Dynamic allocation of servers for large scale rendering application

Detta är en Uppsats för yrkesexamina på avancerad nivå från Luleå tekniska universitet/Institutionen för system- och rymdteknik

Författare: Samuel Andersson; [2021]

Nyckelord: Proactive; Render; Rendering; Dynamic; Allocation; Servers;

Sammanfattning: Cloud computing has been widely used for some time now, and its area of use is growing larger and larger year by year. It is very convenient for companies to use cloud computing when creating certain products, however it comes with a great price. In this thesis it will be evaluated if one could optimize the expenses for a product regardless of what platform that is used. And would it be possible to anticipate how much resources a product will need, and allocate those machines in a dynamic fashion?       In this thesis the work of predicting the need of rendering machines based on response times from user requests, and dynamically allocate rendering machines to a product based on this need will be evaluated. The solution used will be based on machine learning, where different types of regression models will try to predict the response times of the future, and evaluate whether or not they are acceptable. During the thesis both a simulation and a replica of the real architecture will be implemented. The replica of the real architecture will be implemented by using AWS cloud services.       The resulting regression model that turned out to be best, was the simplest possible. A linear regression model with response times as the independent variable, and the queue size per rendering machine was used as the dependent variable. The model performed very good in the region of realistic response times, but not necessarily that good at very high response times or at very low response times. That is not considered as a problem though, since response times in those regions should not be of concern for the purpose of the regression model. The effects of the usage of the regression model seems to be better than in the case of using a completely reactive scaling method. Although the effects are not really clear, since there is no user data available. In order for the effects to be evaluated in a fair way, there is a need of user patterns in terms of daily usage of the product. Because the requests in the used simulation are based on pure randomness, there is no correlation in what happened 10 minutes back in the simulation and what will happen 10 minutes in the future. The effect of that is that it is really hard to estimate how the dependent variable will change over time. And if that can not be estimated in a proper way, the results with the inclusion of the regression model can not be tested in a realistic scenario either.

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