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Paper Abstract and Keywords
Presentation 2026-04-09 12:50
AI load balancer according to SLO
Kenshiro Wada, Kenzo Okuda, Hiroki Baba, Kentaro Hayashi, Naoki Kimishima, Tomonori Takeda (NTT) NS2026-5
Abstract (in Japanese) (See Japanese page) 
(in English) In the operational deployment of large language models (LLMs), inference latency is a critical performance metric.
However, actual requests exhibit strong heterogeneity in input length and concurrent execution counts, making optimisation difficult with a single configuration or simple round-robin distribution.
vLLM incorporates features such as Continuous Batching and Chunked Prefill, enabling the configuration of multiple profiles—latency-oriented, throughput-oriented, or long-text-oriented—through settings.
However, in existing deployments, these are applied statically, failing to fully utilise the performance potential.
This paper proposes an AI-based routing method that dynamically switches execution profiles based on request characteristics: input length (short, medium, long), prefix reuse, and concurrent execution count.
It selects the optimal profile for each characteristic and quantitatively evaluates its effectiveness.
Experimental results show the proposed method improves average and p50 latency by approximately 14%, and p95 latency by approximately 7%, compared to Round-Robin.
This demonstrates that profile selection based on request characteristics is an effective and easily implementable optimisation technique in real-world operations.
Keyword (in Japanese) (See Japanese page) 
(in English) AI / LLM / Proxy / / / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 2, NS2026-5, pp. 23-28, April 2026.
Paper # NS2026-5 
Date of Issue 2026-04-02 (NS) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NS2026-5

Conference Information
Committee NS  
Conference Date 2026-04-09 - 2026-04-10 
Place (in Japanese) (See Japanese page) 
Place (in English) Yakushima Island Environmental and Cultural Village Center + Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Communication traffic theory, Traffic and quality evaluation, Network performance evaluation, QoS/QoE, Reliability and robustness, Traffic and quality management, AI and machine learning, Network and system operation management, High capacity, low latency, many connections, General 
Paper Information
Registration To NS 
Conference Code 2026-04-NS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) AI load balancer according to SLO 
Sub Title (in English)  
Keyword(1) AI  
Keyword(2) LLM  
Keyword(3) Proxy  
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1st Author's Name Kenshiro Wada  
1st Author's Affiliation NTT (NTT)
2nd Author's Name Kenzo Okuda  
2nd Author's Affiliation NTT (NTT)
3rd Author's Name Hiroki Baba  
3rd Author's Affiliation NTT (NTT)
4th Author's Name Kentaro Hayashi  
4th Author's Affiliation NTT (NTT)
5th Author's Name Naoki Kimishima  
5th Author's Affiliation NTT (NTT)
6th Author's Name Tomonori Takeda  
6th Author's Affiliation NTT (NTT)
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Speaker Author-1 
Date Time 2026-04-09 12:50:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2026-5 
Volume (vol) vol.126 
Number (no) no.2 
Page pp.23-28 
#Pages 6 
Date of Issue 2026-04-02 (NS) 

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