# Global Resource Scheduling for Distributed Edge Computing
## 2 Related Work
[21] proposed a distributed intelligent resource scheduling (DIRS) framework to minimize the sum of task delay and energy consumption. The scheduling problem is formulated as a mixed-integer nonlinear programming. [PERSON] et al. [22] proposed an intelligent metaheuristic optimization model to address the problem of low-quality service and achieved the optimization of time and precision in task unloading through PSO on GPU. [PERSON] et al. [23] introduced a coevolutionary algorithm with function-independent decomposition (FID). For large-scale problems, the binary code of the original model is converted to integer code to reduce dimensionality, and a new FID is designed to decompose the problem effectively. Routing scheduling research finds wide-ranging applications in engineering, such as urban vehicle scheduling and robot path planning in intelligent factories [24]. [PERSON] et al. [25] introduced a scheduling model for urban bus charging, considering a heterogeneous fleet of electric buses with different battery capacities. The optimized scheduling method aims to minimize the number of buses charged simultaneously, thereby reducing the peak load. [PERSON] et al. [26] proposed a time scheduling method for fixed line-oriented urban bus departures. They proved the NP-hardness of the scheduling problem, simplified it as a variant of a k-clustering problem, and provided a practical application of dynamic programming. [PERSON] et al. [27] proposed parallelizing particle swarm optimization based on a graphics processing unit (GPU) to balance cluster sizes and identify the shortest path. (3) Network scheduling  
[PERSON] et al. [28] introduced a context-aware offloading problem for multiple users based on Bayesian learning automata (BLA). BLA is employed to learn all states and actions within the network, subsequently enhancing the offloading algorithm. Contextual information is gathered through autonomous management in a monitoring-analysis-planning-execution loop throughout all offloading processes. [PERSON] et al. [29] studied current 4G-5G interworking solutions, analyzed the practical application feasibility and challenges of various interworking solutions, discussed the possibility of spectrum sharing between 4G and 5G wireless networks, and explored the migration path to 5G independent networks. [PERSON] et al. [30] proposed a dynamic resource allocation algorithm for virtual network functions (VNFs). The algorithm considers the latency requirements of different applications in the service function chain, allowing delay-sensitive applications to reduce end-to-end network latency through edge resources. [PERSON] et al. [31] proposed an adaptive distributed scheduling algorithm on resource-constrained edge clusters of the Internet of Things. The algorithm utilizes a convolutional neural network (CNN) to optimize the distributed resource scheduling problem, using minimum memory occupation as the objective function. This method adopts a new work scheduling process to improve data reuse and reduce overall execution delay. In summary, current research spans various fields, encompassing intelligent grids [32], smart mining [33], smart factories [34], and more. However, a predominant trend in current research is the concentration on a singular optimization goal, such as minimizing delay or finding the shortest path. Different engineering application scenarios present distinct challenges, and their solution methods vary accordingly. Therefore, the design of optimization methods should align with specific requirements dictated by the nature of each application.