End-to-End Latency-Minimizing and Load-Balanced Request Scheduling for Edge LLM Inference in Agentic AI Services
Zhen Li, Jun Cai, Haoran Gao, An Li, Tan Li
cs.AI
Quick verdict
Lyapunov + reward-redistribution RL scheduler for edge LLM inference; strong simulation gains reported, but chat traces and limited external baselines temper the claims.
Summary
The paper proposes LYREO, an online scheduler for LLM inference requests across heterogeneous wireless edge servers serving agentic AI applications. It develops a fine-grained cross-slot system model covering wireless transmission, batched prefill, iteration-level decoding with continuous batching (early-finished eviction, late-joining admission), and KV-cache evolution, and defines server workload as normalized KV-cache memory-time consumption. The long-term latency-minimization problem under peak-memory and variance-based load-balancing constraints is reformulated via Lyapunov virtual queues into a per-slot drift-plus-penalty objective, cast as a sequence-MDP with delayed rewards, and solved with PPO combined with an LSTM-based reward redistribution using sequence truncation and value bootstrapping; a theorem bounds the learned policy's performance gap. Simulations (8 servers with RTX 3080/3090/4090 throughput profiles from vLLM profiling of Llama-3.2-1B, LMSYS-Chat-1M request lengths, 800 one-second slots, 5 seeds) report ~2.4s average latency versus 3.9s (Ly-PPO) and 5.2s (PPO), the lowest load-balancing deviation, and ablations showing count-based workload and constraint removal degrade tail latency and KV headroom. Main limitations: simulation-only evaluation with chat rather than agentic traces, a priori known output lengths, an omitted index-mapping detail, no code release, and baselines excluding cited state-of-the-art LLM-serving schedulers.
Review complete
Assessment at a glance
5
risk flags identified
● high 0
● medium 3
● low 2
5 strengths · 5 weaknesses · 5 author questions
Levels reflect verification complexity — not a quality score. Evolve never rates your paper.
Key claims under review
- Conventional atomic-task edge scheduling models fail to capture cross-slot, multi-stage LLM inference, and a KV-cache-tracking model (Section III-C) captures how each assignment reshapes serving conditions and end-to-end latency.
- Normalized KV-cache memory-time consumption (Eq. 15) is a more faithful workload signal than request counts for heterogeneous edge LLM servers, as confirmed by the LYREO-Count ablation in Table II.
- Lyapunov virtual queues convert the long-term load-balancing constraint into per-slot corrective pressure, and LSTM-based reward redistribution with sequence truncation and value bootstrapping converts delayed latency feedback into timely learning signals without changing the optimal policy.
- The performance gap of the learned policy versus the ideal redistributed-reward optimum is bounded by Eq. (37), with the truncation contribution shrinking as gamma^H.
- In simulation, LYREO reaches ~2.4s average end-to-end latency versus 3.9s (Ly-PPO) and 5.2s (PPO) while achieving the lowest load-balancing deviation, with ablations confirming each component's necessity.
Strengths
Fine-grained cross-slot inference model
Section III-C explicitly models batched prefill (Eq. 4), DEED-style iteration-level decoding with early-finished eviction and late-joining admission, and per-iteration KV-cache evolution (Eqs. 5-12) with a non-monotonic memory trajectory, going beyond the atomic single-slot task abstraction the authors correctly criticize in prior edge work.
Workload metric validated by ablation, not just asserted
The normalized KV-cache memory-time consumption (Eq. 15) is tested against request counting: Table II shows LYREO-Count is worse on all four metrics (latency +6.1%, deviation +37.5%, P99 +21.9%, high-KV ratio +25.2%), giving empirical support for the metric's design.
Explicit error budget connecting truncation to policy quality
Ablation quantifies the latency/load-balance trade-off directly
Broad sensitivity analysis
Weaknesses
Agentic framing not reflected in the evaluation workload
Baselines exclude the cited state of the art
Key implementation detail omitted for brevity
Hard memory constraint demoted to a soft penalty in learning
Thin statistical reporting and minor numerical inconsistency
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Frequently asked questions
What is LYREO and what problem does it solve?
LYREO is a reinforcement-learning-based request scheduler for LLM inference running on multiple wireless edge servers. It addresses two coupled difficulties: an LLM request occupies a server's GPU memory and compute across many seconds (so scheduling decisions interact across time), and the latency consequence of a decision is only observable after the request completes. LYREO uses Lyapunov virtual queues to keep long-term workload balanced across heterogeneous servers and reward redistribution (an LSTM that predicts cumulative return) to convert delayed outcomes into immediate learning signals, optimized with PPO.
How much better is LYREO than existing approaches?
In the paper's simulations (8 edge servers equipped with RTX 3080/3090/4090-class throughput profiles running Llama-3.2-1B, chat-like request lengths from LMSYS-Chat-1M), LYREO converges to about 2.4 seconds average end-to-end latency, versus roughly 3.9 seconds for Lyapunov-assisted PPO and 5.2 seconds for vanilla PPO, while also achieving the lowest load-balancing deviation. These are simulation results; no comparison with production LLM-serving schedulers like Llumnix is provided, and no real testbed evaluation is reported.
What is the KV cache memory-time workload metric?
Instead of counting queued requests, the paper measures each server's per-slot workload as the KV-cache memory occupied at each decoding iteration multiplied by that iteration's duration, summed and normalized by the server's total memory-time capacity (memory size times slot length). This captures both how much memory a request holds and how long it holds it, so a long-output request on a slow, small-memory server registers as much heavier load than the same request on a fast, large-memory server. An ablation shows replacing it with request counting degrades latency, tail latency, and memory headroom.
Is code or a real-system implementation available?
No. The evaluation is simulation-only; GPU throughput parameters come from offline profiling with a vLLM-based engine on real RTX 3080/3090/4090 hardware, but the paper states no public code or data release, and one implementation detail (the cross-slot index mapping for per-request decoding time) is explicitly omitted. Independent reproduction currently requires re-deriving that mapping and re-profiling the GPUs.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Figures are available only as axis-label text in the extraction; all plotted values cited in this review come from the authors' in-text descriptions and cannot be independently verified.
- The abstract's final sentence is truncated in the extracted text.
- Equations were not independently re-derived; the correctness of Lemma 1 and Theorem 1 was assessed from the presented proofs at a structural level only.
- This review covers arXiv v1 (2609.17193, 15 Sep 2026); the paper does not appear to have undergone peer review yet.
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