Analysis and Optimization for Passive One-way Delay Measurement Tax in Container Networks

Container networks have become crucial to the overall performance and health of network systems due to the increasing adoption of container technologies. Passive per-packet and per-hop one-way delay (p4 h-OWD) measurement is essential for prompt and accurate detection and localization of network issues in container environments. Existing measurement methods for generic virtual networks require intrusive packet modification to uniquely match packets during p4 h-OWD measurements. However, the overhead and ensuing performance impact induced by high-frequency packet operations during the measurement process have been largely overlooked in the context of lightweight and weak-isolation container networks. This paper demonstrates that even state-of-the-art technologies leveraging the efficient extended Berkeley Packet Filter (eBPF) can introduce significant overhead, impacting both the networking and computing performance of container networks during p4 h-OWD measurements. To effectively reduce the p4 h-OWD measurement tax, which encompasses the measurement overhead and its consequent impact, we propose a non-intrusive method to obtain unique packet identifiers in container networks, thereby avoiding the significant operational overhead associated with intrusive packet matching. Building on this foundation, we present CNDMeas, an efficient eBPF -based technology designed for p4 h-OWD mea-surement within container networks. Evaluation results show that CNDMeas effectively limits the increase in CPU time dedicated to handling software interrupts to within 3 % and reduces the impact on both networking performance by up to 71 % in terms of delay, and computing performance during the measurement process compared to state-of-the-art technologies.

I. Introduction

Over the years, containers are extensively employed in multiple domains including serverless computing [1], Network Function Virtualization (NFV) [2], and digital twin (DT) simulation [3], thanks to their attributes of being lightweight, flexible, and offering near-native performance [4], [5]. Con-tainer networks are instrumental in establishing connections between containers and assembling service functions into function chains, representing a kind of new-emerging but important form of the network infrastructure. To ensure the proper functioning of container networks, it is critical to conduct measurements in them, as such measurements enable informed decision-making and effective management by pro-viding valuable insights into the network status [6].