KubeDSM: A Kubernetes-based Dynamic Scheduling and Migration Framework for Cloud-Assisted Edge Clusters

2025-01-13

AI Summary

To address inefficient task scheduling and resource fragmentation caused by resource constraints and unstable connectivity in edge computing, this paper proposes a QoS-aware cloud-edge collaborative resource management framework. The framework introduces a novel batch-processing scheduling algorithm and a three-tier dynamic migration mechanism—comprising intra-edge, edge-to-cloud, and cloud-to-edge migrations—integrated with lightweight container live migration, QoS-aware scheduling policies, and an edge-state-adaptive decision model, implemented atop an extended Kubernetes platform. Experimental results demonstrate an average 23.6% improvement in edge Pod deployment rate, a 41.2% reduction in deployment instability (measured by standard deviation), a 57.3% decrease in resource fragmentation, and 100% QoS compliance. This work is the first to deeply integrate batch scheduling with multi-level dynamic migration, significantly enhancing both service quality for latency-sensitive applications and resource utilization efficiency in edge environments.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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 edge nodes' limited capacity and unreliable connectivity. This paper introduces KubeDSM, a Kubernetes-based dynamic scheduling and migration framework tailored for cloud-assisted edge environments. KubeDSM addresses the challenges of 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 the impact of migration strategies and Quality of Service (QoS) configurations on the edge ratios achieved by KubeDSM. The findings reveal that enabling migrations significantly enhances the edge ratio by reducing fragmentation. Additionally, KubeDSM's adaptability in respecting QoS requirements while maximizing overall edge ratios is confirmed through different QoS scenarios.

Problem

• Edge Computing
• Resource Management
• Optimization

Innovation

• KubeDSM
• Edge-Cloud Synergy
• Automated Resource Scheduling