A study of limitations and performance in scalable hosting using mobile devices

Sammanfattning: At present day, distributed computing is a widely used technique, where volunteers support different computing power needs organizations might have. This thesis sought to benchmark distributed computing performance limited to mobile device support since this type of support is seldom done with mobile devices. This thesis proposes two approaches to harnessing computational power and infrastructure of a group of mobile devices. The problems used for benchmarking are small instances of deep learning training. One requirement posed by the mobile devices’ non-static nature was that this should be possible without any significant prior configuration. The protocol used for communication was HTTP. The reason deep-learning was chosen as the benchmarking problem is due to its versatility and variability. The results showed that this technique can be applied successfully to some types of problem instances, and that the two proposed approaches also favour different problem instances. The highest request rate found for the prototype with a 99% response rate was a 2100% increase in efficiency compared to a regular server. This was under the premise that it was provided just below 2000 mobile devices for only particular problem instances.