# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 7 Literature Review of Problem Solution Techniques
### DRL-Based Scheduling Algorithms  
[PERSON] et al. [16] present a solution to the task migration problem using deep reinforcement learning. The problem involves determining the optimal time and location to move services using the Markov decision process, without prior knowledge of the user's mobility pattern. The authors aimed to reduce migration time. [PERSON] et al. [3] proposed a deep reinforcement learning model to enable the scheduler to adjust to changing environments through asynchronous updates quickly. This novel idea achieves lower energy consumption than existing reinforcement learning models, allowing the scheduling of resource-intensive tasks on more powerful machines. A weakness of the proposed approach is the need for continuous profiling of CPU, RAM, disk, and bandwidth demand of new tasks in real edge-cloud environments. This could result in additional computational overhead. [PERSON] et al. [19] focus on Mobile Edge Computing, and propose a task scheduling algorithm that relies on deep reinforcement learning and incorporates a resource allocation strategy that involves task migration among edge and central cloud servers. With an appropriate scheduling strategy, they can achieve a shorter response time. However, one drawback of their solution is a reallocation policy, which is necessary since time-sensitive application tasks may arrive at an edge server when resources are insufficient, requiring immediate execution. [PERSON] et al. [35] model the scheduling problem as an MDP and use deep reinforcement learning techniques to optimize the service placement decision to satisfy QoS requirements. The simulations demonstrate that the proposed DRL-based approach is more effective than heuristic-based solutions for time-sensitive service placement problems. Their main goals are to reduce the number of unfulfilled requests, the number of fog nodes chosen for placement, the number of high-priority services that were not allocated, and the distance of the chosen fog nodes from the users requesting services. [PERSON] et al. [36] propose a novel approach for addressing the application placement problem in heterogeneous fog computing environments using a distributed deep reinforcement learning technique. [PERSON] et al. [43] describe a study that uses Deep Reinforcement Learning (DRL) techniques applied to Vehicle-to-Vehicle communication to optimize the utility of scheduling tasks and minimize delay in a fog computing environment that supports vehicle heterogeneity. [PERSON] et al. [41] aims to decrease energy consumption and latency in heterogeneous fog environments by proposing a binary offloading strategy based on deep learning that leverages several parallel deep neural networks (DNNs) to facilitate the scheduling decision-making process. After the decision outputs are stored in a relay memory, they are used to train and test all neural networks. Notably, in real-time scheduling, their proposal may require the continuous allocation of resources. Additionally, in a dynamic fog environment where user mobility is critical, their strategy may not be a viable option for data offloading patterns. [PERSON] et al. [5] formulated a two-step decision problem in cloud-edge based group-distributed manufacturing systems. The scope of the traditional task scheduling problem has been expanded in this paper to include deadline constraints. The authors approach the task placement problem in two steps: first, they prioritize tasks, and second, they schedule the tasks based on their priorities. The proposed approach based on deep reinforcement learning aims to minimize the total delay time of task scheduling by employing two agents: one for prioritizing tasks and another for selecting the suitable node. One challenge that remains to be addressed is the reduction of model training time. [PERSON] et al. [15] focused on addressing task scheduling to reduce latency and computing costs while also meeting resource and deadline constraints.