多叢集邊緣運算系統中微服務的資源分配與容器擴展

Abstract

隨著 6G 時代的來臨與分散式系統的演進，邊緣運算已成為部署低延遲、高資源效率應用的重要架構。特別是微服務架構，由具模組化且鬆耦合的元件所組成，在建構具擴展性與可維護性的網路邊緣應用上獲得廣泛關注。然而，於異質且地理分散的多叢集邊緣運算（MCEC）環境中部署基於微服務的應用，仍面臨許多關鍵挑戰，特別是在實現高效且具擴展性的資源管理方面。儘管現有研究已探討邊緣運算中的資源分配策略，許多方法卻忽略了承載微服務執行的服務容器之運算效率。為彌補此不足，我們提出RACCOON，一種專為MCEC 環境中的微服務部署所設計的請求卸載級聯式資源分配演算法。RACCOON 旨在最小化使用者感知之服務延遲，並同時優化整體資源使用率。作為輔助機制，我們進一步提出RASCAL，一種基於強化學習（Reinforcement Learning, RL）的容器擴展機制，能在容器層級動態調整資源配置，以提升系統效能。實驗結果顯示，我們的方法在使用者平均回應時間與運算負載方面，皆持續優於現有先進基準方案，展現其在實際邊緣環境中應用微服務的實用效能。

With the advent of the 6G era and the evolution of distributed systems, edge computing has become a pivotal architecture for deploying latency-sensitive, resource-efficient applications. In particular, the microservice architecture, characterized by modular and loosely coupled components, has gained significant traction for building scalable and maintainable applications at the network edge. However, deploying microservice-based applications in heterogeneous and geographically distributed Multi-Cluster Edge Computing (MCEC) environments presents critical challenges, especially in achieving efficient and scalable resource management. Although existing research has explored resource allocation strategies in edge computing, many approaches neglect the computational efficiency of the service containers that underpin microservice execution. To address this gap, we propose RACCOON, a request-offloading cascaded resource allocation algorithm tailored for microservice-oriented deployments in MCEC settings. RACCOON aims to minimize user-perceived service latency while optimizing overall resource utilization. Complementing this, we introduce RASCAL, a reinforcement learning (RL)-based container scaling mechanism that dynamically adjusts resource provisioning at the container level to further enhance system performance. Experimental evaluation shows that our approach consistently outperforms state-of-the-art baselines in terms of average user response time and computational overhead, demonstrating its practical effectiveness for microservice deployments in real-world edge environments.