應用程式在邊緣運算系統中的協同延遲提取方法

Abstract

隨著第五代行動通訊網路(5G)對低延遲應用的需求日益增長，多接取邊緣運算（MEC）已成為一項具有前景的解決方案。MEC通過將資源從雲端移至網路邊緣，能夠根據即時的網路資訊實現立即回應和動態資源分配。容器由於其輕量和易於部署的特性，被視為MEC服務部署中一向有價值的虛擬化技術。然而，容器冗長的啟動時間可能導致回應時間過長，特別是在擁有較長傳播延遲、頻繁部署和遷移特性的邊緣運算場景之中。在這篇論文，我們研究了MEC中的映像檔快取、容器分配和倉庫選擇問題，其中利用本地倉庫快取應用程式映像檔，並由工作節點為使用者提供服務。為了解決上述問題，我們提出了一種映像檔快取策略，採用部分快取方式，使得本地倉庫可以快取應用程式映像檔的基本或完整功能版本。此外，我們使用基於邊緣的協同延遲提取演算法來解決容器分配和倉庫選擇的問題。為了評估我們所提出的演算法的表現，我們在測試平台上使用真實的應用程式使用數據和熱門映像檔進行實驗。實驗結果顯示，我們的演算法在平均使用者回應時間和快取命中率方面皆優於傳統的貪婪演算法。

With the growing demand for latency-sensitive applications in 5G networks, Multi-access Edge Computing (MEC) has emerged as a promising solution. MEC enables instant response and dynamic resource allocation based on real-time network information by moving resources from the cloud to the network edge. Containers, known for their lightweight nature and ease of deployment, have been recognized as a valuable virtualization technology for MEC service deployment. However, the prolonged startup time of containers can lead to long response time, particularly in edge computing scenarios characterized by long propagation time, frequent deployment, and migration. In this paper, we investigate on the image caching, container assignment, and registry selection problem in MEC where local registries are utilized to cache application images, while worker nodes provide services to users. To address the problem, we propose an image caching strategy that employs partial caching, allowing local registries to cache either the least functional or complete version of application images. In addition, a container assignment and registry selection problem is solved by using an edge-based collaborative lazy pulling algorithm. To evaluate the performance of our proposed algorithms, we conduct experiments with real-world app usage data and popular images in a testbed environment. The experimental results demonstrate that our algorithms outperform traditional greedy algorithms in terms of average user response time and cache hit rate.