# Global Resource Scheduling for Distributed Edge Computing
## 2 Related Work  
Currently, the research focus in distributed resource scheduling is broad and encompasses various domains. These include network data flow scheduling [7], cloud computing [8], language processing [9], energy scheduling [10], blockchain [11], vehicular edge computing [12], and other fields [13]. The classification of related research work is summarized in Table 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Category** & **Description** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{Resource scheduling} & Resource scheduling is an important research area in edge computing, involving how to effectively manage and allocate computational resources in distributed systems to achieve performance optimization, load balancing, and maximized resource utilization. \\  & & Research in the context of edge computing with a mesh topology focuses on determining the optimal path for migrating edge computing tasks within a distributed resource environment. Studies in this area primarily emphasize performance metrics such as low latency and high reliability. \\  & & Network scheduling in edge computing is a critical research area aimed at optimizing resource management and task scheduling within the edge computing environment to meet the growing computational demands and enhance user experience. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of related work. [PERSON] et al. [14] transformed the multi-virtual machine scheduling problem into a structured combinatorial optimization problem, approaching it as a reinforcement learning problem. They introduced a reinforcement learning algorithm with an incremental reward scheme and a scenario-guided sampling strategy. [PERSON] et al. [15] proposed an optimization model for computing resource allocation that utilizes cooperative evolutionary computation. This model addresses the joint optimization problem of queue-based computation offloading and adaptive computing resource allocation. The method ensures task computation latency for all mobile nodes (MNs) within a time block, optimizes the total accessibility rates of MNs, and reduces energy consumption for both unmanned aerial vehicles (UAVs) and MNs, all while meeting the constraints of task computation. In [16], regional optimization of the irrigation plan is achieved by combining a multiobjective algorithm with the exponential efficiency coefficient method. [PERSON]. [17] proposes several optimization attributes based on high-end equipment development characteristics, and designs an effective heuristic algorithm. Given that the resource-constrained project scheduling problem is NP-Hard, three problem neighborhoods are constructed, and a variable neighborhood search algorithm is developed for problem resolution. [PERSON] et al. [18] addressed issues in the employee evaluation mechanism of enterprises. They improved the current evaluation model based on neural networks and analytic hierarchy process, scientifically evaluating the matching degree of employees' abilities and job requirements. This is achieved by learning the radial basis function (RBF) neural network model for optimal employee-job matching and migration. [PERSON] et al. [19] studied the scheduling method of dynamic service resources in a cloud manufacturing environment. This method aims at time, cost, quality, and capacity and establishes an optimal scheduling model for emotional service resources. ACO is improved, and GA functions are used to optimize the objective function. A GA-based optimization fusion algorithm is proposed to solve the model. [PERSON] et al. [20] proposed a distributed particle swarm optimization (PSO) method extended to large-scale cloud workflow scheduling. This method divides the entire population into multiple groups, employing the master-slave multigroup distributed model to co-evolve these groups into a distributed PSO (DPSO) to enhance algorithm diversity. The DPSO adopts a dynamic group learning (DGL) strategy to balance diversity and convergence. [PERSON] et al.