Thread pools, integrated in programming languages, packages and dependencies are widely used by developers. Thread pools assume they are running alone on the system, which is not always the case. Previous research has shown that adapting thread pool size has been effective under specific conditions. In this research, scaling the thread pool with respect to CPU and network usage is examined. However, simpler metrics can achieve better results. Two solutions are provided for algorithmically scaling the thread pool, both with a different use-case in mind. Next to that an improved way to collect CPU and network metrics is provided, allowing for real-time thread-based measurements. The result of the scaling solution is improved performance, while also offering reduced energy usage. This research shows that when multiple thread pools are running on the same machine, performance and efficiency is improved.

This report describes the process, motivation and design choices made during the Bachelor End Project in collaboration with DutchSec. The project consists of implementing Lua-scripting into Honeytrap, which is programmed in Go. The following chapters will discuss which design choices were made, how the research was performed and how the final functionalities were implemented. A detailed system verification is done with proof of added value and besides that the system testing methods are described. Furthermore a conclusion is given that discusses what the project has achieved, what the use-cases are and whether it does what the client wants it to do.