Vectorisation of Program Codes for Machine Learning Based Resource Estimation

The manual limitation of software-related resources (memory quantity, processing time) might be challenging even in modern desktop operating systems or on server computers. The applications are always competing for the available resources and those of greater need for resources may starve the others. In case of server computers, outages can happen in this period. A previous solution for this problem was to install resource intensive applications on a physically separate computer therefore they did not affect other applications. With the appearance of virtualisation on one physical computer more virtual machines are running simultaneously. Each of their resources can be limited separately. In this case the abundance of virtual machines means the unnecessary waste of resources. Another more suitable method is the use of containers which run on the operating system’s kernel, however these provide an isolated, limitable environment. The addressed problem to solve is the estimation of resources needed which even nowadays is often working on an empirical basis. In our research we are analysing the source code of applications by static code analysis technique, making a composite call graph and a dependency graph. With the descriptors and characteristics of the graphs there is an opportunity to train neural networks the programmed estimation of required resources. [1]