An important aspect of managing multi access point (AP) IEEE 802.11 networks is the support for mobility management by controlling the handover process. Most handover algorithms, residing on the client station (STA), are reactive and take a long time to converge, and thus severely impact Quality of Service (QoS) and Quality of Experience (QoE). Centralized approaches to mobility and handover management are mostly proprietary, reactive and require changes to the client STA. In this paper, we first created an Software-Defined Networking (SDN) modular handover management framework called HuMOR, which can create, validate and evaluate handover algorithms that preserve QoS. Relying on the capabilities of HuMOR, we introduce ABRAHAM, a machine learning backed, proactive, handover algorithm that uses multiple metrics to predict the future state of the network and optimize the load to ensure the preservation of QoS. We compare ABRAHAM to a number of alternative handover algorithms in a comprehensive QoS study, and demonstrate that it outperforms them with an average throughput improvement of up to 139%, while statistical analysis shows that there is significant statistical difference between ABRAHAM and the rest of the algorithms.

This article presents a low-cost laboratory that has been designed and developed to enhance learning experience and help students gain skills and knowledge in the field of distributed systems. In order to build a comprehensive distributed file system, we used the laboratory consisting of 40 card-sized Raspberry Pi devices, with the accent on stability, scalability, and low cost. Aiming to assess the impact of this new learning environment on the learning process and its outcomes, we surveyed students following the completion of three project stages during the 17 laboratory exercises in one academic year, ensuring that we maintained the same subjects of study during the experiments. Supported by interesting answers on various sets of questions, we provide a valuable insight into students' experience, obstacles and observations during system's implementation. This particular insight paves the way toward further laboratory improvement, adopting this approach in other courses related to ours, and encouraging teachers to embrace similar practice regardless of the field of education.