The scope of the master thesis was to model cost and risk factors of virtual computing labs (Apache VCL) in order to make cost, risk and service performance optimizing researches on hybrid Apache VCL cloud setups. Using the created model a hypothetical Apache VCL implementation project at a hypothetical Hungarian university had to be optimized based on predefined criteria for a specific load profile.
At the beginning I got familiar with the open source Apache VCL and the Apache VCL used at the Department of Measurement and Information Systems (DMIS) of Budapest University of Technology and Economics (BME) in order to understand the processes of computer allocations in Apache VCL to be able to simulate them later on. There were three main types of allocations in Apache VCL which were circuitously recognized and described. Using this knowledge I created a filtered, simplified model of Apache VCL in order to simulate only the relevant parts.
After having the appropriate information about Apache VCL and the simplified model the next step was to find such a cloud simulator that could be used to truly model and simulate Apache VCL including the handling of reservation requests and slot management as well. Eventually, the open source CloudSim cloud simulator was chosen. CloudSim cannot be used to simulate Apache VCL without changes in its source code. This modification contained four must have conceptual features which were missing from CloudSim: cost, system utilization, future request and service performance modeling related to hybrid cloud setups.
Finally, the relevance and usability of the extended CloudSim was demonstrated through a hypothetical implementation project of Apache VCL at a Hungarian university. During the case study I have proven that the created simulator can be used to get cost optimal cloud setups for assumed future image requests. Decision making was done by unit cost and service performance indicator of cloud infrastructure implementation projects.
The conclusion is that there is an optimal hybrid cloud configuration where the unit cost of image requests is minimal while the service performance indicator has its maximum. Smaller unit cost can be only reached if the quality of the service is decreased.