Containers are becoming a popular way of running applications in edge computing. Before running the application, the edge node must download the application’s container image consisting of multiple layers. However, given the limited bandwidth in edge computing, the container startup latency due to long image download time seriously affects the real-time performance. In this article, we jointly determine the container assignment and the layer download sequence to reduce the total startup latency. We formulate the Container Assignment and Layer Sequencing (CALS) problem and prove its NP-hardness. A Layer-Aware Scheduling Algorithm (LASA) is proposed, fully considering layer sharing among images. First, layers shared by the same set of images are grouped to reduce CALS’s problem scale without affecting the optimal result. Second, considering both layer sharing and existing layer size on edge nodes, a layer-aware algorithm is designed to assign containers to appropriate edge nodes. Finally, to determine the layer download sequence on each edge node, an approximation algorithm is proposed. We further analyze the approximation ratio of LASA in the case of identical edge nodes with sufficient capacity. Extensive experiments based on real-world data show the effectiveness of LASA, which reduces the total startup latency by 40% to 60%.

中文翻译：

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边缘计算中的高效容器分配和层排序


容器正在成为边缘计算中运行应用程序的流行方式。在运行应用程序之前，边缘节点必须下载应用程序的由多层组成的容器镜像。然而，由于边缘计算的带宽有限，镜像下载时间过长导致的容器启动延迟严重影响实时性能。在本文中，我们共同确定容器分配和层下载顺序，以减少总启动延迟。我们制定了容器分配和层排序（CALS）问题并证明了其 NP 难度。提出了一种分层感知调度算法（LASA），充分考虑图像之间的层共享。首先，将同一组图像共享的层进行分组，以减少 CALS 的问题规模，而不影响最优结果。其次，考虑到边缘节点上的层共享和现有层大小，设计了一种层感知算法来将容器分配给适当的边缘节点。最后，为了确定每个边缘节点上的层下载顺序，提出了一种近似算法。我们进一步分析了在具有足够容量的相同边缘节点的情况下LASA的近似率。基于真实世界数据的大量实验表明了 LASA 的有效性，可将总启动延迟降低 40% 至 60%。