Online Layer-Aware Joint Request Scheduling, Container Placement, and Resource Provision in Edge Computing

• Published in: IEEE transactions on services computing 2024-11, p.1-14
• Main Authors: Li, Zhenzheng, Lou, Jiong, Tang, Zhiqing, Guo, Jianxiong, Wang, Tian, Jia, Weijia, Zhao, Wei
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Container placement; Containers; Costs; Delays; Edge computing; Image edge detection; Optimization; Processor scheduling; request scheduling; resource provision; Scheduling; Servers
• ISSN: 1939-1374; 2372-0204
• DOI: 10.1109/TSC.2024.3504237

Containers have emerged as a pivotal tool for service deployment in edge computing. Before running the container, an image composed of several layers must exist locally. Recent strategies have utilized layer-sharing in images to reduce deployment delays. However, existing research only focuses on a single aspect of container orchestration, like container placement, neglecting the joint optimization of the entire orchestration process. To fill in such gaps, this paper introduces an online strategy that considers layer-aware container orchestration, encompassing request scheduling, container placement, and resource provision. The goal is to reduce costs, adapt to evolving user demands, and adhere to system constraints. We present an online optimization problem that accounts for various real-world factors in orchestration, including container and server expenses. An online algorithm is proposed, integrating a regularization-based approach and stepwise rounding to address this optimization problem efficiently. The regularization approach separates time-dependent container placement and server wake-up costs, requiring only current information and past decisions. The stepwise rounding process generates feasible solutions that meet system constraints, reducing computational costs. Additionally, a competitive ratio proof is provided for the proposed algorithm. Extensive evaluations demonstrate that our approach achieves about 20% performance enhancement compared to baseline algorithms.