Cloud Radio Access Network dimensioning

As the data traffic, as well as the speed demands, increases, the mobile networks require means for economically fulfil these demands. The solution comes from the cloud. In order to move the processing to the cloud, it must be carefully dimensioned to know how much resources each situation requires. This means there must be a way to calculate from the traffic the virtual machines required and the hardware resources the virtual machines need, when the cloud infrastructure used is OpenStack. This thesis provides two methods for calculating the virtual machines from the traffic profile. The first one is based on performance testing of the virtual network functions and the second one is based on machine learning technique called multiple linear regression analysis. Furthermore in this work, approximation algorithms are being used in order to solve multidimensional variates of classical optimization problems such as bin packing problem and subset sum problem. These algorithms are used to dimension required resources from the virtual machines to hardware and vice versa. The algorithms are bundled to a program with a graphical user interface to make as user friendly as possible.