Region-based Memory Management and Actor Model Concurrency An initial study of how the combination performs

Sammanfattning: Modern computer systems and the requirements we place upon them are vastly different from those of early systems. With the emergence of Internet of Things (IoT) devices, the number of devices with hard, real-time deadlines have increased greatly. The presence of a garbage collector does not resonate well with such systems, as garbage collectors typically use a stop-the-world approach. The problem is amplified further in languages such as Erlang, where it is commonplace to spawn many processes. In Erlang, each process has its own heap which is individually garbage collected. A common design of an IoT device is a board with several different sensors and peripherals. The presence of so many garbage collectors should be enough to deter us from using Erlang to program IoT devices, but the idea of writing small, isolated programs to manage each of the sensors is appealing. Another memory management principle, one which could eliminate the need for a garbage collector, is called Region-based Memory Management. This thesis has investigated how well current Region-based Memory Management techniques work when they are applied to a setting that implements Actor Model concurrency. To investigate this an Actor Model concurrency-library has been implemented in Standard ML and compiled with the MLKit compiler - a compiler which uses Region-Based memory management. Evaluating the speed and memory performance of the library shows that the combination performs poorly. The Region-inference algorithm employed by the compiler struggles with identifying when data can be deallocated and retains most data throughout the execution of a program. We identify some key problems and propose how they could be solved. We conclude that current techniques are not well suited in a setting with Actor Model concurrency. We cannot, however, say if the combination of Region-based memory management and the Actor concurrency model works well or not, as further research is required.