framework for evaluating the performance of working with relational databases on cloud platforms by analyzing the results of executing the simple and complex database queries on the resources involved.
References
TOP 10 cloud trends in 2018, available at: https://ecm-journal.ru/post/TOP-10-oblachnykh-tendencii-v-2018-godu.aspx.
What is cloud computing?, available at: https://aws.amazon.com/ru/what-is-cloud-computing/.
Gartner., available at: https://www.gartner.com/en.
Marinescu, D., C. (2018), "Cloud Service Providers and the Cloud Ecosystem", Cloud Computing, No. 2, P. 13 – 49. DOI: https://doi.org/10.1016/B978-0-12-812810-7.00002-9
Database prices SQL Azure, available at: https://azure.microsoft.com/ru-ru/pricing/details/sql-database/single/.
Database SQL Azure, available at: https://azure.microsoft.com/ru-ru/services/sql-database/.
"How to migrate to Amazon RDS for SQL Server using transactional replication", available at: https://aws.amazon.com/ru/blogs/database/how-to-migrate-to-amazon-rds-for-sql-server-using-transactional-replication/.
Amazon RDS pricing for SQL Server, available at: https://aws.amazon.com/ru/rds/sqlserver/pricing/.
Delaney, K. (2018), Microsoft Azure Options for SQL Server Relational Databases, Microsoft Corporation, 22 p.
Marozzo, F. (2019), "Infrastructures for High-Performance Computing: Cloud Infrastructures", Reference Module in Life Sciences, No. 1, P. 240 – 246. DOI: https://doi.org/10.1016/B978-0-12-809633-8.20374-9
Boukhelef, D., Boukhobza, J., Boukhalfa, K., Ouarnoughi, H., Lemarchand, L (2019),"Optimizing the cost of DBaaS object placement in hybrid storage systems", Future Generation Computer Systems, Vol. 93, P. 176 – 187.
DOI: https://doi.org/10.1016/j.future.2018.10.030
Saif, S., Wazir, І. (2018), "Performance Analysis of Big Data and Cloud Computing Techniques: A Survey", Procedia Computer Science, No. 132, P. 118 – 127. DOI: https://doi.org/10.1016/j.procs.2018.05.172
Importing and Exporting SQL Server Databases, available at:https://docs.aws.amazon.com/en_us/AmazonRDS/latest/UserGuide/ SQLServer.Procedural.Importing.html.
Resource limits for single databases using the DTU-based purchasing model, available at: https://docs.microsoft.com/en-us/azure/sql-database/sql-database-dtu-resource-limits-single-databases.
Azure SQL Database DTU Calculator, available at: http://dtucalculator.azurewebsites.net/.
Passos da Costa, P. J., Rosado da Cruz, A. M. (2012), "Migration to Windows Azure – Analysis and Comparison", Procedia Technology, Vol. 5, P. 93–102. DOI: https://doi.org/10.1016/j.protcy.2012.09.011
Han, J., Kamber, M., Pei, J. (2012), "Data Warehousing and Online Analytical Processing".
DOI: https://doi.org/10.1016/B978-0-12-381479-1.00004-6
Dabbechi, H,. Nabli, A., and Bouzguenda, L. (2016), "Towards Cloud-Based Data Warehouse as a Service for Big Data Analytics", Lecture Notes in Computer Science, 9876, P.180–189. DOI: https://doi.org/10.1007/978-3-319-45246-317
Zolotariov, D. (2020), "The system of automated billing on the basis of bad infrastructure has been developed" [ "Rozpodіlena sistema avtomatizovanih obchislen' na bazі hmarnoї іnfrastrukturi"], Innovative technologies and scientific solutions for industry, No. 4 (14), P.47-56. DOI: https://doi.org/10.30837/ITSSI.2020.14.047
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