Dynamic multi-hop networks face fast link-level dynamics including topological changes, stochastic failures, time-varying residual capacities, etc., which can cause transmission timeout, persistent congestion and frequent route flapping, undermining network stability. Aiming at jointly optimizing route qualities including success rate, path length, congestion, and path-rerouting stability, etc., sophisticated planning algorithm is needed to flexibly adapt to the time-varying patterns of the whole network. In this paper, a novel interactive two-level reinforcement learning (RL) policy is proposed and decouples candidate path generation from online selection. The upper-level learns to propose a set of stable candidate paths for arbitrary source–destination data routing demands, guided by long-term per-path instability estimations aggregated from past collections. The lower-level performs online selection from the candidates, by employing a temporal graph attention encoder to capture state dynamics, and applying selection policy constrained by success rate, stability, low-congestion, tail-switching avoidance, etc. The two-level policy is interactively learned by bridging them with a flow of stability features from the lower- to upper-level. Experiments on public network datasets demonstrate that the proposed method consistently achieves high-quality routing, with the reduced congestion and path switching while maintaining high success rates and near-shortest paths.
Microservice applications rely heavily on DNS for internal service discovery in ephemeral environments. Unlike traditional public DNS, cloud-native DNS operates as a core infrastructure component with strict requirements for correctness, low latency, and adaptation to frequent record changes. There is a gap between existing DNS research focused mainly on performance in public environments and the unexplored resilience of internal, cloud-native DNS systems, especially those supporting private zones in containerized environments.This paper addresses this gap by examining the role of private DNS in cloud-native systems and applying resilience testing techniques to systematically evaluate its behavior. First, this work analyzes public cloud incident post-mortems and finds that DNS is rarely the primary root cause of outages, and incidents disproportionately affect private DNS over public DNS. Secondly, we design and evaluate a resilience testing strategy for a multi-cluster cloud-native DNS system published by Swisscom AG, the biggest telecommunications provider in Switzerland. The proposed strategy uses chaos injection and DNS traffic generation to evaluate availability, latency, accuracy, and recovery behavior under different disruption scenarios. We find that the system operates within acceptable limits in most scenarios and resilience testing can reliably capture the impact of caching, resolution inconsistencies, and failure-handling weaknesses across test cases.
Compact probabilistic data structures (CPDS) such as Bloom filters, Invertible Bloom Lookup Tables (IBLTs), and sketches are widely used in network telemetry systems to collect traffic statistics directly in resource constrained, high-speed programmable data planes (e.g., smartNICs and Intel Tofino switches). While CPDS enable scalable and efficient monitoring, they also increase the attack surface area. Adversaries can launch pollution attacks that corrupt stored statistics, leading to incorrect network management decisions.In this paper, we present ListGuard, a lightweight system that detects and recovers flow statistics in IBLT-based telemetry primitives affected by pollution attacks. Our key idea is to selectively sample additional flows and use them to resume the stalled decoding process without modifying the IBLT structure. Using realistic traces, we demonstrate attacks and show that ListGuard restores affected statistics with low memory overhead.
Fine-grained network traffic identification is crucial for service quality assurance, security monitoring, and traffic scheduling. However, the construction of fine-grained datasets currently relies heavily on manual efforts, suffering from prohibitive costs, low efficiency, and rapid obsolescence due to frequent application updates. To overcome these scalability bottlenecks, we propose NanoTG, an automated framework leveraging a Plan-Execute-Reflect Agent architecture for reliable mobile traffic generation. NanoTG autonomously explores target apps to identify fine-grained app-activities and achieves semanticlevel traffic attribution by aligning network flows with granular execution logs. We evaluated NanoTG on 10 popular applications. During exploration phase, NanoTG uncovered 21.6% more unique IP addresses and 22.7% more SNIs than baseline tools. This demonstrates that NanoTG can identify more fine-grained app-activities. For app-activity quality, NanoTG generated 113 high-quality tasks and achieved a 62% improvement in network relevance score over baseline methods. We further discuss the effectiveness of NanoTG-generated traffic, and preliminarily validate its realism and downstream task utility.
