## 2 Related Work
### Overview of Existing Solutions
The paper [21] introduces a dynamic congestion management brokerage system that aims to meet the QoS requirements of the IoT, as outlined in the service-level agreement (SLA). This system effectively handles a large influx of Cloud requests that originate from the Fog broker layer while also presenting a forwarding policy that allows the Cloud service broker to selectively forward high-priority requests to appropriate Cloud resources from both Fog brokers and Cloud users. The underlying concept behind this system is based on the weighted fair queuing (WFQ) Cisco queuing mechanism, which simplifies the management and control of potential congestion issues on the Cloud service broker side. Considering the mobility of nodes and the diverse nature of computing devices, the need for an efficient load-balancing algorithm becomes evident. The work [22] introduces a load-balancing approach specifically designed to minimize service latency. The primary goal is to distribute the latency uniformly among all nodes, ensuring a fully decentralized process. This approach ensures that no user encounters a lower QoS than others. Optimizing the placement of tasks on Edge devices to achieve optimal performance is a significant challenge in Fog-assisted architecture. This process of mapping tasks to services is known as the SPP. In the recent work [23], a heuristic algorithm named \"Clustering of Fog Devices and Requirement-Sensitive Service First\" (SCATTER) was introduced to effectively solve the SPP. To address the NP-hard optimization problem of application placement strategies in a hierarchical Fog-Cloud environment, specifically based on directed acyclic graphs (DAGs), a heuristic approach has been commonly used. This approach generates sub-optimal solutions by employing a non-preemptive placement policy. Moreover, the current practice of merging multiple DAG-based applications into one is ineffective in minimizing the overall makespan. This is due to the lack of fairness considerations among multiple DAG-based applications. In response to this limitation, a novel application placement strategy based on dynamic scheduling is proposed in the work [24]. This strategy leverages the schedule gaps in virtual machines across different layers, aiming to minimize the makespan while ensuring adherence to deadlines. A major challenge in Fog networks is how to distribute computational tasks efficiently between Fog and Cloud nodes, which have varying computing capacities at different distances from end users. To address this challenge, in the work [25], a universal non-convex Mixed-Integer Nonlinear Programming (MINLP) problem was formulated. The objective of this problem is to minimize both task transmission and processing-related energy while considering delay constraints. The formulated problem is transformed using Successive Convex Approximation (SCA) and decomposed through primal and dual decomposition techniques. To tackle this optimization problem, two practical algorithms have been proposed: Energy-Efficient Resource Allocation (EEFFRA) and Low-Complexity (LC)-EEFFRA. The paper [26] proposes strategies to integrate FC into the Cloud-based Industrial Internet of Things (IIoT) and establish a Cloud-Fog-integrated IIoT (CF-IIoT) network seamlessly. The primary goal is to achieve ultra-low service response latency, which requires incorporating distributed computing within the CF-IIoT network. To optimize load balancing in this distributed Cloud-Fog environment, the paper proposes a Real-Coded