Efficient container image distribution is crucial for enabling machine learning inference at the network edge, where resource limitations and dynamic network conditions create significant challenges. In this paper, we present PeerSync, a decentralized P2P-based system designed to optimize image distribution in edge environments. PeerSync employs a popularity- and network-aware download engine that dynamically adapts to content popularity and real-time network conditions. PeerSync further integrates automated tracker election for rapid peer discovery and dynamic cache management for efficient storage utilization. We implement PeerSync with 8000+ lines of Rust code and test its performance extensively on both large-scale Docker-based emulations and physical edge devices. Experimental results show that PeerSync delivers a remarkable speed increase of 2.72$\times$, 1.79$\times$, and 1.28$\times$ compared to the Baseline solution, Dragonfly, and Kraken, respectively, while significantly reducing cross-network traffic by 90.72% under congested and varying network conditions.
翻译：高效的容器镜像分发对于在网络边缘实现机器学习推理至关重要，该场景下的资源限制和动态网络条件带来了重大挑战。本文提出PeerSync，一个去中心化的基于P2P的系统，旨在优化边缘环境中的镜像分发。PeerSync采用一个感知内容流行度和网络状况的下载引擎，能够动态适应内容流行度和实时网络条件。PeerSync进一步集成了自动追踪器选举以实现快速节点发现，以及动态缓存管理以实现高效的存储利用。我们使用超过8000行Rust代码实现了PeerSync，并在大规模基于Docker的仿真环境和物理边缘设备上对其性能进行了广泛测试。实验结果表明，在拥塞和变化的网络条件下，与基线解决方案、Dragonfly和Kraken相比，PeerSync分别实现了2.72$\times$、1.79$\times$和1.28$\times$的显著速度提升，同时将跨网络流量大幅降低了90.72%。
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