2b439e8a5f
Issue files (.scratch/issues/20-29): retrospective specs for all work done in the current sprint — hardening, route-timeout, start-layer protocol, heartbeat stats, availability map, rolling RPM, smart assignment, throughput routing, routing tests, relay outbound client. ADRs (docs/adr/0011-0014): 0011 — Auto-shard from memory budget and tracker network assignment 0012 — X-Meshnet-Start-Layer overlapping shard execution protocol 0013 — Rolling RPM statistics, smart assignment scoring, throughput routing 0014 — Relay outbound client for NAT/internet pipeline hops prd.json: US-020 through US-029 added, all marked done. ralph_progress.py now shows 29/29 complete (100%). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
33 lines
1.1 KiB
Markdown
33 lines
1.1 KiB
Markdown
# US-026 — Smart model assignment via demand×coverage scoring
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Status: done
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Priority: Medium
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Stage: Implemented
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## Context
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`/v1/network/assign` currently picks the model with the largest uncovered shard gap,
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ignoring traffic. A model serving 1000 RPM at 60% coverage is far more valuable to fill
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than a zero-traffic model at 50% coverage.
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## Scoring formula
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```
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score = (demand_rpm + 1.0) × (coverage_deficit + 0.01)
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```
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- `demand_rpm`: combined RPM from `_StatsCollector.get_combined_stats()`
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- `coverage_deficit`: fraction of model layers with zero node coverage, in [0.0, 1.0]
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- `+1.0` floor: models with no traffic still compete by coverage
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- `+0.01` floor: fully-covered models still have a non-zero score if they have traffic
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`price_per_token: 0.0` reserved in the response for future billing integration.
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## Acceptance criteria
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- [ ] `_handle_network_assign` computes score per model and returns the highest
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- [ ] Demand uses combined stats (local + peer slices)
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- [ ] `price_per_token: 0.0` present in response
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- [ ] Test: high-demand low-coverage model beats low-demand high-coverage model
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- [ ] `python -m pytest` passes
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