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# kvcache-simulator # kvcache-simulator
Discrete-event simulator for cluster-level LLM **prefill** serving with a Discrete-event simulator for cluster-level LLM **prefill** serving with a
two-tier KV cache (GPU HBM + CPU DRAM / v6d) and KV-aware request routing. two-tier KV cache and routing experiments. The simulator models a
Replays real production traces against a synthetic cluster so you can PD-disaggregated deployment: only the **prefill** path is simulated, while
ablate routing strategies and cache sizing without spinning up any GPUs. decode is reduced to a small completion tail for TTFT/E2E accounting.
Assumes **PD (prefill/decode) disaggregation** — only the prefill path is It is intended for answering questions like:
modeled.
## Features - How much do different KV-aware routers help on the same trace?
- How much HBM/DRAM capacity is enough before routing dominates?
- How do prefix-locality policies behave under bucketed input-length pools?
- What is the gap between online LRU and offline-optimal cache capacity?
- **Architecture-derived roofline compute** — auto-derives FLOPs, ## What The Repo Models
attention coefficients, and weight-streaming costs from model
architecture (MoE, MLA, GQA, DSA, sliding window). - **Architecture-derived prefill cost** from model structure, including MoE,
- **HuggingFace config.json auto-parsing** — drop in any HF MLA, GQA, sliding-window attention, and DSA.
`config.json` and the simulator extracts layer counts, attention heads, - **Two-tier KV hierarchy** with L0 GPU HBM and L1 host DRAM, plus remote
MoE expert configs, MLA LoRA ranks, and DSA sparse parameters. RDMA fetches via a meta-store.
- **Built-in GPU hardware presets** — H100, H800, H20, A100-80GB, - **Single-pool and bucketed clusters**. Bucketed mode separates the service
A100-40GB, B200 with tensor-parallel scaling (e.g. `8xb200`). into input-length buckets with isolated instance pools and meta-stores.
- **Two-tier KV cache hierarchy** — L0 (GPU HBM) + L1 (CPU DRAM) with - **Local instance routing and global bucket routing** with detailed
LRU eviction and cross-instance RDMA fetch via a cluster-wide per-request routing logs.
meta-store. - **Trace replay with optional input-length filtering** so the same trace can
- **11 routing policies** — from baselines (random, round-robin) to be sliced into buckets without rewriting the source file.
cache-aware (min\_pd, prefix\_affinity) for systematic ablation. - **Offline oracle analysis** for unlimited capacity, Belady, and LRU hit-rate
- **Token-bucket link contention** — PCIe and RDMA bandwidth modeled with ceilings.
reservation-based token-bucket queues.
- **Oracle analysis** — computes theoretical hit-rate ceilings (infinite ## Highlights
cache, Belady optimal, LRU) for gap analysis.
- **HF `config.json` auto-loading**: point `model.config_json` at a model
config and the simulator derives architecture parameters automatically.
- **Hardware presets**: `h100`, `h800`, `h20`, `h20-141g`, `a100-80gb`,
`a100-40gb`, `b200`, and `b300`, plus TP variants such as `8xb200`.
- **18 local router modes** covering baselines, load-based, cache-aware,
affinity, and TTFT-estimating policies.
- **2 global bucket router modes**: `strict_input_length` and `bucket_score`.
- **Detailed outputs**: `summary.json`, `per_request.csv`, `instances.csv`,
`routing_log.jsonl`, plus `ablation.json` / `oracle.json` when applicable.
## Build ## Build
```bash ```bash
cargo build --release cargo build --release
# binary: target/release/kvcache-sim
``` ```
Fetch the upstream trace (consumed as a git submodule): If you want the public Qwen trace submodule as well:
```bash ```bash
git submodule update --init --recursive git submodule update --init --recursive
``` ```
## Usage The release binary is:
### 1. Run a single simulation
```bash ```bash
target/release/kvcache-sim run --config configs/glm5-8xb200-hf.yaml target/release/kvcache-sim
``` ```
Prints `summary.json` to stdout and writes the full output directory ## Quick Start
(see [Outputs](#outputs) below).
### 2. Compare routers on the same trace (ablation) Validate a config:
```bash
target/release/kvcache-sim validate --config configs/glm5-8xb200.yaml
```
Run one simulation:
```bash
target/release/kvcache-sim run --config configs/glm5-8xb200.yaml
```
Compare several routers on the same trace:
```bash ```bash
target/release/kvcache-sim ablate \ target/release/kvcache-sim ablate \
--config configs/glm5-8xb200-hf.yaml \ --config configs/glm5-8xb200.yaml \
--routers random,least_loaded,least_tokens,min_pd,prefix_affinity \ --routers random,least_loaded,cache_score,cache_affinity,estimated_ttft
--evict-policies lru \
--output-dir runs/glm5_ablation
``` ```
Writes `ablation.json` with one row per `router x evict_policy`. Auto-pick the smallest cluster size that meets a TTFT target, then ablate at
that size:
`ablate` currently supports only `lru` as a valid eviction policy. The
aggregated output keeps the online prefill-time metrics
(`ttft_mean/p50/p95/p99`) and omits `e2e`.
The previous replay-based `belady` approximation has been removed from
the CLI because it was not an exact full-hierarchy Belady algorithm and
could produce misleading comparisons against `lru`.
### 3. Compute theoretical hit-rate ceilings (oracle)
```bash ```bash
# Cluster-aggregate capacity (default) target/release/kvcache-sim ablate \
target/release/kvcache-sim oracle \ --config configs/glm5-8xb200.yaml \
--config configs/glm5-8xb200-hf.yaml --num-instances 64 --auto-instances \
--auto-probe-router cache_score \
# A single instance's HBM budget --auto-target-ttft-mean 4.0
target/release/kvcache-sim oracle \
--config configs/glm5-8xb200-hf.yaml --per-instance
# Explicit capacity in blocks
target/release/kvcache-sim oracle \
--config configs/glm5-8xb200-hf.yaml --capacity-blocks 200000
``` ```
Reports three numbers: Run the oracle:
- `unlimited.hit_rate` — absolute ceiling (infinite cache)
- `belady_finite.hit_rate` — optimal-eviction ceiling at the given capacity
- `lru_finite.hit_rate` — production LRU at the same capacity
Gap between `lru_finite` and `belady_finite` = headroom from a smarter
eviction policy. Gap between `belady_finite` and `unlimited` = headroom
only reachable by adding capacity.
### 4. Validate a config without running
```bash ```bash
target/release/kvcache-sim validate --config configs/glm5-8xb200-hf.yaml target/release/kvcache-sim oracle \
--config configs/glm5-8xb200.yaml \
--per-instance
``` ```
Parses the YAML, prints derived per-instance block budgets, and dumps `run` prints `summary.json` to stdout and also writes the full output directory
the first 5 trace records so you can sanity-check the path. under `sim.output_dir`.
## CLI overrides ## Current Command Boundaries
These flags work on **all** subcommands and override the YAML in place, The repository now supports both legacy single-pool clusters and bucketed
so the same config can be reused across sweeps: service topologies, but not every CLI path supports both yet.
- `run`: supports `cluster.num_instances` and `cluster.buckets`
- `validate`: supports `cluster.num_instances` and `cluster.buckets`
- `ablate`: currently **single-pool only**
- `ablate --evict-policies`: currently supports **`lru` only**
- `oracle`: currently **single-pool only**
- `--num-instances` override: currently **single-pool only**
- `--auto-instances`: currently **single-pool only**
In practice, bucket-aware experiments are ready in `run`, while fixed-placement
ablation and oracle analysis still reject `cluster.buckets`.
## Config Model
### Single-Pool Cluster
Use `cluster.num_instances` for the original flat instance pool:
```yaml
cluster:
num_instances: 32
meta_store:
ttl_seconds: 300.0
router:
mode: cache_affinity
```
### Bucketed Service
Use `cluster.buckets` plus a `global_router` to model explicit input-length
buckets:
```yaml
cluster:
meta_store:
ttl_seconds: 300.0
router:
mode: cache_affinity
load_alpha: 1.5
prefix_k: 8
global_router:
mode: strict_input_length
length_penalty_weight: 1.0
load_weight: 1.0
cache_weight: 1.0
buckets:
- name: short
input_length_min: 0
input_length_max: 32768
num_instances: 8
- name: long
input_length_min: 32769
input_length_max: 131072
num_instances: 4
```
Rules enforced by config validation:
- `cluster.num_instances` and `cluster.buckets` are mutually exclusive
- bucket ranges must not overlap
- every bucket must have `num_instances > 0`
- `input_length_min <= input_length_max`
### CLI Overrides
These flags apply on top of the YAML config:
| Flag | Overrides | | Flag | Overrides |
|--------------------------|-------------------------------------------| |------|-----------|
| `--num-instances <N>` | `cluster.num_instances` | | `--num-instances <N>` | `cluster.num_instances` |
| `--max-requests <N>` | `sim.max_requests` | | `--max-requests <N>` | `sim.max_requests` |
| `--trace <PATH>` | `sim.trace_path` | | `--trace <PATH>` | `sim.trace_path` |
@@ -121,233 +181,206 @@ so the same config can be reused across sweeps:
| `--seed <N>` | `sim.seed` | | `--seed <N>` | `sim.seed` |
| `--precise-topk <N>` | `cluster.router.precise_probe_topk` | | `--precise-topk <N>` | `cluster.router.precise_probe_topk` |
| `--ttl-seconds <S>` | `cluster.meta_store.ttl_seconds` | | `--ttl-seconds <S>` | `cluster.meta_store.ttl_seconds` |
| `--input-length-min <N>` | `sim.input_length_min` |
| `--input-length-max <N>` | `sim.input_length_max` |
`oracle` additionally takes `--capacity-blocks <N>` / `--per-instance` Subcommand-specific additions:
and `--out <PATH>`. `ablate` additionally takes `--routers <csv>` and
`--evict-policies <csv>` (currently only `lru`).
## Router modes - `ablate`: `--routers`, `--evict-policies`, `--auto-instances`,
`--auto-target-ttft-mean`, `--auto-candidates`, `--auto-probe-router`,
`--jobs`
- `oracle`: `--capacity-blocks`, `--per-instance`, `--out`
Set `cluster.router.mode` in the YAML or list in `--routers`: ## Routing Modes
### Global Bucket Routers
Configured through `cluster.global_router.mode`.
| Mode | What it does |
|------|---------------|
| `strict_input_length` | Routes to the unique bucket whose `[input_length_min, input_length_max]` contains the request. |
| `bucket_score` | Scores every bucket using weighted length mismatch, aggregate queue load, and predicted cache miss. Can intentionally deviate from the strict length bucket. |
### Local Instance Routers
Configured through `cluster.router.mode`. All of these names are accepted by
`run`, and any of them can be passed to `ablate --routers` on single-pool
configs.
| Mode | Aliases | What it does | | Mode | Aliases | What it does |
|-------------------|------------------|--------------------------------------------------------------------------------------| |------|---------|---------------|
| `random` | | Uniform random. Baseline. | | `random` | | Uniform random baseline. |
| `round_robin` | `rr` | Deterministic round-robin. Baseline. | | `round_robin` | `rr` | Deterministic round-robin baseline. |
| `least_loaded` | | `argmin(kv_blocks_used + alpha * queue_len)`. KV-blind load balance. | | `least_loaded` | | Minimizes `kv_blocks_used + alpha * queue_len`. |
| `least_tokens` | `lt` | `argmin(waiting_tokens)`. Pure load balance by queued compute work. | | `least_tokens` | `lt` | Minimizes queued token work. |
| `ttl_aware` | `ttl` | Picks instance with longest prefix in the global TTL meta-store. Cache-only. | | `ttl_aware` | `ttl` | Uses the global TTL meta-store to chase the longest reusable prefix. |
| `precise` | `precise_aware` | Probes top-K least-loaded instances' actual caches; charges probe latency into TTFT. | | `precise` | `precise_aware` | Probes top-K least-loaded instances for actual cache contents and charges probe latency. |
| `min_pd` | `minpd`, `pd` | Minimizes `P*D` (prefill tokens x ongoing requests). Cluster-wide RDMA-aware. | | `min_pd` | `minpd`, `pd` | Minimizes `P * D` using ongoing load and prefix reuse. |
| `cache_load` | `cl` | Filters to least-loaded 1/4 instances, then picks best cache prefix. | | `cache_load` | `cl` | Filters to lightly loaded instances, then chooses the best cache prefix. |
| `cache_score` | `cs` | Exponential scoring: `2^(alpha * queue_len + beta * miss_blocks)`. | | `cache_affinity` | `caff`, `ca` | Strong cache-first scoring with rendezvous-based sticky homes for prefix families. |
| `estimated_ttft` | `ettft`,`optimal`| Estimates `drain_time + fetch_time` per instance using architecture-aware compute. | | `cache_affinity_weak_rend` | `caff_weak` | Ablation: weak cache weights plus rendezvous placement. |
| `prefix_affinity` | `affinity`, `pa` | Rendezvous-hashed prefix fingerprinting for deterministic cache locality. | | `cache_affinity_strong_only` | `caff_strong` | Ablation: strong cache weights without rendezvous tie-breaking. |
| `cache_score` | `cs` | Exponential score over queue length and miss blocks. |
| `cache_score_strong` | `cs_strong`, `css` | Parity probe with stronger cache weighting than default `cache_score`. |
| `cache_score_ttl` | `csttl`, `cs_ttl` | `cache_score` variant that also uses TTL/meta-store visibility. |
| `estimated_ttft` | `ettft`, `optimal` | First-principles TTFT estimate per instance using compute plus KV movement. |
| `prefix_affinity` | `affinity`, `pa` | Deterministic prefix fingerprinting with affinity fan-out and load-aware selection. |
| `adaptive_affinity` | `aa` | Uses hot-prefix detection: affinity for short hot stems, TTFT optimization otherwise. |
| `lineage_affinity` | `la` | Combines parent stickiness, family homesets, and strong local cache scoring. |
### Router parameters Router tuning knobs in `cluster.router`:
These fields in `cluster.router` tune specific routers: | Field | Default | Used by |
|-------|---------|---------|
| `load_alpha` | `1.0` | `least_loaded`, `ttl_aware`, affinity families |
| `score_alpha` | `1.0` | `cache_score`, `cache_score_ttl` |
| `score_beta` | `0.1` | `cache_score`, `cache_score_ttl` |
| `prefix_k` | `8` | prefix and affinity fingerprinting |
| `affinity_fan_out` | `0` | `prefix_affinity`, `adaptive_affinity`, `lineage_affinity` |
| `precise_probe_latency_us` | `50.0` | `precise` |
| `precise_probe_topk` | `4` | `precise` |
| Field | Default | Used by | Description | ## Model And Hardware Configuration
|--------------------------|---------|------------------|------------------------------------------------------|
| `load_alpha` | `1.0` | `least_loaded` | Weight of queue\_len vs kv\_blocks\_used |
| `score_alpha` | `1.0` | `cache_score` | Load weight in `2^(alpha*load + beta*miss)` |
| `score_beta` | `0.1` | `cache_score` | Cache-miss weight in `2^(alpha*load + beta*miss)` |
| `prefix_k` | `8` | `prefix_affinity`| Number of leading blocks for the prefix fingerprint |
| `affinity_fan_out` | `0` | `prefix_affinity`| Top-K affinity candidates (0 = auto: n/8, min 2) |
| `precise_probe_latency_us`| `50.0`| `precise` | Simulated per-probe latency (microseconds) |
| `precise_probe_topk` | `4` | `precise` | Number of instances probed |
### Router design spectrum ### Model Config
``` Recommended pattern:
Cache-only Hybrid Load-only
(hot-spot risk) (cache-blind)
┌─────────┬───────────┬───────────┬────────────┬───────────┬───────────┐
ttl_aware precise cache_score min_pd prefix_ least_ random
cache_load affinity loaded
est_ttft least_tokens
```
`prefix_affinity` sits in a unique position: it builds **proactive cache
locality** by consistently routing same-prefix requests to the same
instances (via rendezvous hashing), rather than reactively chasing
existing cache state. This yields the highest L0 hit rates while
maintaining load balance through within-group drain-time-aware selection.
## Model configuration
### HuggingFace config.json (recommended)
Point `model.config_json` at any HF `config.json` to auto-extract
architecture:
```yaml ```yaml
model: model:
config_json: ../models/GLM-5/config.json config_json: ../models/GLM-5/config.json
dtype_bytes: 2 # required (not in HF schema) name: glm-5
block_size_tokens: 512 # required (not in HF schema) compute_dtype: fp8
weight_dtype: fp4
dtype_bytes: 1
block_size_tokens: 512
``` ```
Auto-detected features: Notes:
| Feature | Detection trigger | What it extracts | - `config_json` is resolved relative to the YAML file
|-----------|-------------------------------|----------------------------------------------| - explicit YAML fields override values loaded from the model config
| **MoE** | `n_routed_experts`, `num_local_experts`, or `num_experts` | Expert count, active experts, shared experts, expert FFN width | - `compute_dtype` selects the compute FLOPS tier
| **MLA** | `kv_lora_rank` present | KV/Q LoRA ranks, qk\_rope/nope dims, v\_head\_dim | - `weight_dtype` controls model-weight bytes separately from KV-cache bytes
| **DSA** | `first_k_dense_replace` present| Dense window, sparse stride, first dense layers | - `dtype_bytes` sizes the KV cache
| **Sliding window** | `sliding_window` present | Window size |
| **GQA** | `num_key_value_heads < num_attention_heads` | KV head count for grouped-query attention |
Explicit YAML fields always override the auto-detected values. The architecture loader understands:
### Inline specification - MoE expert counts and active experts
- MLA LoRA ranks and attention dimensions
- DSA sparse-attention parameters
- sliding-window attention
- GQA from KV-head count
Alternatively, specify architecture fields directly: ### Hardware Presets
```yaml Recommended pattern:
model:
name: qwen2.5-coder-7b
num_layers: 28
hidden_size: 3584
num_attention_heads: 28
num_kv_heads: 4
head_dim: 128
intermediate_size: 18944
dtype_bytes: 2
block_size_tokens: 16
```
When `hidden_size` is present, the compute model is auto-derived
(architecture mode). Without it, you must supply legacy manual
coefficients (`flops_per_token_prefill`, `attn_quadratic_coeff`, etc.).
### Bundled model configs
| Model | Path | Architecture |
|-------|------|--------------|
| GLM-5 (744B/40B-active) | `models/GLM-5/config.json` | MoE (256 routed, 8 active, 1 shared) + MLA + DSA |
| GLM-5-FP8 | `models/GLM-5-FP8/config.json` | GLM-5 architecture + upstream FP8 quantization metadata |
| Qwen3-Coder-480B-A35B FP8 | `models/Qwen3-Coder-480B-A35B-Instruct-FP8/config.json` | MoE (160 experts, 8 active) + GQA |
## Hardware configuration
### Using presets (recommended)
Set `hardware.type` to a preset name — individual fields can override:
```yaml ```yaml
hardware: hardware:
type: 8xb200 type: 8xb300
hbm_bytes: 500.0e9 # override KV budget (after model weights) hbm_bytes: 1900.0e9
```
Available presets:
| Preset | FLOPS | HBM | Mem BW | PCIe |
|-------------|------------|---------|------------|------|
| `h100` | 989 TFLOPS | 80 GB | 3.35 TB/s | Gen5 |
| `h800` | 989 TFLOPS | 80 GB | 3.35 TB/s | Gen5 |
| `h20` | 148 TFLOPS | 96 GB | 4.0 TB/s | Gen5 |
| `h20-141g` | 148 TFLOPS | 141 GB | 4.8 TB/s | Gen5 |
| `a100-80gb` | 312 TFLOPS | 80 GB | 2.0 TB/s | Gen4 |
| `a100-40gb` | 312 TFLOPS | 40 GB | 1.555 TB/s | Gen4 |
| `b200` | 2.25 PFLOPS| 192 GB | 8.0 TB/s | Gen6 |
Prefix with `2x`, `4x`, or `8x` for tensor-parallel groups (e.g.
`8xh20`). FLOPS, memory bandwidth, and HBM scale linearly; RDMA and
DRAM are set to sensible per-node defaults.
### Inline specification
```yaml
hardware:
gpu_flops: 1.80e16
gpu_mem_bw: 6.40e13
hbm_bytes: 500.0e9
dram_bytes: 1.5e12 dram_bytes: 1.5e12
pcie_bw: 128.0e9
pcie_latency_us: 4.0
rdma_bw: 50.0e9
rdma_latency_us: 6.0
max_batch_slots: 256 max_batch_slots: 256
prefill_chunk_tokens: 4096
``` ```
## Architecture-aware compute model Available preset families:
The simulator derives a **roofline prefill model** from model - `h100`, `h800`, `h20`, `h20-141g`
architecture: - `a100-80gb`, `a100-40gb`
- `b200`, `b300`
- TP forms such as `2xh100`, `4xh20`, `8xb200`, `8xb300`
``` ## Bundled Configs
prefill_time(N tokens) = max(compute_time, memory_time)
compute_time = layers * (N * linear_flops + attn_coeff * N * effective_ctx(N)) / gpu_flops Representative configs in `configs/`:
memory_time = layers * weight_bytes_per_layer / gpu_mem_bw
| Config | Notes |
|--------|-------|
| `glm5-8xb200.yaml` | GLM-5 on `8xb200`, single-pool baseline config. |
| `glm5-fp8-8xh20-141g.yaml` | GLM-5-FP8 on `8xh20-141g`, with a 0-32k input-length filter. |
| `glm5-fp8-8xh20-141g-ca-tuned.yaml` | Same family as above, tuned for `cache_affinity`. |
| `glm5-nvfp4-8xb300.yaml` | GLM-5-NVFP4 on `8xb300`. |
| `glm5-nvfp4-fp8compute-8xb300.yaml` | NVFP4 weights with FP8 compute on `8xb300`. |
| `qwen3-coder-480b-8xh20.yaml` | Qwen3-Coder-480B-A35B on `8xh20`. |
Many of the `glm5-*n*.yaml` configs are bucket/slice-specific experiment
points that use `sim.input_length_min` and `sim.input_length_max`.
## Trace Inputs
This repository currently contains two trace sources:
- `bailian-traces/`
- `glm_coder_blksz_512_040915-040917.jsonl`
- `qwen3_coder_blksz_512_040915-040917.jsonl`
- `qwen-bailian-usagetraces-anon/` submodule
- public 16-token-block Qwen traces such as
`qwen_coder_blksz_16.jsonl` and `qwen_traceB_blksz_16.jsonl`
The simulator expects JSONL records with fields like:
```json
{
"chat_id": 159,
"parent_chat_id": 55,
"timestamp": 61.114,
"input_length": 521,
"output_length": 132,
"type": "text",
"turn": 2,
"hash_ids": [1089, 1090, 1091]
}
``` ```
- **MoE**: only active experts contribute to FLOPs and weight streaming Only prefill-side behavior is modeled; `output_length` is used only for a
(shared experts always counted) decode tail in completion metrics.
- **MLA**: compressed KV projections reduce attention FLOPs; KV cache
uses `kv_lora_rank + qk_rope_head_dim` instead of `2 * kv_heads * head_dim`
- **DSA**: `effective_ctx = min(N, dense_window) + max(0, N - dense_window) / sparse_stride`,
with the first K layers using full dense attention
- **GQA**: fewer KV heads reduce both attention compute and KV cache size
## Bundled config files
| Config | Model | Hardware | Instances | Trace |
|--------|-------|----------|-----------|-------|
| `glm5-8xb200-hf.yaml` | GLM-5 via HF config.json | 8xB200 preset | 32 | GLM coder blk512 |
| `glm5-fp8-8xh20-141g.yaml` | GLM-5-FP8 via ModelScope config.json | 8xH20-141G preset | 128 | GLM coder blk512 |
| `glm5-nvfp4-8xb300.yaml` | GLM-5-NVFP4 via HF config.json | 8xB300 preset | 8 | GLM coder blk512 |
| `qwen3-coder-480b-8xh20.yaml` | Qwen3-Coder via HF | 8xH20 preset | 32 | Qwen coder blk16 |
## Outputs ## Outputs
Each run writes a directory under `sim.output_dir`: Each `run` writes a directory under `sim.output_dir`:
| File | Contents | | File | Contents |
|----------------------|----------------------------------------------------------------------------| |------|----------|
| `summary.json` | Router, throughput, TTFT p50/p95/p99, hit rates per tier, total RDMA/PCIe bytes | | `summary.json` | Aggregate throughput, TTFT/E2E percentiles, hit rates, RDMA bytes, PCIe bytes. |
| `per_request.csv` | `req_id,arrival,ttft,e2e,instance,total_blocks,l0_hit,l1_hit,remote_hit,miss,rdma_bytes,pcie_bytes,probe_overhead_s` | | `per_request.csv` | Per-request latency and cache stats, including `bucket`, `instance`, and `length_bucket_match`. |
| `instances.csv` | `t,instance,queue_len,kv_blocks_used,kv_blocks_total,busy` per sample | | `instances.csv` | Periodic per-instance samples with `bucket`, `instance`, `queue_len`, and KV usage. |
| `routing_log.jsonl` | One JSON per request: all router candidates + chosen instance + reason | | `routing_log.jsonl` | One JSON route decision per request, including `global_mode`, `mode`, `chosen_bucket`, candidate buckets, and candidate instances. |
For `ablate`: an extra `ablation.json` with one summary per router. Additional outputs:
For `oracle`: an `oracle.json` with the three hit-rate analyses.
### Reading results quickly - `ablate`: writes `ablation.json`
- `oracle`: writes `oracle.json`
- `ablate --auto-instances`: writes calibration runs under
`<output_dir>/auto_instances/`
Quick inspection examples:
```bash ```bash
# Pretty-print the summary jq . runs/glm5_8xb200/summary.json
cat runs/glm5_8xb200_hf/summary.json | jq .
# Compare all routers from an ablation
cat runs/glm5_8xb200_hf/ablation.json | \
jq '.[] | {router, ttft_mean, ttft_p50, hit_rate_l0, miss_rate}'
# Sort by TTFT
cat runs/glm5_8xb200_hf/ablation.json | \
jq 'sort_by(.ttft_mean) | .[] | {router, ttft_mean, hit_rate_l0}'
``` ```
## Trace format ```bash
jq 'sort_by(.ttft_mean) | .[] | {router, ttft_mean, hit_rate_l0, miss_rate}' \
runs/glm5_8xb200/ablation.json
```
The simulator reads the Alibaba ## Oracle Semantics
[`qwen-bailian-usagetraces-anon`](https://github.com/alibaba-edu/qwen-bailian-usagetraces-anon)
JSONL schema. Each record has `chat_id`, `timestamp`, `input_length`,
`output_length`, and `hash_ids` (block hashes, typically 16 tokens each).
Only the input side is used.
Available traces in the submodule: `oracle` computes three hit-rate references at a chosen cache capacity:
| Trace | Requests | Description | - `unlimited.hit_rate`: absolute ceiling with infinite capacity
|-------|----------|-------------| - `belady_finite.hit_rate`: offline-optimal eviction at the chosen capacity
| `qwen_coder_blksz_16.jsonl` | 43k | Qwen Coder serving traffic | - `lru_finite.hit_rate`: LRU at the same capacity
| `qwen_traceA_blksz_16.jsonl` | 43k | Qwen general traffic A |
| `qwen_traceB_blksz_16.jsonl` | 173k | Qwen general traffic B | When `sim.input_length_min` / `sim.input_length_max` are set, `oracle` still
| `qwen_thinking_blksz_16.jsonl` | 11k | Qwen reasoning/thinking traffic | feeds the full trace into cache state but only counts requests inside the
selected input-length range. That matches the intended "measure one bucket
inside a mixed workload" interpretation.
The gap from `lru_finite` to `belady_finite` is eviction-policy headroom. The
gap from `belady_finite` to `unlimited` is pure capacity headroom.
## Testing ## Testing
@@ -355,6 +388,5 @@ Available traces in the submodule:
cargo test --release cargo test --release
``` ```
28 tests: 27 unit tests (compute model, HF config parsing, hardware The test suite covers config parsing, hardware presets, routing behavior,
presets) + 1 integration smoke test that runs routers on a synthetic bucket-aware service semantics, oracle logic, and smoke-style end-to-end runs.
shared-prefix trace and asserts the expected hit-rate ordering.