Papers for
content delivery network operators
Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.
Machine learning cuts font loading time for large web typography
MLFont: Machine Learning-Driven Font Subsetting and Range-Compact Unicode-Range Slicing for Low-Latency Web Typography
Abstract: CSS3 @font-face rules with unicode-range descriptors partition web fonts into downloadable slices. While Latin fonts need ~20 KB in WOFF2, Pan-European (LGC) and East Asian (CJK) fonts contain 2,297-30,759 codepoints (158 KB-5.9 MB in WOFF2), where heuristic partitioning causes unused-glyph downloads, high HTTP RTT, CSS bloat, or missing-glyph fallbacks. We present MLFont, a machine learning and optimization framework that learns font slicing strategies from corpora via three stages: (1) a Zipfian High-Frequency Tier (most) isolating top-frequency codepoints; (2) a Latent Co-Occurrence Topic Tier using Online Variational Bayes LDA (Online LDA) or a Deep Neural Network Topic Model (DNNTM) with Shannon Entropy Difference Loss and Sinkhorn-Knopp normalization; and (3) a Memetic Range-Compact Genetic Algorithm (GA) with Potts-Viterbi dynamic programming and island bridging that minimizes topic discrepancy and CSS fragmentation. Across five Google Noto fonts (LGC, KR, JP, TC, SC), MLFont cuts 3G per-glyph latency by 56.7%-75.3% over single-file delivery and 46.1%-66.3% over equal-bin baselines on CJK with 100% coverage, reduces CSS intervals by 17.9%-41.7%, and achieves 1.56x-6.09x cold-cache speedups (65.4%-84.9% CJK and 44.7% LGC bandwidth savings) across 20 websites in simulator and Headless Chrome benchmarks (45/45 exact slice-set parity). Code: https://github.com/wq2012/mlfont Models: https://huggingface.co/collections/wq2012/mlfont
Internet host location improves using reactive timing from unsolicited scans
Reactive Constraint-Based Geolocation of Internet Hosts
Abstract: Active IP geolocation techniques rely on the responsiveness of Internet hosts, while passive techniques depend on data sources that are unevenly adopted and prone to staleness and error. In this work, we invert the active IP geolocation problem by listening at a geographically distributed set of vantage points for unsolicited Internet scans. By reactively completing connections with these scanners, we obtain round-trip time (RTT) measurements from hosts that may otherwise be unresponsive and use those measurements for geolocation. Over the course of 20 days in August 2026, we recorded 46.8 million scans from 289,746 distinct scanners, with a reactive RTT for each scan. We show that reactive RTTs align with those obtained by sending active probes, with more than three-quarters of 990,204 measurement pairs differing by at most 10 ms. Over half of the scanners that yielded a reactive RTT did not answer our active probes, highlighting the value of our approach. We demonstrate the feasibility of multilateration using purely reactive RTTs for 56,112 distinct IP addresses. Finally, using speed-of-light constraints, we refute commercial geolocation claims for 6,852 addresses.
Edge servers balance load and cut wait times for AI language requests
End-to-End Latency-Minimizing and Load-Balanced Request Scheduling for Edge LLM Inference in Agentic AI Services
Abstract: Large language model (LLM)-powered agentic AI services increasingly demand low-latency inference, motivating the deployment of LLMs across distributed edge servers. However, heterogeneous communication and computing capabilities, together with dynamically evolving inference states, make the edge server selection for each incoming request time-varying and tightly coupled across slots. In this paper, we investigate an online request scheduling framework for edge LLM inference that jointly minimizes long-term average end-to-end latency and regulates workload distribution across heterogeneous edge servers. Two main challenges arise in this context. First, conventional latency models cannot accurately capture the fine-grained dynamics of multi-stage LLM execution. Second, the latency consequence of a scheduling decision is observed only after request completion, making immediate decision evaluation difficult. To address these challenges, we develop a cross-slot inference model that captures transmission, prefill, iteration-level decoding, and key-value (KV) cache evolution for each diverse request, and characterize server workload through a KV cache memory-time consumption metric. We propose the LYREO approach that transforms the long-term load-balancing constraint via Lyapunov optimization and employs reward redistribution with sequencebased return prediction to convert delayed outcomes into timely learning signals for earlier decisions. Simulations under various configurations demonstrate that LYREO consistently achieves lower latency and more balanced load distribution than representative learning-based and heuristic baseline schemes.