# solvers.TailLatencyModel { #mlsysim.solvers.TailLatencyModel }

```python
solvers.TailLatencyModel()
```

Analyzes queueing delays and P99 tail latency for deployed inference models.

Models inference servers as M/M/c queues to determine if the deployment
can sustain the target arrival rate while meeting strict SLA latency bounds.

Literature Source:
1. Dean & Barroso (2013), "The Tail at Scale."

## Methods

| Name | Description |
| --- | --- |
| [solve](#mlsysim.solvers.TailLatencyModel.solve) | Solves for P50 and P99 tail latencies under variable load. |

### solve { #mlsysim.solvers.TailLatencyModel.solve }

```python
solvers.TailLatencyModel.solve(
    arrival_rate_qps,
    service_latency_ms,
    num_replicas=1,
    service_time_cv=1.0,
)
```

Solves for P50 and P99 tail latencies under variable load.

#### Parameters {.doc-section .doc-section-parameters}

| Name               | Type   | Description                                                                                                                                                                                     | Default    |
|--------------------|--------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------|
| arrival_rate_qps   | float  | Request arrival rate in queries per second.                                                                                                                                                     | _required_ |
| service_latency_ms | float  | Mean service time per request in milliseconds.                                                                                                                                                  | _required_ |
| num_replicas       | int    | Number of server replicas (c in M/M/c).                                                                                                                                                         | `1`        |
| service_time_cv    | float  | Coefficient of variation of service time (default 1.0 = exponential). When CV != 1, applies Kingman's M/G/1 correction factor (cv^2 + 1) / 2 to queue wait times, approximating M/G/c behavior. | `1.0`      |