## 3 Related Work  
Distributing jobs in dew environments _optimally_ is a challenging combinatorial problem [6; 23]. Many factors must be taken into account to assign jobs to devices correctly. Those factors range from a device's CPU speed to a device's availability within a time window, from how often jobs arrive at the scheduler to how urgent jobs must be completed. Many previous works have proposed heuristic methods to deal with this complex problem. A heuristic method is a practical approach to obtaining approximated yet satisfactory solutions to problems where finding the optimal solution is computationally intractable, in this case, the job assignment problem. This paper seeks human-designed heuristics that consider features from the SCE and are aware of network changes through time. Also, we explore an alternative approach, which consists of _learning_ a policy to distribute jobs using RL. To provide a coherent view of the related work, we divided this discussion into two parts: We first discuss existing heuristic methods to distribute jobs in an SCE and then review previous works at the intersection of RL with cloud and edge computing. In the context of SCEs, different algorithms have been proposed to optimize the system's utility and job execution time, using the mobile devices' remaining battery as a formal constraint of the resource allocation problem formulation [24; 25; 26; 27; 28]. These algorithms, however, assume complete and accurate information regarding the energy spent and the execution time for every candidate node, making it challenging to apply in real-life scenarios. In Refs. [29; 30], they addressed the previous limitation by proposing algorithms that do not rely on complete information. This approach seeks to exploit the nodes' proximity and cost-effectiveness of node transferring capabilities. However, their studies focused on analyzing the effect of nodes' mobility rather than balancing the load to efficiently utilize the battery and processing power of the available resources. Refs. [6; 22] presented other types of heuristics, which distribute jobs by considering the mobile device's battery level and computing scores obtained from benchmarks. These methods outperformed traditional scheduling algorithms (such as round robin) but have the limitation of only considering battery-dependent devices. This problem was recently addressed by Ref. [7]. In Ref. [7], the authors studied hybrid SCEs, which combine both battery-dependent and non-battery-dependent devices, and proposed heuristic methods that consider the device's battery level, computing score, and current workload to distribute jobs in dew environments. In contrast to those works, where the primary assumption was that the network topology remained static, meaning that the structure and connections within the network did not change over time, this work takes a different approach. Our research recognizes the limitations of static topologies, particularly in real-world scenarios where network environments are often dynamic and subject to change. By \"dynamic environments\", we specifically refer to situations where mobile devices can enter or exit the boundary of an SCE over time, leading to fluctuations in the network's composition and connection throughput. To address these challenges, we propose new heuristic algorithms specifically designed to adapt to network dynamic conditions. To achieve this, we improved the simulation support offered by EdgeDewSim [7], enhancing its capability to support the development of new connection-aware job distribution as the topology evolves, configure nodes' mobility in an easy-to-reproduce manner, and represent network changes as an effect of nodes' mobility. By incorporating these advancements, our approach aims to provide a more robust and flexible solution for job distribution in dynamic network environments. RL Methods in Edge and Dew Computing  
In cloud computing, task scheduling focuses on deciding which resources (servers) should process an incoming task.