# Load-Balanced Dynamic SFC Migration Based on Resource Demand Prediction
## 2 Related Work  
Within the framework of Network Function Virtualization, researchers from around the world have conducted many studies on the dynamic migration of Service Function Chains and have achieved significant results. One major challenge in dynamic migration is how to allocate resources for SFCs as their resource demands change and the duration that SFCs remain in the network is uncertain. Recent research typically takes one or more factors related to migration costs as optimization objectives and proposes corresponding migration decision algorithms. For instance, the authors of [4] proposed a migration algorithm based on Deep Q-Networks (DQNs). Their goal was to minimize end-to-end latency and address service interruptions caused by overloaded nodes, link failures, and VNF instance failures. The authors of [5] introduced a framework that uses Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) to determine the optimal migration paths for VNFs. Their aim was to minimize migration time and cost. This solution analyzes network conditions, workload patterns, and resource availability to achieve dynamic and efficient VNF relocation, which significantly improves network performance and reliability. Additionally, the authors of [6] presented a scalable cluster-based VNF migration algorithm. This algorithm is designed to minimize the total embedding cost while meeting the latency requirements between VNFs. It performs better than existing methods in terms of embedding costs and has a much shorter execution time compared to brute-force search methods. However, the methods mentioned above only trigger migrations after changes occur in network traffic or after network node or link failures. These are forms of reactive migration and cannot proactively handle sudden changes in resource demand. Currently, to solve reactive migration, researchers are trying to predict resource demands in advance. In [7], the authors proposed a CAT-LSTM model to predict the resource demands of VNFs using SFC data. They improved accuracy by using aspect embedding and attention mechanisms. Similarly, the authors of [8] introduced a Graph Neural Network (GNN) model based on the graph features. This model accurately predicts VNF resource demands by identifying multidimensional dependencies within SFC graph structures. Although studies in [7; 8] have made progress in prediction, they do not deeply explore how to effectively use these predictions to optimize resource allocation. Moreover, existing research primarily focuses on optimizing objectives such as reducing latency and migration costs. However, merely minimizing energy consumption or overhead may lead to uneven utilization of network resources, and future dynamic traffic changes could result in frequent VNF migrations. Additionally, in edge computing networks where resources are relatively scarce and highly dynamic, greater emphasis should be placed on the distribution and utilization efficiency of resources to ensure network load balancing. Current studies have demonstrated the advantages of ensuring network load balancing in enhancing network performance. For example, the authors of [9] proposed a load-balanced Virtual Network Embedding algorithm based on deep reinforcement learning for regional satellite networks. The algorithm divides the satellite network into multiple mapping regions and deploys Virtual Network Requests only in the region with the lowest load. It simultaneously considers topology dynamics and resource loads, effectively improving acceptance rates while also enhancing resource utilization. Similarly, the authors of [10] introduced a task scheduling algorithm considering load balancing issues in cloud computing environments. The proposed BCSV algorithm achieves better results in terms of completion time, load balancing, and resource utilization compared to previous algorithms, even in heterogeneous environments. This improvement enhances the overall performance of cloud computing networks.