## 2 Related Work  
This section analyzes related contributions, highlighting the main novelties of the current contribution in relation to the available literature. Machine learning (ML) can be used to exploit the data originating from mobile sensors [10], but also to control the networks underlying the computational edge systems. [PERSON] et al. [11] present a comprehensive literature analysis covering ML in the next generation of mobile networks to guarantee the quality of end-to-end applications. More specifically, they classified the available ML-related literature in the next four parts, network access in the media access control (MAC) layer, end-to-end network routing, end-to-end network congestion control and the end-to-end adaptive streaming control. Finally, they discuss some open research issues and relevant guidelines for further investigation into upcoming mobile networking scenarios. In the networking area, the developed MAB algorithms have focused on learning about the optimal operation policies for enhancing intrusion detection [12; 13] or routing via a hybrid mesh network [14] to optimize the throughput. Differently, our MAB proposal aims to achieve end-to-end path delay minimization, meaning a more robust network connectivity despite the unexpected congestion situations that occur in the network devices. Considering a SDN scenario with multiple controllers, [PERSON] et al. [15] investigate how learning can boost both the switch-controller association and the load of the control channel. [PERSON] et al. [16] and [PERSON] et al. [17] use RL to learn the optimum routing path, but these force the SDN controller within each system time slot to actively measure the delay in every network link, which excessively overloads the system, as evidenced in the results of Section 4.3. Alternatively, we propose a more scalable MAB-based approach, which measures a single end-to-end path delay in each time slot. We investigate a centralized MAB agent that acts as a topmost system manager to learn which end-to-end path traverses the set of less-congested network devices. Then, the SDN controller configures this optimum end-to-end path in the generic network by transferring proper flow rules to the network devices involved in that path, aiming to detect and avoid abnormal congestion situations in network devices and preserving the network quality offered to the system's topmost-layer applications or services. A multitude of MAB algorithms have been crafted over the years to handle different types of reward distribution [7; 8]. A family of algorithms is dedicated to handling stochastic reward distributions, with the well-known UCB algorithm [18] being a remarkable representative. Other algorithms, such as Exp3 [19], have been developed to tackle the more general case, in which rewards can be specified by an adversary, which may not behave in a stochastic way. Although more general, adversarial algorithms tend to underperform compared to stochastic algorithms when facing stochastic rewards. The success of UCB has triggered the proposal of several extensions, such as KL-UCB [20], which is known to outperform the standard UCB algorithm. Despite their abilities, most MAB algorithms for stochastic domains are restricted to handle stationarity reward distributions, an assumption that hardly holds in dynamic networking scenarios. A well-known extension of the UCB algorithm for non-stationary reward distributions is known as Discounted UCB [21; 22], which considers recent rewards to be more relevant than older ones. We exploit the strength of both KL-UCB and Discounted UCB to proposed an hybrid solution. However, it is often necessary to include additional mechanisms to handle sudden and abrupt distribution changes. Some algorithms cope with this problem by detecting changes in reward distributions and resetting the algorithm when those events occur, such as M-UCB [23]. We build on this intuition and tune it to produce a more robust solution for our proposed MAB agent.