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ARXIV:2601.21758 · LLM INFRASTRUCTURE · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2601.21758LLM INFRASTRUCTURESUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
EWSJF is an adaptive scheduler that significantly improves LLM inference by optimizing workload distribution to enhance fairness and throughput.
Opportunity summary
Pain EWSJF is an adaptive scheduler that significantly improves LLM inference by optimizing workload distribution to enhance fairness and throughput.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
EWSJF is an adaptive scheduler that significantly improves LLM inference by optimizing workload distribution to enhance fairness and throughput. Standard First-Come, First-Served (FCFS) policies suffer from severe head-of-line blocking, leading to high tail latency…
Serving Large Language Models (LLMs) under mixed workloads--short, latency-sensitive interactive queries alongside long, throughput-oriented batch requests--poses a fundamental scheduling challenge. Standard First-Come, First-Served (FCFS) policies suffer from severe head-of-line blocking, leading to high tail…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. We introduce EWSJF (Effective Workload-based Shortest Job First), an adaptive request-level scheduler that learns workload structure in real time to jointly improve fairness and…
LLM Infrastructure moved forward this cycle; last verified April 2026. Public score 6.0/10.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score6.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
EWSJF is an adaptive scheduler that significantly improves LLM inference by optimizing workload distribution to enhance fairness and throughput.
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Paper Pack
10.48550/arXiv.2601.21758EWSJF is an adaptive scheduler that significantly improves LLM inference by optimizing workload distribution to enhance fairness and throughput.
Abstract
Serving Large Language Models (LLMs) under mixed workloads--short, latency-sensitive interactive queries alongside long, throughput-oriented batch requests--poses a fundamental scheduling challenge. Standard First-Come, First-Served (FCFS) policies suffer from severe head-of-line blocking, leading to high tail latency and underutilized hardware. We introduce EWSJF (Effective Workload-based Shortest Job First), an adaptive request-level scheduler that learns workload structure in real time to jointly improve fairness and throughput. EWSJF operates upstream of execution-level schedulers and integrates four components: (1) Refine-and-Prune, an unsupervised partitioning algorithm that discovers performance-homogeneous request groups; (2) Dynamic Queue Routing for assigning requests to these groups; (3) Density-Weighted Scoring, a context-aware prioritization function balancing urgency and fairness; and (4) Bayesian Meta-Optimization, which continuously tunes scoring and partitioning parameters based on live performance feedback. Implemented in vLLM, EWSJF improves end-to-end throughput by over 30% and reduces average Time-To-First-Token for short requests by up to 4x compared to FCFS. These results demonstrate that adaptive, learning-based request scheduling is a critical missing layer for efficient and responsive LLM serving. Implementation available at https://anonymous.4open.science/r/vllm_0110-32D8.
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Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
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Preparing verified analysis
Dimensions overall score 6.0
PROBLEM
EWSJF is an adaptive scheduler that significantly improves LLM inference by optimizing workload distribution to enhance fairness and throughput. Standard First-Come, First-Served (FCFS) policies suffer from severe head-of-line blocking, leading to high tail latency and underutil...
METHOD
Serving Large Language Models (LLMs) under mixed workloads--short, latency-sensitive interactive queries alongside long, throughput-oriented batch requests--poses a fundamental scheduling challenge. Standard First-Come, First-Served (FCFS) policies suffer from severe head-of-lin...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. We introduce EWSJF (Effective Workload-based Shortest Job First), an adaptive request-level scheduler that learns workload structure in real time to jointly improve fairness and throughput.
WHY NOW
LLM Infrastructure moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed public claims while anchored extraction refreshes.
EWSJF is an adaptive scheduler that significantly improves LLM inference by optimizing workload distribution to enhance fairness and throughput. Standard First-Come, First-Served (FCFS) policies suffer from severe head-of-line blocking, leading to high tail latency and underutilized hardware.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Serving Large Language Models (LLMs) under mixed workloads--short, latency-sensitive interactive queries alongside long, throughput-oriented batch requests--poses a fundamental scheduling challenge. Standard First-Come, First-Served (FCFS) policies suffer from severe head-of-line blocking, leading to high tail latency and underutilized hardware.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. We introduce EWSJF (Effective Workload-based Shortest Job First), an adaptive request-level scheduler that learns workload structure in real time to jointly improve fairness and throughput.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Infrastructure moved forward this cycle; last verified April 2026. Public score 6.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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EWSJF is an adaptive scheduler that significantly improves LLM inference by optimizing workload distribution to enhance fairness and throughput.
Segment
LLM Infrastructure
Adoption evidence
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Commercial read
6.0/10 public viability
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status
missing
reason
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proof status
unverified
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confidence low
next verification path
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Source missing: Build Passport payload.
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Evidence coverage
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stale
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Build readiness
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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missing
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Evidence
0 references, 0 sources, 17% evidence coverage.
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Buyer clarity
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
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missing
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Classify regulatory flags before commercialization planning.
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ARTIFACTS
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DEFENSIBILITY
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