This work asks whether a Large Language Model can replace the static routing policy itself, reading HAProxy and Prometheus telemetry every 10 seconds and isolating faulty servers through guardrailed calls to the HAProxy Data Plane API.
Abstract
Static load balancers cannot mitigate a backend that is degraded rather than down: round-robin and least-connections keep routing traffic to a server returning HTTP 500s until an operator intervenes. We ask whether a Large Language Model can replace the static routing policy itself, reading HAProxy and Prometheus telemetry every 10 seconds and isolating faulty servers through guardrailed calls to the HAProxy Data Plane API. On a reproducible benchmark with a persistent structural fault built into roughly one-third of a heterogeneous fleet, we sweep 15 open-weight models across five families (0.35B to 35B total parameters; dense, mixture-of-experts, and efficient-sparse architectures), reasoning modes, fleet scales of 3 to 9 backends, and two routing algorithms, totaling 240 runs. We find a capability threshold near 3B active parameters. Below it, LLM policies are typically unreliable and sometimes worse than no policy; above it, every model, regardless of architecture, saturates near an 88% reduction in client-perceived 5xx errors over the static baseline. The threshold is approximate: Gemma 4 E2B clears it with 2B active parameters, while the dense 3B Granite 4.0 Micro does not. The availability gain has costs. Draining concentrates load onto surviving servers, inflating tail latency 2.6 to 2.8 times, and enabling reasoning multiplies token spend roughly tenfold, overrunning the control interval and degrading effectiveness. The efficient operating point is a supra-threshold model in its cheapest non-reasoning mode, wrapped inside deterministic guardrails.
Load balancers in practice often rely on fixed heuristics such as weighted round-robin (WRR) or least connection (LC). Although these methods scale well, they do not capture differences in backend service capacity or runtime performance variations. which can increase tail latency and request drop rates in shared clusters. This issue becomes particularly significant for modern cloud inference workloads such as large language model (LLM) serving, where request processing times and backend utilization can vary substantially. To address this issue, we propose LP-WRR, an external control-plane mechanism that adjusts weighted round-robin weights using latency feedback. Our method periodically solves a constrained linear program (LP) to reduce expected service latency, then smooths the resulting weights with an exponential moving average so that updates do not fluctuate excessively, all while keeping the optimization logic off the data path. We then evaluate LP-WRR in a simulator for shared latency-sensitive services, using workloads derived from seven days of Azure ChatGPT traces as a representative inference workload, and compare it with the original load-balancing algorithm. Across the heterogeneity scenarios at low workload, our method improves both mean and tail latency, reducing p95 latency by $\mathbf{2 4. 7 \%}$ on average compared to baseline policies. Under the highest tested load, it also lowers the system-wide drop ratio in both homogeneous and heterogeneous infrastructure.
Hai Pham Thanh, Dang Hoang Nguyen, Anh Nguyen Tuan et al.· IEEE International Conferenc...· 0 citations
FunPilot is presented, a system that enables rapid LLM-assisted diagnosis and remediation for serverless applications that uses an event-driven control loop to diagnose active symptoms, derive control knob updates, and validate remediation decisions while coordinating with the underlying autoscaler.
Fresh, objectively scored benchmark items can support auditable accuracy-cost-latency routing when features encode verifiable computational structure, and show that fresh, objectively scored benchmark items can support auditable accuracy-cost-latency routing when features encode verifiable computational structure.
Grace Xu· Journal of Science Innovatio...· 0 citations
Anytest, an in-situ black-box testing tool that localizes root causes of transport-layer NPAs on commodity RoCEv2 RNICs and Ethernet switches without re-cabling or hardware modification, and implements Anytest's DPDK-based endpoints, which realize protocol correctness while enforcing μs-level packet timing at the hardware line rate.
Zhaochen Zhang, Jiaqi Gao, Sheng Cheng et al.· Conference on Applications,...· 0 citations
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Jiaxuan Chen, Jianshu She, Ye Yuan et al.· arXiv.org· 1 citation
Performance modeling is central to hardware design and software optimization, yet constructing these models requires structured reasoning about computation, data reuse, storage, and movement. We introduce PerfReasoning, a benchmark that evaluates LLMs both as direct performance reasoners and as generators of analytical performance-model code. Given workload, architecture, and mapping specifications, models compare mappings and predict off-chip traffic and buffer requirements. The strongest closed-source models exceed 90% on reasoning-based Q&A, and the best open-weight model reaches 82.4%. However, model construction is substantially harder: while GPT-5.6 Sol exceeds 80% pass rate, all other model configurations average below 15% and vary markedly across runs. Task-specific RL raises a 4B model's mapping-reasoning accuracy by 15.7 points, whereas feedback-free multi-round self-revision prompting is not reliably effective. PerfReasoning exposes the gap between plausible architectural reasoning and reliable performance-model construction. We will publicly release the benchmark to support reproducible evaluation and track future progress.
Da Zhao, K. Sankaralingam, Christos Kozyrakis et al.· 0 citations
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