Abstract-This paper describes the basic components of a research project aimed at the application of natural computing metaheuristics to optimize the horizontal scaling of databases. Column oriented databases were selected for the project because of their unique properties. A mathematical model has been created in order to align the problem of horizontal scalability to the general optimization methods, such as natural computing algorithms.
Abstract-The massive amounts of data processed by information systems raise the importance of detailed database performance analysis. Column-oriented data stores are becoming increasingly popular in big data appliances. This paper identifies database performance factors on the basis of empirical studies on a custom implementation. To summarize the research, a simple performance mathematical model has been created.
Abstract-Modelling of a database performance depending on numerous factors is the first step towards its optimization. The linear regression model with optional parameters was created. Regression equation coefficients are optimized with the Flower Pollination metaheuristic algorithm. The algorithm is executed with numerous possible execution parameter combinations and results are discussed. Potential obstacles are discussed and alternative modelling approaches are mentioned.
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