# Global Resource Scheduling for Distributed Edge Computing
## 1 Introduction  
The application scope of global resource scheduling in distributed edge computing is extensive. Existing research predominantly emphasizes optimization objectives such as maximizing resource utilization, load balancing, minimizing latency, and reducing energy consumption. However, there is a pressing need for more effective definitions of resource quality, particularly concerning tasks and data, within the constraints imposed. For instance, Figure 1a illustrates a scenario within an intelligent traffic system. In such a system, diverse edge computing nodes are distributed across different regions, monitoring locally owned traffic resources such as traffic police, rescue vehicles, and ambulances. These nodes submit real-time resource scheduling requests based on the prevailing traffic situation. In Figure 1a, both Node B and Node C require Resource 3, and there exist multiple feasible options for allocating Resource 3 among these nodes. Figure 1a exemplifies a scenario in the field of Internet of Things (IoT) edge computing. Base Station A receives data from Sensors 1, Sensor 2, and Sensor 3, and must determine the target base station to which the data from each sensor should be forwarded. For instance, the decision of where Base Station A should forward the data from Sensor 1 is crucial. From a global perspective, minimizing latency becomes the optimization objective, considering that all data are ultimately transferred. In these scenarios, the consideration of resource quality, akin to resource price, becomes imperative. The existence of different resource prices further complicates the challenge posed by the global resource scheduling problem. Several scholars have undertaken extensive research on large-scale distributed resource migration and dispatching, each based on different application scenarios [1; 2; 3]. For instance, [PERSON] et al. [4] proposed a two-layer distributed collaborative evolution (DCE) architecture with adaptive computing resources for large-scale optimization. Experimental studies demonstrated that the proposed distributed architecture exhibits high scalability and efficiency. [PERSON] et al. [5] utilized virtualization technology to apply a hybrid approach to resource management. This technology employs the K average cluster to the mapper and a micro-genetic algorithm to enhance the ramming method. The research indicated that KMGA technology effectively reduces energy consumption in data centers and maintains continuous service quality, providing a favorable compromise. Additionally, compared with particle swarm optimization (PSO) and similar hybrid technologies based on genetic algorithms, it minimizes the number of virtual machine migrations and generation time. [PERSON] et al. [6] established an intelligent manufacturing workshop resource dispatch model. They improved non-dominating sorting genetic algorithms (NSGA-II), selected evaluation functions based on sorting levels and congestion, and introduced a competitive mechanism. The generation of new groups was based on process and machine-based random mutation strategies and cross-methods. An improved elite retention strategy, a variable proportional method to determine probability, and a layered analysis method to decide the optimal solution were incorporated. The efficacy and superiority of the improved algorithm were validated through benchmark case tests and real-world production and processing challenges. However, existing research faces certain challenges, mainly characterized by the following: firstly, a relatively limited focus on global optimization; secondly, a tendency towards single optimization goals, typically centering on specific factors like delay and energy consumption; and thirdly, a lack of consideration for dynamic change parameters during the optimization process. Current research predominantly centers on proposing optimization scheduling solutions tailored to specific application scenarios, highlighting a need for more extensive exploration of a unified global scheduling model.