Resource estimation for executing program codes using machine learning

Nowadays more and more servers are put to use due to the explosive improvement of information technology all around the world. Smart solutions, big data storages and cloud technologies brings with it the ever increasing energy consumption. It is often heard on the news that the energy consumption of some newly built server farm is getting to the level of a smaller city. The question of the near future is the efficient use of energy and avoiding wasteful usage of it. In the context of softwares this can be achieved by running the softwares in optimized hardware environments, using the capacity of the serves to the maximum, allocating more procedures to them until the point where it does not affect performance. In this study we will build a training pattern from hundreds of program codes, vectorizing them based on their main characteristics. Under a dynamic stress test their ideal resource consumption will be determined, then a deep neural network will be trained to determine the minimal resource allocations needed ( memory size, cpu cores) to achieve optimal runtime for a program code that is unknown to it. Based on this it would be possible to develop an optimal distributing algorithm that would place the services on a whole server farm in a way to achieve the desired quality of service using the least amount of hardware.