<p>Edge computing has become critical for enabling latency-sensitive applications, especially when paired with cloud resources to form cloud-assisted edge clusters. However, efficient resource management remains challenging due to the limited capacity and unreliable connectivity of edge nodes. This paper introduces KubeDSM, a Kubernetes-based dynamic scheduling and migration framework tailored for cloud-assisted edge environments. KubeDSM targets resource fragmentation, dynamic scheduling, and live migration while ensuring quality of service (QoS) for latency-sensitive applications. Unlike Kubernetes’ default scheduler, KubeDSM adopts batch scheduling to minimize resource fragmentation and incorporates a live-migration mechanism to optimize edge resource utilization. Specifically, KubeDSM facilitates three key operations: intra-edge migration to reduce fragmentation, edge-to-cloud migration during resource shortages, and cloud-to-edge migration when resources become available, thereby increasing the number of pods allocated to the edge. Our results demonstrate that KubeDSM consistently achieves a higher average edge ratio and a lower standard deviation in edge ratios, highlighting its ability to provide more effective and stable scheduling across different deployments. We also explore how migration strategies and different QoS configurations affect the edge ratios achieved by KubeDSM. The findings reveal that enabling migration significantly improves the edge ratio by reducing fragmentation. Additionally, experiments under different QoS scenarios confirm that KubeDSM can respect QoS requirements while maximizing overall edge ratios.</p>