TLDR: This paper introduces Amin, an adaptive algorithm for scheduling Large Language Model (LLM) inference that effectively minimizes latency despite uncertainty in output lengths. Unlike conservative methods that over-allocate resources, Amin dynamically refines output length estimates based on lower bounds, proving robust and efficient even with imprecise predictions, often matching the performance of an ideal scheduler.
Large Language Models (LLMs) have transformed artificial intelligence, enabling human-like text generation for various applications, from conversational AI to advanced search. However, the process of running these models, known as LLM inference, is a significant operational challenge. It’s an online, multi-task service that consumes substantial energy, making efficient scheduling crucial to reduce latency and power consumption.
A core difficulty in optimizing LLM inference is the uncertainty surrounding output lengths. While the length of an input prompt is known immediately, the length of the generated response is not. This output length critically impacts how much memory is needed and how long the processing will take. Traditional scheduling methods often assume perfect knowledge, which isn’t realistic in real-world scenarios where predictions are inherently imperfect.
Addressing Prediction Uncertainty in LLM Scheduling
To tackle this, a recent research paper, “Adaptively Robust LLM Inference Optimization under Prediction Uncertainty,” by Zixi Chen, Yinyu Ye, and Zijie Zhou, proposes novel algorithms that leverage machine learning predictions to manage this uncertainty. Instead of precise output lengths, their approach assumes predictions provide an interval classification—a minimum and maximum range for each request’s output.
The paper first introduces a straightforward, conservative algorithm called Amax. This algorithm schedules requests based on the upper bound of their predicted output lengths. The idea is to prevent memory overflow by always reserving enough memory for the worst-case scenario. While this guarantees no memory issues, it often leads to significant inefficiency. If the actual output lengths are much shorter than the predicted upper bounds, Amax overestimates memory usage, reducing the number of requests that can be processed simultaneously and increasing overall latency. Its performance degrades considerably as prediction accuracy decreases.
To overcome Amax’s limitations, the researchers developed Amin, an adaptive and more robust algorithm. Amin takes the opposite approach: it initially treats the predicted lower bound of the output length as the estimate. As the LLM generates tokens, Amin dynamically refines this estimate. If the system detects that continuing to process the current batch would exceed memory limits, Amin intelligently removes jobs from the batch, prioritizing those that have generated fewer tokens. Crucially, when a job is removed, its lower bound estimate is updated to reflect the tokens already generated, ensuring that future scheduling decisions are more informed.
A significant advantage of Amin is that it relies solely on the predicted lower bounds of output lengths. Predicting accurate lower bounds is often much easier and faster than estimating precise upper bounds in real-world settings. This design choice makes Amin highly practical and robust, especially when prediction intervals are wide or asymmetric.
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Performance and Practical Implications
The theoretical analysis of Amin shows a substantial improvement over Amax. While Amax’s performance can become unbounded (very poor) when predictions are highly uncertain, Amin achieves a logarithmic competitive ratio, indicating much stronger robustness. This means Amin performs consistently well even with imprecise predictions, often approaching the efficiency of an ideal scheduler that has perfect foresight.
Numerical experiments using a real-world dataset (LMSYS-Chat-1M) further validate Amin’s effectiveness. In scenarios with very rough predictions (e.g., output length anywhere between 1 and 1000 tokens), Amax performed poorly due to its conservative nature. In contrast, Amin consistently delivered average latency nearly identical to the “hindsight” scheduler, which operates with perfect knowledge of output lengths. Even when predictions were more accurate, Amin continued to match or closely approach the performance of the ideal scheduler, significantly outperforming Amax as prediction uncertainty increased.
The research also explores how Amin performs under specific output distributions and introduces a “promote-ℓ” policy (Aℓ) for two-point distributions, showing that adaptive strategies can be chosen based on workload characteristics to further enhance performance. This work provides valuable insights for designing and deploying efficient and robust LLM inference systems in dynamic, real-world environments where prediction uncertainty is a given. You can read the full paper here.
