Efficient scheduling is crucial for interactive Large Language Model (LLM) applications, where low request completion time directly impacts user engagement. Size-based scheduling algorithms like Shortest Remaining Process Time (SRPT) aim to reduce average request completion time by leveraging known or estimated request sizes and allowing preemption by incoming jobs with shorter service times. However, two main challenges arise when applying size-based scheduling to LLM systems. First, accurately predicting output lengths from prompts is challenging and often resource-intensive, making it impractical for many systems. As a result, the state-of-the-art LLM systems default to first-come, first-served scheduling, which can lead to head-of-line blocking and reduced system efficiency. Second, preemption introduces extra memory overhead to LLM systems as they must maintain intermediate states for unfinished (preempted) requests. In this paper, we propose TRAIL, a method to obtain output predictions from the target LLM itself. After generating each output token, we recycle the embedding of its internal structure as input for a lightweight classifier that predicts the remaining length for each running request. Using these predictions, we propose a prediction-based SRPT variant with limited preemption designed to account for memory overhead in LLM systems. This variant allows preemption early in request execution when memory consumption is low but restricts preemption as requests approach completion to optimize resource utilization. On the theoretical side, we derive a closed-form formula for this SRPT variant in an M/G/1 queue model, which demonstrates its potential value. In our system, we implement this preemption policy alongside our embedding-based prediction method. Our refined predictions from layer embeddings achieve 2.66x lower mean absolute error compared to BERT predictions from sequence prompts. TRAIL achieves 1.66x to 2.01x lower mean latency on the Alpaca dataset and 1.76x to 24.07x lower mean time to the first token compared to the state-of-the-art serving system.

The impact of Internet phenomena depends on how they impact users, but researchers lack visibility into how to translate Internet events into their impact. Distressingly, the research community seems to have lost hope of obtaining this information without relying on privileged viewpoints. We argue for optimism thanks to new network measurement methods and changes in Internet structure which make it possible to construct an "Internet traffic map". This map would identify the locations of users and major services, the paths between them, and the relative activity levels routed along these paths. We sketch our vision for the map, detail new measurement ideas for map construction, and identify key challenges that the research community should tackle. The realization of an Internet traffic map will be an Internet-scale research effort with Internet-scale impacts that reach far beyond the research community, and so we hope our fellow researchers are excited to join us in addressing this challenge.

This corrigendum corrects and extends our results on the benefit of peer locking in mitigating the propagation of route leaks on the Internet, originally published in [2]. The updated results show even higher benefits of peer locking than originally reported, and an extended analysis covering additional peer locking deployment scenarios shows partial deployments also yield significant reduction in propagation of leaked routes.

Does an outage impact any users? Can a geolocation database known to be good at locating users and bad at infrastructure be trusted for a particular prefix? Is a content-heavy network likely to peer with a particular network? For these questions and many more, knowing which prefixes contain Internet users aids in interpreting Internet analysis. However, existing datasets of Internet activity are out of date, unvalidated, based on privileged data, or too coarse. As a step towards identifying which IP prefixes contain users, we present multiple novel techniques to identify which IP prefixes host web clients without relying on privileged data. Our techniques identify client activity in ASes responsible for 98.8% of Microsoft CDN traffic and in prefixes responsible for 95.2% of Microsoft CDN traffic. Less than 1% of prefixes identified by our technique as active do not contact Microsoft at all. We present measurements of Internet usage worldwide and sketch future directions for extending the techniques to measure relative activity levels across prefixes.

The Tier-1 ISPs have been considered the Internet's backbone since the dawn of the modern Internet 30 years ago, as they guarantee global reachability. However, their influence and importance are waning as Internet flattening decreases the demand for transit services and increases the importance of private interconnections. Conversely, major cloud providers -- Amazon, Google, IBM, and Microsoft-- are gaining in importance as more services are hosted on their infrastructures. They ardently support Internet flattening and are rapidly expanding their global footprints, which enables them to bypass the Tier-1 ISPs and other large transit providers to reach many destinations.