test: add tests for replica calculation and planner scaling (#2525)

Signed-off-by: Hannah Zhang <hannahz@nvidia.com>

docs/architecture/sla_planner.md

@@ -115,4 +115,4 @@ kubectl apply -f disagg_planner.yaml -n {$NAMESPACE}
 ```
 
 > [!NOTE]
-> The SLA planner requires a frontend that reports metrics at `/metrics` HTTP endpoint with number of requests, ISL, OSL, TTFT, ITL in the correct format. The dynamo frontend provides these metrics automatically.
+> The SLA planner requires a frontend that reports metrics at the `/metrics` HTTP endpoint with the number of requests, ISL, OSL, TTFT, and ITL in the correct format. The dynamo frontend provides these metrics automatically.
\ No newline at end of file

tests/planner/.gitignore

+# E2E test results - don't commit test artifacts to git
+e2e_scaling_results/
+
+# Temporary files
+*.tmp
+*.log
+
+# Python cache
+__pycache__/
+*.pyc
+*.pyo
\ No newline at end of file

tests/planner/README.md

@@ -86,6 +86,7 @@ python benchmarks/sin_load_generator/sin_synth.py \
 
 The dataset starts at 12 requests/s, increases to 36 requests/s at t=300s, decreases back to 12 requests/s at t=600s, and repeats.
 The total duration is 30 minutes or 1800 seconds.
+
 ## Planner Dry Run
 
 Before testing SLA planner on real deployments, we provide a dry run feature to test the autoscaling behavior on a given dataset. Specifically, in dry run mode,
@@ -129,3 +130,75 @@ The second plot shows the actual ISL/OSL and the predicted ISL/OSL. The first tw
 The third plot shows the actual prefill throughput, number of prefill workers that planner scales, and the safe throughput limit with the number of prefill workers. If the actual throughput is below the safe throughput limit, the deployment has the capacity to adhere the TTFT SLA. Note that in the real deployment, due to other factors such as queueing, load balancing, KV cache transfer latency, and ISL variance, it is not guaranteed that the actual deployment can adhere the TTFT SLA.
 
 The fourth plot, similar to the third plot, shows the actual decode throughput, number of decode workers that planner scales, and the safe throughput limit with the number of decode workers. If the actual throughput is below the safe throughput limit, the deployment has the capacity to adhere the ITL SLA. Note that in the real deployment, due to other factors such as load balancing and OSL variance, it is not guaranteed that the actual deployment can adhere the ITL SLA.
+
+## Scaling Tests
+
+This directory contains comprehensive tests for validating the SLA planner's scaling behavior. The tests validate both the replica calculation logic and end-to-end scaling behavior. The scaling test uses a graduated load approach rather than dataset files, as it proved more reliable for metric generation and scaling triggers.
+
+### Test Types
+
+1. **Unit Tests** (`test_replica_calculation.py`) - Test the mathematical formulas for calculating prefill and decode replicas in isolation
+2. **End-to-End Tests** (`scaling/run_scaling_test.sh`) - Test complete workflow including Kubernetes deployment, load generation, and pod scaling validation
+
+### Quick Start
+
+#### Run Unit Tests Only
+Test the replica calculation logic without requiring Kubernetes:
+
+```bash
+# Set PYTHONPATH to include planner components
+PYTHONPATH=components/planner/src python -m pytest tests/planner/test_replica_calculation.py -v
+
+# Or from the tests/planner directory:
+cd tests/planner
+PYTHONPATH=../../components/planner/src python -m pytest test_replica_calculation.py -v
+```
+
+**Note**: The unit tests automatically mock external dependencies (prometheus_client, runtime modules) to ensure they can run in isolation without requiring the full Dynamo environment.
+
+#### Run Full End-to-End Test
+Test complete scaling behavior including Kubernetes deployment and load generation:
+
+```bash
+./scaling/run_scaling_test.sh
+```
+
+With custom namespace:
+```bash
+./scaling/run_scaling_test.sh --namespace production
+```
+
+To save results to `tests/planner/e2e_scaling_results` instead of `/tmp`:
+```bash
+./scaling/run_scaling_test.sh --save-results
+```
+
+**E2E Test Deployment Management:**
+- If no deployment exists: creates, tests, and cleans up deployment
+- If deployment exists: uses existing deployment and preserves it
+- Perfect for development workflows where you want to keep deployments running between tests
+
+**Test Scenario**
+
+The main test scenario validates prefill scaling for H200 with 1P1D → 2P1D configuration:
+
+- **Phase 1**: 8 req/s for 90s (baseline - maintains 1P1D)
+- **Phase 2**: 15 req/s for 120s (moderate load - maintains 1P1D)
+- **Phase 3**: 25 req/s for 180s (scaling trigger - scales to 2P1D)
+- **ISL/OSL**: 4000/150 tokens (optimized for prefill bottleneck)
+- **Transition delay**: 30s between phases
+- **Total test duration**: ~7 minutes + scaling observation
+- **Smart cleanup**: Only removes deployment if test created it (preserves existing deployments)
+
+### Prerequisites
+
+**For Unit Tests:**
+- Python dependencies installed
+- PYTHONPATH set to include `components/planner/src` (see unit test examples above)
+
+**For E2E Tests:**
+- Kubernetes cluster with GPU nodes
+- kubectl configured and accessible
+- genai-perf available in PATH
+- Python dependencies installed
+- PYTHONPATH properly configured for planner imports
\ No newline at end of file

tests/planner/conftest.py

+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Local conftest.py for planner tests to disable automatic test logging.
+This overrides the autouse logger fixture from the parent conftest.py.
+"""
+
+import pytest
+
+
+@pytest.fixture(autouse=True)
+def logger(request):
+    """Dummy logger fixture that does nothing - overrides the parent one."""
+    yield

tests/planner/scaling/disagg_planner.yaml

+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+apiVersion: nvidia.com/v1alpha1
+kind: DynamoGraphDeployment
+metadata:
+  name: vllm-disagg-planner
+  annotations:
+    nvidia.com/enable-grove: "false" # temporarily disable grove because current k8s connector does not work with grove
+spec:
+  envs:
+    - name: DYNAMO_SERVICE_CONFIG
+      value: '{"Prometheus":{"global":{"scrape_interval":"5s"},"scrape_configs":[{"job_name":"prometheus","static_configs":[{"targets":["localhost:9090"]}]},{"job_name":"frontend","static_configs":[{"targets":["vllm-disagg-planner-frontend:8000"]}]}]}}'
+    - name: DYNAMO_NAMESPACE
+      value: "vllm-disagg-planner"
+    - name: PROMETHEUS_PORT
+      value: "8000"
+  services:
+    Frontend:
+      dynamoNamespace: vllm-disagg-planner
+      componentType: frontend
+      replicas: 1
+      extraPodSpec:
+        mainContainer:
+          image: nvcr.io/nvidian/nim-llm-dev/vllm-runtime:dep-301.6
+          args:
+            - "python3 -m dynamo.frontend --http-port 8000"
+    Planner:
+      dynamoNamespace: vllm-disagg-planner
+      envFromSecret: hf-token-secret
+      componentType: planner
+      replicas: 1
+      livenessProbe:
+        httpGet:
+          path: /metrics
+          port: 9085
+        periodSeconds: 60
+        timeoutSeconds: 30
+        failureThreshold: 10
+      readinessProbe:
+        httpGet:
+          path: /metrics
+          port: 9085
+        initialDelaySeconds: 60
+        periodSeconds: 60
+        timeoutSeconds: 30
+        failureThreshold: 10
+      extraPodSpec:
+        mainContainer:
+          image: nvcr.io/nvidian/nim-llm-dev/vllm-runtime:dep-301.6
+          workingDir: /workspace/components/planner/src/dynamo/planner
+          ports:
+            - name: metrics
+              containerPort: 9085
+          command:
+            - /bin/sh
+            - -c
+          args:
+            - >-
+              python3 -m planner_sla
+              --environment=kubernetes
+              --backend=vllm
+              --adjustment-interval=60
+              --profile-results-dir=/workspace/tests/planner/profiling_results/H200_TP1P_TP1D
+              --prometheus-port=9085
+              --ttft=0.1
+              --itl=0.01
+              --load-predictor=constant
+    Prometheus: # NOTE: this is set on Prometheus to ensure a service is created for the Prometheus component. This is a workaround and should be managed differently.
+      dynamoNamespace: vllm-disagg-planner
+      componentType: prometheus
+      replicas: 1
+      envs:
+        - name: PYTHONPATH
+          value: "/workspace/components/planner/src"
+      livenessProbe:
+        httpGet:
+          path: /
+          port: 9090
+        periodSeconds: 60
+        timeoutSeconds: 30
+        failureThreshold: 10
+      readinessProbe:
+        httpGet:
+          path: /
+          port: 9090
+        initialDelaySeconds: 30
+        periodSeconds: 60
+        timeoutSeconds: 30
+        failureThreshold: 10
+      extraPodSpec:
+        mainContainer:
+          image: nvcr.io/nvidian/nim-llm-dev/vllm-runtime:dep-301.6
+          workingDir: /workspace/components/backends/vllm
+          command:
+            - /bin/sh
+            - -c
+          args:
+            - "python3 -m dynamo.planner.prometheus"
+    VllmDecodeWorker:
+      dynamoNamespace: vllm-disagg-planner
+      envFromSecret: hf-token-secret
+      componentType: worker
+      replicas: 1
+      resources:
+        limits:
+          gpu: "1"
+      extraPodSpec:
+        mainContainer:
+          startupProbe:
+            httpGet:
+              path: /health
+              port: 9090
+            periodSeconds: 30
+            failureThreshold: 60
+          image: nvcr.io/nvidian/nim-llm-dev/vllm-runtime:dep-301.6
+          workingDir: /workspace/components/backends/vllm
+          command:
+            - /bin/sh
+            - -c
+          args:
+            - "python3 -m dynamo.vllm --model nvidia/Llama-3.1-8B-Instruct-FP8 --migration-limit=3 --max-model-len=8192"
+    VllmPrefillWorker:
+      dynamoNamespace: vllm-disagg-planner
+      envFromSecret: hf-token-secret
+      componentType: worker
+      replicas: 1
+      resources:
+        limits:
+          gpu: "1"
+      extraPodSpec:
+        mainContainer:
+          startupProbe:
+            httpGet:
+              path: /health
+              port: 9090
+            periodSeconds: 30
+            failureThreshold: 60
+          image: nvcr.io/nvidian/nim-llm-dev/vllm-runtime:dep-301.6
+          workingDir: /workspace/components/backends/vllm
+          command:
+            - /bin/sh
+            - -c
+          args:
+            - python3 -m dynamo.vllm --model nvidia/Llama-3.1-8B-Instruct-FP8 --is-prefill-worker --migration-limit=3 --max-model-len=8192

tests/planner/scaling/run_scaling_test.sh

+#!/bin/bash
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+# Run SLA planner scaling end-to-end test
+# This script:
+# 1. Deploys the disaggregated planner if not already running
+# 2. Sets up port forwarding to localhost:8000
+# 3. Waits for the deployment to be ready
+# 4. Runs the hardcoded scaling test (12 req/s -> 24 req/s)
+# 5. Cleans up
+
+set -e
+
+# Configuration
+NAMESPACE=${NAMESPACE:-default}
+YAML_FILE="disagg_planner.yaml"
+FRONTEND_PORT=8000
+LOCAL_PORT=8000
+DEPLOYMENT_NAME="vllm-disagg-planner"
+SAVE_RESULTS=false
+
+# Colors for output
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+BLUE='\033[0;34m'
+NC='\033[0m' # No Color
+
+log_info() {
+    echo -e "${BLUE}[INFO]${NC} $1"
+}
+
+log_success() {
+    echo -e "${GREEN}[SUCCESS]${NC} $1"
+}
+
+log_warning() {
+    echo -e "${YELLOW}[WARNING]${NC} $1"
+}
+
+log_error() {
+    echo -e "${RED}[ERROR]${NC} $1"
+}
+
+# Check prerequisites
+check_prerequisites() {
+    log_info "Checking prerequisites..."
+
+    if ! command -v kubectl &> /dev/null; then
+        log_error "kubectl not found. Please install kubectl."
+        exit 1
+    fi
+
+    if ! command -v python &> /dev/null && ! command -v python3 &> /dev/null; then
+        log_error "Python not found. Please install Python."
+        exit 1
+    fi
+
+    if ! kubectl cluster-info &> /dev/null; then
+        log_error "Cannot connect to Kubernetes cluster."
+        exit 1
+    fi
+
+    if [ ! -f "test_scaling_e2e.py" ]; then
+        log_error "test_scaling_e2e.py not found. Make sure you're in the tests/planner directory."
+        exit 1
+    fi
+
+    # Check for genai-perf
+    if ! command -v genai-perf &> /dev/null; then
+        log_warning "genai-perf not found. This tool is required for load generation."
+        echo -n "Would you like us to install it for you? (y/n): "
+        read -r response
+        if [[ "$response" =~ ^[Yy]$ ]]; then
+            log_info "Installing genai-perf and perf_analyzer..."
+            # Install specific versions for reproducibility and security
+            if pip install 'nvidia-ml-py3>=12.0.0' 'genai-perf>=0.0.4' 'tritonclient[all]>=2.48.0'; then
+                log_success "genai-perf and perf_analyzer installed successfully"
+            else
+                log_error "Failed to install genai-perf. Please install it manually: pip install 'nvidia-ml-py3>=12.0.0' 'genai-perf>=0.0.4' 'tritonclient[all]>=2.48.0'"
+                exit 1
+            fi
+        else
+            log_error "genai-perf is required for the scaling test. Please install it: pip install 'nvidia-ml-py3>=12.0.0' 'genai-perf>=0.0.4' 'tritonclient[all]>=2.48.0'"
+            exit 1
+        fi
+    fi
+
+    log_success "Prerequisites check passed"
+}
+
+# Check if deployment already exists and is running
+check_existing_deployment() {
+    log_info "Checking for existing deployment..."
+
+    # Check for the DynamoGraphDeployment custom resource
+    if kubectl get dynamographdeployment "$DEPLOYMENT_NAME" -n "$NAMESPACE" &> /dev/null; then
+        log_info "DynamoGraphDeployment $DEPLOYMENT_NAME already exists - skipping redeployment"
+
+        # Check if the DynamoGraphDeployment is ready
+        local status
+        status=$(kubectl get dynamographdeployment "$DEPLOYMENT_NAME" -n "$NAMESPACE" -o jsonpath='{.status.state}')
+        if [ "$status" = "successful" ]; then
+            # Check if frontend pod is running
+            if kubectl get pods -n "$NAMESPACE" -l "nvidia.com/dynamo-component-type=frontend,nvidia.com/dynamo-namespace=vllm-disagg-planner" --field-selector=status.phase=Running | grep -q .; then
+                log_success "Existing deployment is ready"
+                return 0
+            else
+                log_warning "Existing deployment pods are not ready, will redeploy"
+                return 1
+            fi
+        else
+            log_warning "Existing deployment is not ready (status: $status), will redeploy"
+            return 1
+        fi
+    else
+        log_info "No existing deployment found"
+        return 1
+    fi
+}
+
+# Deploy the planner
+deploy_planner() {
+    log_info "Deploying SLA planner..."
+
+    if [ ! -f "$YAML_FILE" ]; then
+        log_error "Deployment file $YAML_FILE not found"
+        exit 1
+    fi
+
+    # Apply the deployment
+    if kubectl apply -f "$YAML_FILE" -n "$NAMESPACE"; then
+        log_success "Deployment applied successfully"
+    else
+        log_error "Failed to apply deployment"
+        exit 1
+    fi
+
+    log_info "Waiting for DynamoGraphDeployment to be processed..."
+    if kubectl wait --for=condition=Ready dynamographdeployment/"$DEPLOYMENT_NAME" -n "$NAMESPACE" --timeout=600s; then
+        log_success "DynamoGraphDeployment is ready"
+    else
+        log_error "DynamoGraphDeployment failed to become ready within timeout"
+        exit 1
+    fi
+
+    log_info "Waiting for pods to be running (this may take several minutes for image pulls)..."
+
+    log_info "Waiting for frontend pod..."
+    if kubectl wait --for=condition=Ready pod -l "nvidia.com/dynamo-component-type=frontend,nvidia.com/dynamo-namespace=vllm-disagg-planner" -n "$NAMESPACE" --timeout=900s; then
+        log_success "Frontend pod is ready"
+    else
+        log_error "Frontend pod failed to become ready within timeout"
+        exit 1
+    fi
+
+    log_info "Waiting for all pods to be running..."
+    sleep 30
+}
+
+setup_port_forward() {
+    log_info "Setting up port forwarding..."
+
+    # Kill any existing port forward on the same port
+    if lsof -ti:$LOCAL_PORT &> /dev/null; then
+        log_warning "Port $LOCAL_PORT is already in use, attempting to free it..."
+        kill "$(lsof -ti:$LOCAL_PORT)" 2>/dev/null || true
+        sleep 2
+    fi
+
+    local frontend_service="vllm-disagg-planner-frontend"
+
+    if ! kubectl get service "$frontend_service" -n "$NAMESPACE" &> /dev/null; then
+        log_error "Frontend service '$frontend_service' not found"
+        return 1
+    fi
+
+    log_info "Port forwarding to service: $frontend_service"
+    kubectl port-forward service/"$frontend_service" "$LOCAL_PORT:$FRONTEND_PORT" -n "$NAMESPACE" >/dev/null 2>&1 &
+    PORT_FORWARD_PID=$!
+
+    log_info "Waiting for port forwarding to be established..."
+    for i in {1..30}; do
+        if curl -s http://localhost:$LOCAL_PORT/health &> /dev/null; then
+            log_success "Port forwarding established and service is healthy"
+            return 0
+        fi
+        sleep 2
+    done
+
+    log_error "Failed to establish port forwarding or service is not healthy"
+    return 1
+}
+
+cleanup_port_forward() {
+    if [ ! -z "$PORT_FORWARD_PID" ]; then
+        log_info "Cleaning up port forwarding..."
+        kill $PORT_FORWARD_PID 2>/dev/null || true
+        wait $PORT_FORWARD_PID 2>/dev/null || true
+    fi
+}
+
+cleanup_deployment() {
+    log_info "Cleaning up deployment..."
+    kubectl delete -f "$YAML_FILE" -n "$NAMESPACE" --ignore-not-found
+
+    log_info "Waiting for cleanup to complete..."
+    kubectl wait --for=delete dynamographdeployment/"$DEPLOYMENT_NAME" -n "$NAMESPACE" --timeout=120s || true
+
+    log_info "Cleanup complete"
+}
+
+run_test() {
+    log_info "Running scaling test (graduated 8->15->25 req/s)..."
+
+    local python_cmd="python3"
+    if ! command -v python3 &> /dev/null; then
+        python_cmd="python"
+    fi
+
+    local test_args="--namespace $NAMESPACE"
+    if [ "$SAVE_RESULTS" = true ]; then
+        test_args="$test_args --save-results"
+        log_info "Results will be saved to tests/planner/e2e_scaling_results"
+    fi
+
+    if $python_cmd test_scaling_e2e.py $test_args; then
+        log_success "Scaling test PASSED"
+        return 0
+    else
+        log_error "Scaling test FAILED"
+        return 1
+    fi
+}
+
+main() {
+    while [[ $# -gt 0 ]]; do
+        case $1 in
+            --namespace)
+                NAMESPACE="$2"
+                shift 2
+                ;;
+            --save-results)
+                SAVE_RESULTS=true
+                shift
+                ;;
+            --help)
+                echo "Usage: $0 [--namespace NS] [--save-results]"
+                echo ""
+                echo "Run SLA planner scaling test (graduated 8->15->25 req/s prefill scaling)"
+                echo ""
+                echo "Options:"
+                echo "  --namespace NS    Kubernetes namespace (default: default)"
+                echo "  --save-results    Save results to tests/planner/e2e_scaling_results instead of /tmp"
+                echo "  --help            Show this help"
+                exit 0
+                ;;
+            *)
+                log_error "Unknown option: $1"
+                echo "Use --help for usage information"
+                exit 1
+                ;;
+        esac
+    done
+
+    log_info "SLA Planner Scaling Test"
+    log_info "Namespace: $NAMESPACE"
+    log_info "Scenario: Graduated 8->15->25 req/s (1P1D -> 2P1D prefill scaling, ISL=4000/OSL=150)"
+
+    check_prerequisites
+
+    trap cleanup_port_forward EXIT
+
+    # Check if we need to deploy
+    local deployed_by_us=false
+    if ! check_existing_deployment; then
+        deploy_planner
+        deployed_by_us=true
+    fi
+
+    if ! setup_port_forward; then
+        log_error "Failed to setup port forwarding"
+        exit 1
+    fi
+
+    local test_result=0
+    if ! run_test; then
+        test_result=1
+    fi
+
+    # Only cleanup deployment if we deployed it
+    if [ "$deployed_by_us" = true ]; then
+        cleanup_deployment
+    fi
+
+    if [ $test_result -eq 0 ]; then
+        log_success "Test completed successfully!"
+    else
+        log_error "Test failed!"
+    fi
+
+    exit $test_result
+}
+
+main "$@"
\ No newline at end of file

tests/planner/test_replica_calculation.py

+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Unit tests for SLA planner replica calculation logic.
+
+These tests focus specifically on the replica calculation formulas without
+testing load prediction, interpolation, or correction factors.
+"""
+
+import argparse
+import math
+import os
+
+# Mock dependencies before importing planner modules
+import sys
+
+# We'll import the actual Planner class to test its calculation logic
+from unittest.mock import MagicMock, Mock, patch
+
+import pytest
+
+# Create mock modules for dependencies that might not be available in test environment
+mock_prometheus = MagicMock()
+mock_prometheus.Gauge = MagicMock()
+mock_prometheus.start_http_server = MagicMock()
+
+mock_runtime = MagicMock()
+mock_runtime.logging = MagicMock()
+mock_runtime.logging.configure_dynamo_logging = MagicMock()
+
+# Patch them into sys.modules before importing
+sys.modules["prometheus_client"] = mock_prometheus
+sys.modules["dynamo.runtime"] = mock_runtime
+sys.modules["dynamo.runtime.logging"] = mock_runtime.logging
+
+# Now import after mocking
+from dynamo.planner.utils.planner_core import Metrics, Planner  # noqa: E402
+
+
+@pytest.fixture
+def planner():
+    """Set up test environment with mocked dependencies."""
+    # Create mock arguments
+    args = argparse.Namespace()
+    args.adjustment_interval = 60
+    args.prefill_engine_num_gpu = 1
+    args.decode_engine_num_gpu = 1
+    args.min_endpoint = 1
+    args.max_gpu_budget = 10
+    args.ttft = 80  # ms
+    args.itl = 10  # ms
+    args.backend = "vllm"
+    args.no_operation = True  # Don't actually scale
+    args.prometheus_port = 0  # 0 means disabled
+    args.load_predictor = "constant"
+    args.load_prediction_window_size = 10
+    args.profile_results_dir = os.path.join(
+        os.path.dirname(__file__),
+        "profiling_results/H200_TP1P_TP1D",
+    )
+    args.environment = "kubernetes"
+
+    # Mock the runtime
+    mock_runtime = Mock()
+
+    # Patch Prometheus Gauge to avoid registry conflicts
+    with patch("dynamo.planner.utils.planner_core.Gauge") as mock_gauge:
+        mock_gauge.return_value = Mock()
+
+        # Create planner instance
+        planner = Planner(mock_runtime, args)
+
+        # Mock the interpolators to return fixed values for testing
+        planner.prefill_interpolator = Mock()
+        planner.decode_interpolator = Mock()
+
+        # Mock the predictors to return fixed values
+        planner.num_req_predictor = Mock()
+        planner.isl_predictor = Mock()
+        planner.osl_predictor = Mock()
+
+        # Mock the connector since we're not testing actual scaling
+        planner.connector = Mock()
+
+        # Mock prometheus client
+        planner.prometheus_api_client = Mock()
+
+        # Set up some baseline correction factors
+        planner.p_correction_factor = 1.0
+        planner.d_correction_factor = 1.0
+
+        # Store args for easy access in tests
+        planner.args = args
+
+        yield planner
+        # Cleanup is automatic with context manager
+
+
+class TestReplicaCalculation:
+    """Test replica calculation formulas in isolation."""
+
+    def test_prefill_replica_calculation_basic(self, planner):
+        """Test basic prefill replica calculation."""
+        # Setup test data
+        next_num_req = 10
+        next_isl = 3000
+        prefill_thpt_per_gpu = 40000  # tokens/s/gpu (from the test data)
+
+        # Mock the predictor outputs
+        planner.num_req_predictor.predict_next.return_value = next_num_req
+        planner.isl_predictor.predict_next.return_value = next_isl
+        planner.osl_predictor.predict_next.return_value = 150
+
+        # Mock interpolator output
+        planner.prefill_interpolator.interpolate_thpt_per_gpu.return_value = (
+            prefill_thpt_per_gpu
+        )
+        planner.decode_interpolator.find_best_throughput_per_gpu.return_value = (
+            10000,
+            0.01,
+            0.5,
+        )
+
+        # Calculate expected result manually
+        pred_prefill_load_per_gpu = (
+            next_num_req
+            * next_isl
+            / planner.args.adjustment_interval
+            * min(1, planner.p_correction_factor)
+        )
+        expected_prefill_replicas = math.ceil(
+            pred_prefill_load_per_gpu
+            / prefill_thpt_per_gpu
+            / planner.args.prefill_engine_num_gpu
+        )
+
+        # Set up valid metrics to trigger calculation
+        planner.last_metrics = Metrics(
+            num_req=10, isl=3000, osl=150, ttft=80.0, itl=10.0, request_duration=100.0
+        )
+
+        # Mock workers info
+        async def mock_get_workers_info():
+            return (["prefill1"], ["decode1"])
+
+        planner.get_workers_info = mock_get_workers_info
+
+        # Mock interpolation calls for correction factor calculation
+        planner.prefill_interpolator.interpolate_ttft.return_value = 80.0
+        planner.decode_interpolator.interpolate_itl.return_value = 10.0
+
+        # Run the calculation
+        import asyncio
+
+        asyncio.run(planner.make_adjustments())
+
+        # Extract the calculated values from the log calls or by checking the mock calls
+        # Since we mocked the connector, we can check what replicas were requested
+        if planner.connector.set_component_replicas.called:
+            call_args = planner.connector.set_component_replicas.call_args[0][0]
+            prefill_component = "VllmPrefillWorker"
+            calculated_prefill_replicas = call_args.get(prefill_component, 1)
+
+            print(f"Expected prefill replicas: {expected_prefill_replicas}")
+            print(f"Calculated prefill replicas: {calculated_prefill_replicas}")
+
+            # Allow for small differences due to min_endpoint constraints
+            assert (
+                max(expected_prefill_replicas, planner.args.min_endpoint)
+                == calculated_prefill_replicas
+            )
+
+    def test_decode_replica_calculation_basic(self, planner):
+        """Test basic decode replica calculation."""
+        # Setup test data
+        next_num_req = 10
+        next_osl = 150
+        decode_thpt_per_gpu = 10000  # tokens/s/gpu
+
+        # Mock the predictor outputs
+        planner.num_req_predictor.predict_next.return_value = next_num_req
+        planner.isl_predictor.predict_next.return_value = 3000
+        planner.osl_predictor.predict_next.return_value = next_osl
+
+        # Mock interpolator outputs
+        planner.prefill_interpolator.interpolate_thpt_per_gpu.return_value = 40000
+        planner.decode_interpolator.find_best_throughput_per_gpu.return_value = (
+            decode_thpt_per_gpu,
+            0.01,
+            0.5,
+        )
+
+        # Calculate expected result manually
+        expected_decode_replicas = math.ceil(
+            next_num_req
+            * next_osl
+            / planner.args.adjustment_interval
+            / decode_thpt_per_gpu
+            / planner.args.decode_engine_num_gpu
+        )
+
+        # Set up valid metrics
+        planner.last_metrics = Metrics(
+            num_req=10, isl=3000, osl=150, ttft=80.0, itl=10.0, request_duration=100.0
+        )
+
+        # Mock workers info
+        async def mock_get_workers_info():
+            return (["prefill1"], ["decode1"])
+
+        planner.get_workers_info = mock_get_workers_info
+
+        # Mock interpolation calls for correction factor calculation
+        planner.prefill_interpolator.interpolate_ttft.return_value = 80.0
+        planner.decode_interpolator.interpolate_itl.return_value = 10.0
+
+        # Run the calculation
+        import asyncio
+
+        asyncio.run(planner.make_adjustments())
+
+        # Check the results
+        if planner.connector.set_component_replicas.called:
+            call_args = planner.connector.set_component_replicas.call_args[0][0]
+            decode_component = "VllmDecodeWorker"
+            calculated_decode_replicas = call_args.get(decode_component, 1)
+
+            print(f"Expected decode replicas: {expected_decode_replicas}")
+            print(f"Calculated decode replicas: {calculated_decode_replicas}")
+
+            # Allow for small differences due to min_endpoint constraints
+            assert (
+                max(expected_decode_replicas, planner.args.min_endpoint)
+                == calculated_decode_replicas
+            )
+
+    @pytest.mark.parametrize(
+        "num_req,decode_thpt,expected_p,expected_d",
+        [
+            (10, 10000, 1, 1),  # low_load_10_req_per_second
+            (500, 1000, 1, 2),  # high_load_500_req_per_second (lower decode throughput)
+        ],
+    )
+    def test_scaling_scenario_low_to_high_load(
+        self, planner, num_req, decode_thpt, expected_p, expected_d
+    ):
+        """Test scaling from low to high load scenarios."""
+        # Reset the planner state
+        planner.p_correction_factor = 1.0
+        planner.d_correction_factor = 1.0
+
+        # Mock predictor outputs for this case
+        planner.num_req_predictor.predict_next.return_value = num_req
+        planner.isl_predictor.predict_next.return_value = 3000
+        planner.osl_predictor.predict_next.return_value = 150
+
+        # Mock interpolator outputs (based on H200 1P1D profiling data)
+        planner.prefill_interpolator.interpolate_thpt_per_gpu.return_value = (
+            40000  # tokens/s/gpu
+        )
+        planner.decode_interpolator.find_best_throughput_per_gpu.return_value = (
+            decode_thpt,
+            0.01,
+            0.5,
+        )
+
+        # Set up metrics
+        planner.last_metrics = Metrics(
+            num_req=num_req,
+            isl=3000,
+            osl=150,
+            ttft=80.0,
+            itl=10.0,
+            request_duration=100.0,
+        )
+
+        # Mock workers info
+        async def mock_get_workers_info():
+            return (["prefill1"], ["decode1"])
+
+        planner.get_workers_info = mock_get_workers_info
+
+        # Mock interpolation calls for correction factor calculation
+        planner.prefill_interpolator.interpolate_ttft.return_value = 80.0
+        planner.decode_interpolator.interpolate_itl.return_value = 10.0
+
+        # Reset the mock
+        planner.connector.reset_mock()
+
+        # Run calculation
+        import asyncio
+
+        asyncio.run(planner.make_adjustments())
+
+        # Verify results
+        if planner.connector.set_component_replicas.called:
+            call_args = planner.connector.set_component_replicas.call_args[0][0]
+
+            prefill_replicas = call_args.get("VllmPrefillWorker", 1)
+            decode_replicas = call_args.get("VllmDecodeWorker", 1)
+
+            print(f"Load {num_req} req/s: P={prefill_replicas}, D={decode_replicas}")
+
+            assert (
+                prefill_replicas == expected_p
+            ), f"Prefill replicas mismatch: expected {expected_p}, got {prefill_replicas}"
+            assert (
+                decode_replicas == expected_d
+            ), f"Decode replicas mismatch: expected {expected_d}, got {decode_replicas}"
+
+    def test_gpu_budget_constraint(self, planner):
+        """Test that GPU budget constraints are properly applied."""
+        # Set a low GPU budget
+        planner.args.max_gpu_budget = 3
+
+        # Mock predictor outputs that would normally require more GPUs
+        planner.num_req_predictor.predict_next.return_value = 50  # High load
+        planner.isl_predictor.predict_next.return_value = 3000
+        planner.osl_predictor.predict_next.return_value = 150
+
+        # Mock interpolator outputs
+        planner.prefill_interpolator.interpolate_thpt_per_gpu.return_value = 40000
+        planner.decode_interpolator.find_best_throughput_per_gpu.return_value = (
+            10000,
+            0.01,
+            0.5,
+        )
+
+        # Set up metrics
+        planner.last_metrics = Metrics(
+            num_req=50, isl=3000, osl=150, ttft=80.0, itl=10.0, request_duration=100.0
+        )
+
+        # Mock workers info
+        async def mock_get_workers_info():
+            return (["prefill1"], ["decode1"])
+
+        planner.get_workers_info = mock_get_workers_info
+
+        # Mock interpolation calls
+        planner.prefill_interpolator.interpolate_ttft.return_value = 80.0
+        planner.decode_interpolator.interpolate_itl.return_value = 10.0
+
+        # Run calculation
+        import asyncio
+
+        asyncio.run(planner.make_adjustments())
+
+        # Verify that total GPU usage doesn't exceed budget
+        if planner.connector.set_component_replicas.called:
+            call_args = planner.connector.set_component_replicas.call_args[0][0]
+
+            prefill_replicas = call_args.get("VllmPrefillWorker", 1)
+            decode_replicas = call_args.get("VllmDecodeWorker", 1)
+
+            total_gpus = (
+                prefill_replicas * planner.args.prefill_engine_num_gpu
+                + decode_replicas * planner.args.decode_engine_num_gpu
+            )
+
+            print(
+                f"GPU budget test: P={prefill_replicas}, D={decode_replicas}, Total GPUs={total_gpus}"
+            )
+
+            assert (
+                total_gpus <= planner.args.max_gpu_budget
+            ), "Total GPU usage exceeds budget"
+
+    def test_min_endpoint_constraint(self, planner):
+        """Test that minimum endpoint constraints are respected."""
+        planner.args.min_endpoint = 2
+
+        # Mock predictor outputs that would normally require fewer workers
+        planner.num_req_predictor.predict_next.return_value = 1  # Very low load
+        planner.isl_predictor.predict_next.return_value = 100
+        planner.osl_predictor.predict_next.return_value = 10
+
+        # Mock interpolator outputs
+        planner.prefill_interpolator.interpolate_thpt_per_gpu.return_value = 40000
+        planner.decode_interpolator.find_best_throughput_per_gpu.return_value = (
+            10000,
+            0.01,
+            0.5,
+        )
+
+        # Set up metrics
+        planner.last_metrics = Metrics(
+            num_req=1, isl=100, osl=10, ttft=80.0, itl=10.0, request_duration=100.0
+        )
+
+        # Mock workers info
+        async def mock_get_workers_info():
+            return (["prefill1"], ["decode1"])
+
+        planner.get_workers_info = mock_get_workers_info
+
+        # Mock interpolation calls
+        planner.prefill_interpolator.interpolate_ttft.return_value = 80.0
+        planner.decode_interpolator.interpolate_itl.return_value = 10.0
+
+        # Run calculation
+        import asyncio
+
+        asyncio.run(planner.make_adjustments())
+
+        # Verify minimum constraints are respected
+        if planner.connector.set_component_replicas.called:
+            call_args = planner.connector.set_component_replicas.call_args[0][0]
+
+            prefill_replicas = call_args.get("VllmPrefillWorker", 1)
+            decode_replicas = call_args.get("VllmDecodeWorker", 1)
+
+            print(f"Min endpoint test: P={prefill_replicas}, D={decode_replicas}")
+
+            assert (
+                prefill_replicas >= planner.args.min_endpoint
+            ), "Prefill replicas below minimum"
+            assert (
+                decode_replicas >= planner.args.min_endpoint
+            ), "Decode replicas below minimum"
+
+    def test_prefill_correction_factor_clamping(self, planner):
+        """Test that prefill correction factor > 1 is clamped to 1."""
+        # Set a high correction factor > 1
+        planner.p_correction_factor = 2.5
+        planner.d_correction_factor = 1.0
+
+        # Mock predictor outputs
+        planner.num_req_predictor.predict_next.return_value = 10
+        planner.isl_predictor.predict_next.return_value = 3000
+        planner.osl_predictor.predict_next.return_value = 150
+
+        # Mock interpolator outputs
+        planner.prefill_interpolator.interpolate_thpt_per_gpu.return_value = 40000
+        planner.decode_interpolator.find_best_throughput_per_gpu.return_value = (
+            10000,
+            0.01,
+            0.5,
+        )
+
+        # Set up metrics
+        planner.last_metrics = Metrics(
+            num_req=10, isl=3000, osl=150, ttft=80.0, itl=10.0, request_duration=100.0
+        )
+
+        # Mock workers info
+        async def mock_get_workers_info():
+            return (["prefill1"], ["decode1"])
+
+        planner.get_workers_info = mock_get_workers_info
+
+        # Mock interpolation calls
+        planner.prefill_interpolator.interpolate_ttft.return_value = 80.0
+        planner.decode_interpolator.interpolate_itl.return_value = 10.0
+
+        # Calculate expected result manually with clamping
+        # Should use min(1, 2.5) = 1
+        pred_prefill_load_per_gpu = (
+            10 * 3000 / planner.args.adjustment_interval * min(1, 2.5)  # Should be * 1
+        )
+        expected_prefill_replicas = math.ceil(
+            pred_prefill_load_per_gpu / 40000 / planner.args.prefill_engine_num_gpu
+        )
+
+        # Run calculation
+        import asyncio
+
+        asyncio.run(planner.make_adjustments())
+
+        # Verify that correction factor was effectively clamped
+        if planner.connector.set_component_replicas.called:
+            call_args = planner.connector.set_component_replicas.call_args[0][0]
+            prefill_replicas = call_args.get("VllmPrefillWorker", 1)
+
+            print(
+                f"Correction factor clamping test: Expected={expected_prefill_replicas}, Got={prefill_replicas}"
+            )
+
+            assert prefill_replicas == max(
+                expected_prefill_replicas, planner.args.min_endpoint
+            ), "Prefill correction factor should be clamped to 1"
+
+    def test_decode_correction_factor_zero_handling(self, planner):
+        """Test handling of d_correction_factor <= 0."""
+        # Test both 0 and negative values
+        for correction_factor in [0.0, -1.0]:
+            with patch.object(planner, "connector") as mock_connector:
+                planner.p_correction_factor = 1.0
+                planner.d_correction_factor = correction_factor
+
+                # Mock predictor outputs
+                planner.num_req_predictor.predict_next.return_value = 10
+                planner.isl_predictor.predict_next.return_value = 3000
+                planner.osl_predictor.predict_next.return_value = 150
+
+                # Mock interpolator outputs
+                planner.prefill_interpolator.interpolate_thpt_per_gpu.return_value = (
+                    40000
+                )
+                planner.decode_interpolator.find_best_throughput_per_gpu.return_value = (
+                    10000,
+                    0.01,
+                    0.5,
+                )
+
+                # Set up metrics
+                planner.last_metrics = Metrics(
+                    num_req=10,
+                    isl=3000,
+                    osl=150,
+                    ttft=80.0,
+                    itl=10.0,
+                    request_duration=100.0,
+                )
+
+                # Mock workers info
+                async def mock_get_workers_info():
+                    return (["prefill1"], ["decode1"])
+
+                planner.get_workers_info = mock_get_workers_info
+
+                # Mock interpolation calls
+                planner.prefill_interpolator.interpolate_ttft.return_value = 80.0
+                planner.decode_interpolator.interpolate_itl.return_value = 10.0
+
+                # Run calculation
+                import asyncio
+
+                asyncio.run(planner.make_adjustments())
+
+                # Should handle gracefully without crashing
+                # The code should use args.itl directly instead of dividing by 0
+                if mock_connector.set_component_replicas.called:
+                    call_args = mock_connector.set_component_replicas.call_args[0][0]
+                    decode_replicas = call_args.get("VllmDecodeWorker", 1)
+
+                    print(
+                        f"Correction factor {correction_factor} test: Decode replicas={decode_replicas}"
+                    )
+
+                    # Should get a valid result (not crash)
+                    assert (
+                        decode_replicas >= 1
+                    ), f"Should handle correction factor {correction_factor} gracefully"
+
+    def test_multi_gpu_engines(self, planner):
+        """Test replica calculation with multi-GPU engines."""
+        # Set multi-GPU configuration
+        planner.args.prefill_engine_num_gpu = 2
+        planner.args.decode_engine_num_gpu = 4
+
+        # Mock predictor outputs
+        planner.num_req_predictor.predict_next.return_value = 20
+        planner.isl_predictor.predict_next.return_value = 3000
+        planner.osl_predictor.predict_next.return_value = 150
+
+        # Mock interpolator outputs
+        planner.prefill_interpolator.interpolate_thpt_per_gpu.return_value = 40000
+        planner.decode_interpolator.find_best_throughput_per_gpu.return_value = (
+            5000,
+            0.01,
+            0.5,
+        )  # Lower for scaling
+
+        # Set up metrics
+        planner.last_metrics = Metrics(
+            num_req=20, isl=3000, osl=150, ttft=80.0, itl=10.0, request_duration=100.0
+        )
+
+        # Mock workers info
+        async def mock_get_workers_info():
+            return (["prefill1"], ["decode1"])
+
+        planner.get_workers_info = mock_get_workers_info
+
+        # Mock interpolation calls
+        planner.prefill_interpolator.interpolate_ttft.return_value = 80.0
+        planner.decode_interpolator.interpolate_itl.return_value = 10.0
+
+        # Calculate expected results manually
+        pred_prefill_load_per_gpu = 20 * 3000 / planner.args.adjustment_interval * 1.0
+        expected_prefill_replicas = math.ceil(
+            pred_prefill_load_per_gpu / 40000 / 2
+        )  # 2 GPUs per engine
+
+        expected_decode_replicas = math.ceil(
+            20 * 150 / planner.args.adjustment_interval / 5000 / 4
+        )  # 4 GPUs per engine
+
+        # Run calculation
+        import asyncio
+
+        asyncio.run(planner.make_adjustments())
+
+        if planner.connector.set_component_replicas.called:
+            call_args = planner.connector.set_component_replicas.call_args[0][0]
+
+            prefill_replicas = call_args.get("VllmPrefillWorker", 1)
+            decode_replicas = call_args.get("VllmDecodeWorker", 1)
+
+            print(
+                f"Multi-GPU test: P={prefill_replicas} (expected ~{expected_prefill_replicas}), D={decode_replicas} (expected ~{expected_decode_replicas})"
+            )
+
+            # Verify calculations account for multiple GPUs per engine
+            assert prefill_replicas == max(
+                expected_prefill_replicas, planner.args.min_endpoint
+            )
+            assert decode_replicas == max(
+                expected_decode_replicas, planner.args.min_endpoint
+            )
+
+    def test_complex_gpu_budget_scaling(self, planner):
+        """Test complex GPU budget scaling with proportional reduction and decode adjustment."""
+        # Set tight GPU budget that will trigger complex scaling
+        planner.args.max_gpu_budget = 5
+        planner.args.prefill_engine_num_gpu = 2
+        planner.args.decode_engine_num_gpu = 2
+        planner.args.min_endpoint = 1
+
+        # High load that would normally require more GPUs
+        planner.num_req_predictor.predict_next.return_value = 100
+        planner.isl_predictor.predict_next.return_value = 3000
+        planner.osl_predictor.predict_next.return_value = 150
+
+        # Lower throughput to trigger higher replica needs
+        planner.prefill_interpolator.interpolate_thpt_per_gpu.return_value = 10000
+        planner.decode_interpolator.find_best_throughput_per_gpu.return_value = (
+            1000,
+            0.01,
+            0.5,
+        )
+
+        # Set up metrics
+        planner.last_metrics = Metrics(
+            num_req=100, isl=3000, osl=150, ttft=80.0, itl=10.0, request_duration=100.0
+        )
+
+        # Mock workers info
+        async def mock_get_workers_info():
+            return (["prefill1"], ["decode1"])
+
+        planner.get_workers_info = mock_get_workers_info
+
+        # Mock interpolation calls
+        planner.prefill_interpolator.interpolate_ttft.return_value = 80.0
+        planner.decode_interpolator.interpolate_itl.return_value = 10.0
+
+        # Run calculation
+        import asyncio
+
+        asyncio.run(planner.make_adjustments())
+
+        if planner.connector.set_component_replicas.called:
+            call_args = planner.connector.set_component_replicas.call_args[0][0]
+
+            prefill_replicas = call_args.get("VllmPrefillWorker", 1)
+            decode_replicas = call_args.get("VllmDecodeWorker", 1)
+
+            # Verify total GPU usage doesn't exceed budget
+            total_gpus = (
+                prefill_replicas * planner.args.prefill_engine_num_gpu
+                + decode_replicas * planner.args.decode_engine_num_gpu
+            )
+
+            print(
+                f"Complex GPU budget test: P={prefill_replicas}, D={decode_replicas}, Total GPUs={total_gpus}"
+            )
+
+            assert (
+                total_gpus <= planner.args.max_gpu_budget
+            ), "Total GPU usage should not exceed budget"
+            assert (
+                prefill_replicas >= planner.args.min_endpoint
+            ), "Should respect min_endpoint for prefill"
+            assert (
+                decode_replicas >= planner.args.min_endpoint
+            ), "Should respect min_endpoint for decode"
+
+
+# No need for unittest.main() with pytest!

tests/planner/test_scaling_e2e.py

+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+End-to-end test for SLA planner scaling behavior.
+
+This test assumes a disaggregated planner deployment is already running
+and accessible at localhost:8000. It monitors pod scaling and validates
+that the planner correctly scales from 1P1D to 2P1D when load increases
+through graduated phases: 8 req/s (baseline) → 15 req/s (moderate) → 25 req/s (prefill scaling trigger).
+"""
+
+import asyncio
+import json
+import logging
+import subprocess
+import time
+import urllib.request
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple
+
+from utils.load_generator import LoadGenerator
+
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+# Test configuration constants
+HEALTH_CHECK_TIMEOUT = 10
+PORT_FORWARD_SETUP_DELAY = 3
+FINAL_STABILIZATION_DELAY = 60
+MONITORING_INTERVAL = 15
+BUFFER_DURATION = 90
+
+
+@dataclass
+class PodCounts:
+    """Track pod counts at a specific time."""
+
+    timestamp: float
+    prefill_pods: int
+    decode_pods: int
+    total_pods: int
+
+    def __str__(self):
+        return f"P={self.prefill_pods}, D={self.decode_pods}, Total={self.total_pods}"
+
+
+class KubernetesMonitor:
+    """Monitor Kubernetes deployment and pod scaling."""
+
+    def __init__(
+        self, namespace: str = "default", deployment_name: str = "vllm-disagg-planner"
+    ):
+        self.namespace = namespace
+        self.deployment_name = deployment_name
+        self.pod_history: List[PodCounts] = []
+
+    def _run_kubectl(self, cmd: List[str]) -> Tuple[bool, str]:
+        """Run kubectl command and return success status and output."""
+        try:
+            result = subprocess.run(cmd, capture_output=True, text=True, timeout=30)
+            return result.returncode == 0, result.stdout.strip()
+        except subprocess.TimeoutExpired:
+            logger.error(f"kubectl command timed out: {' '.join(cmd)}")
+            return False, ""
+        except Exception as e:
+            logger.error(f"kubectl command failed: {e}")
+            return False, ""
+
+    def get_pod_counts(self) -> Optional[PodCounts]:
+        """Get current pod counts for prefill and decode workers."""
+        cmd = [
+            "kubectl",
+            "get",
+            "pods",
+            "-n",
+            self.namespace,
+            "--selector",
+            f"nvidia.com/dynamo-namespace={self.deployment_name}",
+            "-o",
+            "json",
+        ]
+
+        success, output = self._run_kubectl(cmd)
+        if not success:
+            logger.warning("Failed to get pod counts")
+            return None
+
+        try:
+            data = json.loads(output)
+            prefill_pods = 0
+            decode_pods = 0
+            total_pods = 0
+
+            for pod in data.get("items", []):
+                pod_phase = pod.get("status", {}).get("phase", "")
+                pod_labels = pod.get("metadata", {}).get("labels", {})
+                component = pod_labels.get("nvidia.com/dynamo-component", "")
+
+                # Only count Running pods
+                if pod_phase == "Running":
+                    if component == "VllmPrefillWorker":
+                        prefill_pods += 1
+                    elif component == "VllmDecodeWorker":
+                        decode_pods += 1
+                    else:
+                        continue
+                    total_pods += 1
+
+            counts = PodCounts(
+                timestamp=time.time(),
+                prefill_pods=prefill_pods,
+                decode_pods=decode_pods,
+                total_pods=total_pods,
+            )
+
+            self.pod_history.append(counts)
+            return counts
+
+        except Exception as e:
+            logger.error(f"Failed to parse pod counts: {e}")
+            return None
+
+    async def monitor_scaling(
+        self, duration: int, interval: int = 10
+    ) -> List[PodCounts]:
+        """Monitor pod scaling for a given duration."""
+        logger.info(f"Monitoring pod scaling for {duration}s (interval: {interval}s)")
+
+        start_time = time.time()
+        monitoring_data = []
+
+        while time.time() - start_time < duration:
+            counts = self.get_pod_counts()
+            if counts:
+                monitoring_data.append(counts)
+                logger.info(f"Pod counts: {counts}")
+
+            await asyncio.sleep(interval)
+
+        return monitoring_data
+
+    def wait_for_deployment_ready(self, timeout: int = 300) -> bool:
+        """Wait for deployment to be ready."""
+        logger.info(f"Waiting for deployment {self.deployment_name} to be ready...")
+
+        cmd = [
+            "kubectl",
+            "wait",
+            "--for=condition=available",
+            f"deployment/{self.deployment_name}",
+            "-n",
+            self.namespace,
+            f"--timeout={timeout}s",
+        ]
+
+        success, output = self._run_kubectl(cmd)
+        if success:
+            logger.info("Deployment is ready")
+            return True
+        else:
+            logger.error(f"Deployment failed to become ready: {output}")
+            return False
+
+    def apply_deployment(self, yaml_file: str) -> bool:
+        """Apply Kubernetes deployment from YAML file."""
+        logger.info(f"Applying deployment from {yaml_file}")
+
+        cmd = ["kubectl", "apply", "-f", yaml_file, "-n", self.namespace]
+        success, output = self._run_kubectl(cmd)
+
+        if success:
+            logger.info("Deployment applied successfully")
+            return True
+        else:
+            logger.error(f"Failed to apply deployment: {output}")
+            return False
+
+    def delete_deployment(self, yaml_file: str) -> bool:
+        """Delete Kubernetes deployment."""
+        logger.info(f"Deleting deployment from {yaml_file}")
+
+        cmd = [
+            "kubectl",
+            "delete",
+            "-f",
+            yaml_file,
+            "-n",
+            self.namespace,
+            "--ignore-not-found",
+        ]
+        success, output = self._run_kubectl(cmd)
+
+        if success:
+            logger.info("Deployment deleted successfully")
+        else:
+            logger.warning(f"Failed to delete deployment: {output}")
+
+        return success
+
+    def check_service_health(
+        self, service_name: str | None = None, port: int = 8000
+    ) -> bool:
+        """Check if the frontend service is healthy."""
+        if service_name is None:
+            service_name = f"{self.deployment_name}-frontend"
+
+        # Port forward to check health
+        cmd = [
+            "kubectl",
+            "port-forward",
+            f"service/{service_name}",
+            f"{port}:{port}",
+            "-n",
+            self.namespace,
+        ]
+
+        proc = None
+        try:
+            # Start port forwarding in background
+            proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+
+            # Give it a moment to establish connection
+            time.sleep(PORT_FORWARD_SETUP_DELAY)
+
+            # Try to check health endpoint
+            try:
+                response = urllib.request.urlopen(
+                    f"http://localhost:{port}/health", timeout=HEALTH_CHECK_TIMEOUT
+                )
+                healthy = response.status == 200
+                logger.info(f"Service health check: {'OK' if healthy else 'FAILED'}")
+            except Exception as e:
+                logger.warning(f"Health check failed: {e}")
+                healthy = False
+
+            return healthy
+
+        except Exception as e:
+            logger.error(f"Failed to check service health: {e}")
+            return False
+        finally:
+            # Ensure port forwarding is terminated
+            if proc is not None:
+                proc.terminate()
+                try:
+                    proc.wait(timeout=5)
+                except subprocess.TimeoutExpired:
+                    proc.kill()
+
+
+class ScalingE2ETest:
+    """End-to-end test for SLA planner scaling behavior."""
+
+    def __init__(
+        self,
+        namespace: str = "default",
+        base_url: str = "http://localhost:8000",
+        save_results: bool = False,
+    ):
+        self.namespace = namespace
+        self.base_url = base_url
+        self.save_results = save_results
+
+        self.k8s_monitor = KubernetesMonitor(namespace)
+        self.load_generator = LoadGenerator(
+            base_url=base_url, save_results=save_results
+        )
+
+        self.test_results: Dict[str, Any] = {}
+
+    async def run_scaling_test(self) -> Dict:
+        """
+        Run the complete scaling test.
+
+        Hardcoded scenario:
+        - Phase 1 (12 req/s): Should maintain 1P1D
+        - Phase 2 (24 req/s): Should scale to 2P1D
+        """
+        logger.info("Starting scaling integration test")
+
+        test_start_time = time.time()
+
+        # Record initial state
+        initial_counts = self.k8s_monitor.get_pod_counts()
+        logger.info(f"Test starting with: {initial_counts}")
+
+        # Start background monitoring
+        # Calculate based on actual phases from load generator
+        # Phase durations: baseline(90s) + transition(30s) + moderate(120s) + transition(30s) + trigger(180s) + buffer
+        total_test_duration = 90 + 30 + 120 + 30 + 180 + BUFFER_DURATION
+        monitoring_task = asyncio.create_task(
+            self.k8s_monitor.monitor_scaling(
+                total_test_duration, interval=MONITORING_INTERVAL
+            )
+        )
+
+        # Initialize results in case of exception
+        baseline_results = {}
+        moderate_results = {}
+        trigger_results = {}
+
+        try:
+            # Use the load generator's built-in scaling test
+            logger.info("Running scaling scenario (8 req/s -> 15 req/s -> 25 req/s)")
+            load_results = await self.load_generator.run_scaling_test()
+
+            # Extract load results for analysis (3-phase structure)
+            phase_results = load_results.get("phase_results", {})
+            baseline_results = phase_results.get("phase1_baseline", {})
+            moderate_results = phase_results.get("phase2_moderate", {})
+            trigger_results = phase_results.get("phase3_prefill_scaling_trigger", {})
+
+            # Check final pod counts
+            final_counts = self.k8s_monitor.get_pod_counts()
+            logger.info(f"Final pod counts: {final_counts}")
+
+            # Wait a bit more to capture any delayed scaling
+            logger.info("Waiting for potential delayed scaling...")
+            await asyncio.sleep(FINAL_STABILIZATION_DELAY)
+
+            # Get final final counts
+            final_final_counts = self.k8s_monitor.get_pod_counts()
+            logger.info(f"Final final pod counts: {final_final_counts}")
+
+        except Exception as e:
+            logger.error(f"Test execution failed: {e}")
+            raise
+        finally:
+            # Stop monitoring
+            monitoring_task.cancel()
+            try:
+                await monitoring_task
+            except asyncio.CancelledError:
+                pass
+
+        # Compile results
+        test_results: Dict[str, Any] = {
+            "test_duration": time.time() - test_start_time,
+            "config": {
+                # Document actual test configuration
+                "baseline_rps": 8.0,
+                "moderate_rps": 15.0,
+                "trigger_rps": 25.0,
+                "phase_durations": {"baseline": 90, "moderate": 120, "trigger": 180},
+                "transition_delay": 30,
+            },
+            "initial_pod_counts": initial_counts.__dict__ if initial_counts else None,
+            "baseline_results": baseline_results,
+            "moderate_results": moderate_results,
+            "trigger_results": trigger_results,
+            "final_pod_counts": final_counts.__dict__ if final_counts else None,
+            "final_final_pod_counts": final_final_counts.__dict__
+            if final_final_counts
+            else None,
+            "pod_history": [counts.__dict__ for counts in self.k8s_monitor.pod_history],
+            "scaling_analysis": self.analyze_scaling_behavior(),
+        }
+
+        return test_results
+
+    def analyze_scaling_behavior(self) -> Dict:
+        """Analyze the scaling behavior from pod history."""
+        if len(self.k8s_monitor.pod_history) < 2:
+            return {"error": "Insufficient data for analysis"}
+
+        history = self.k8s_monitor.pod_history
+
+        # Find scaling events
+        scaling_events = []
+        for i in range(1, len(history)):
+            prev = history[i - 1]
+            curr = history[i]
+
+            if (
+                curr.prefill_pods != prev.prefill_pods
+                or curr.decode_pods != prev.decode_pods
+            ):
+                scaling_events.append(
+                    {
+                        "timestamp": curr.timestamp,
+                        "from": f"P={prev.prefill_pods}, D={prev.decode_pods}",
+                        "to": f"P={curr.prefill_pods}, D={curr.decode_pods}",
+                        "change": {
+                            "prefill": curr.prefill_pods - prev.prefill_pods,
+                            "decode": curr.decode_pods - prev.decode_pods,
+                        },
+                    }
+                )
+
+        # Check if expected scaling occurred
+        initial = history[0]
+        final = history[-1]
+
+        expected_scaling = {
+            "initial_1p1d": initial.prefill_pods == 1 and initial.decode_pods == 1,
+            "final_2p1d": final.prefill_pods == 2 and final.decode_pods == 1,
+            "scaling_occurred": len(scaling_events) > 0,
+            "correct_scaling": (
+                final.prefill_pods == 2
+                and final.decode_pods == 1
+                and initial.prefill_pods == 1
+                and initial.decode_pods == 1
+            ),
+        }
+
+        return {
+            "scaling_events": scaling_events,
+            "initial_state": f"P={initial.prefill_pods}, D={initial.decode_pods}",
+            "final_state": f"P={final.prefill_pods}, D={final.decode_pods}",
+            "expected_scaling": expected_scaling,
+            "total_scaling_events": len(scaling_events),
+        }
+
+    def validate_test_results(self, results: Dict) -> Dict:
+        """Validate that the test achieved expected scaling behavior."""
+        validation: Dict[str, Any] = {"test_passed": False, "issues": [], "summary": ""}
+
+        # Check if we have the expected data
+        if not results.get("scaling_analysis"):
+            validation["issues"].append("No scaling analysis data")
+            return validation
+
+        analysis = results["scaling_analysis"]
+        expected = analysis.get("expected_scaling", {})
+
+        # Validate initial state
+        if not expected.get("initial_1p1d"):
+            validation["issues"].append("Test did not start with 1P1D configuration")
+
+        # Validate final state
+        if not expected.get("final_2p1d"):
+            validation["issues"].append(
+                "Test did not end with expected 2P1D configuration"
+            )
+
+        # Validate scaling occurred
+        if not expected.get("scaling_occurred"):
+            validation["issues"].append("No scaling events detected")
+
+        # Check if correct scaling occurred
+        if expected.get("correct_scaling"):
+            validation["test_passed"] = True
+            validation[
+                "summary"
+            ] = "✅ Test PASSED: Successfully scaled from 1P1D to 2P1D"
+        else:
+            validation[
+                "summary"
+            ] = "❌ Test FAILED: Did not achieve expected 1P1D -> 2P1D scaling"
+
+        # Add performance validation across all phases
+        baseline = results.get("baseline_results", {})
+        moderate = results.get("moderate_results", {})
+        trigger = results.get("trigger_results", {})
+
+        if baseline.get("throughput", 0) > 0:
+            validation["baseline_throughput"] = f"{baseline['throughput']:.2f} req/s"
+        if moderate.get("throughput", 0) > 0:
+            validation["moderate_throughput"] = f"{moderate['throughput']:.2f} req/s"
+        if trigger.get("throughput", 0) > 0:
+            validation["trigger_throughput"] = f"{trigger['throughput']:.2f} req/s"
+
+        return validation
+
+
+async def main():
+    """Main function for running the e2e test."""
+    import argparse
+
+    parser = argparse.ArgumentParser(description="SLA Planner Scaling E2E Test")
+    parser.add_argument("--namespace", default="default", help="Kubernetes namespace")
+    parser.add_argument(
+        "--base-url", default="http://localhost:8000", help="Service URL"
+    )
+    parser.add_argument(
+        "--save-results",
+        action="store_true",
+        help="Save results to tests/planner/e2e_scaling_results instead of /tmp",
+    )
+    # No additional arguments needed - test is hardcoded
+
+    args = parser.parse_args()
+
+    test = ScalingE2ETest(
+        namespace=args.namespace, base_url=args.base_url, save_results=args.save_results
+    )
+
+    try:
+        # Check that service is accessible
+        logger.info(f"Checking service availability at {args.base_url}...")
+
+        # Run the scaling test
+        logger.info("Running scaling test...")
+        results = await test.run_scaling_test()
+
+        # Validate results
+        validation = test.validate_test_results(results)
+
+        # Save results
+        timestamp = int(time.time())
+        results_file = f"/tmp/scaling_test_results_{timestamp}.json"
+        with open(results_file, "w") as f:
+            json.dump({"results": results, "validation": validation}, f, indent=2)
+
+        # Print summary
+        logger.info("=" * 60)
+        logger.info("TEST SUMMARY")
+        logger.info("=" * 60)
+        logger.info(validation["summary"])
+
+        if validation["issues"]:
+            logger.info("\nIssues found:")
+            for issue in validation["issues"]:
+                logger.info(f"  - {issue}")
+
+        if any(k.endswith("_throughput") for k in validation.keys()):
+            logger.info("\nPerformance:")
+            if "baseline_throughput" in validation:
+                logger.info(
+                    f"  Baseline (8 req/s): {validation['baseline_throughput']}"
+                )
+            if "moderate_throughput" in validation:
+                logger.info(
+                    f"  Moderate (15 req/s): {validation['moderate_throughput']}"
+                )
+            if "trigger_throughput" in validation:
+                logger.info(f"  Trigger (25 req/s): {validation['trigger_throughput']}")
+
+        logger.info(f"\nDetailed results saved to: {results_file}")
+        logger.info("=" * 60)
+
+        return 0 if validation["test_passed"] else 1
+
+    except Exception as e:
+        logger.error(f"Test failed with error: {e}")
+        return 1
+
+
+if __name__ == "__main__":
+    import sys
+
+    sys.exit(asyncio.run(main()))

tests/planner/utils/__init__.py

+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0

tests/planner/utils/load_generator.py

+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""
+Load generation script for SLA planner scaling tests.
+
+This script uses genai-perf to generate load at specific request rates
+to test the planner's scaling behavior.
+"""
+
+import argparse
+import asyncio
+import json
+import logging
+import os
+import tempfile
+import time
+from typing import Any, Dict, List, Optional
+
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+
+class LoadGenerator:
+    """Generate load using genai-perf to test planner scaling."""
+
+    def __init__(
+        self,
+        base_url: str = "http://localhost:8000",
+        model: str = "nvidia/Llama-3.1-8B-Instruct-FP8",
+        isl: int = 4000,
+        osl: int = 150,
+        save_results: bool = False,
+    ):
+        self.base_url = base_url
+        self.model = model
+        self.isl = isl
+        self.osl = osl
+        self.save_results = save_results
+
+    def _calculate_genai_perf_params(
+        self,
+        req_per_sec: float,
+    ) -> Dict[str, Any]:
+        """
+        Calculate genai-perf parameters to approximate desired request rate.
+
+        Args:
+            req_per_sec: Desired requests per second
+            duration_sec: Test duration in seconds
+            estimated_request_duration: Estimated average request duration in seconds
+
+        Returns:
+            Dictionary with concurrency and request_rate parameters
+        """
+        concurrency = max(1, int(req_per_sec * 3))
+
+        return {
+            "concurrency": concurrency,
+            "request_rate": req_per_sec,
+        }
+
+    async def generate_load(
+        self, req_per_sec: float, duration_sec: int, artifact_dir: Optional[str] = None
+    ) -> Dict[str, Any]:
+        """
+        Generate load at specified request rate for given duration.
+
+        Args:
+            req_per_sec: Target requests per second
+            duration_sec: Duration to generate load (seconds)
+            artifact_dir: Directory to store genai-perf artifacts
+
+        Returns:
+            Dictionary with load test results
+        """
+        logger.info(f"Generating load: {req_per_sec} req/s for {duration_sec}s")
+
+        # Calculate genai-perf parameters
+        params = self._calculate_genai_perf_params(req_per_sec)
+        logger.info(f"Using request_rate={params['request_rate']} req/s")
+
+        # Create artifact directory if not provided
+        if artifact_dir is None:
+            artifact_dir = tempfile.mkdtemp(prefix="scaling_test_")
+
+        os.makedirs(artifact_dir, exist_ok=True)
+
+        # Drive test length by caller-provided duration
+        request_count = max(1, int(params["request_rate"] * duration_sec))
+
+        logger.info(
+            f"Adjusted parameters: duration={duration_sec}s, request_count={request_count}"
+        )
+
+        # Build genai-perf command based on coworker's successful approach
+        cmd = [
+            "genai-perf",
+            "profile",
+            "--model",
+            self.model,
+            "--tokenizer",
+            self.model,
+            "--endpoint-type",
+            "chat",
+            "--url",
+            self.base_url.replace("http://", ""),
+            "--streaming",
+            "--synthetic-input-tokens-mean",
+            str(self.isl),
+            "--output-tokens-mean",
+            str(self.osl),
+            "--request-rate",
+            str(params["request_rate"]),
+            "--request-count",
+            str(request_count),  # Use request count to limit test duration
+            "--stability-percentage",
+            "50",
+            "--num-dataset-entries",
+            str(
+                max(20, int(params["request_rate"] * 10))
+            ),  # Generate reasonable dataset size
+            "--artifact-dir",
+            artifact_dir,
+            "--",
+            "-v",
+            "-max-threads",
+            "64",
+        ]
+
+        logger.info(f"Running command: {' '.join(cmd)}")
+
+        # Run genai-perf (async)
+        start_time = time.time()
+        timeout = max(duration_sec + 60, int(duration_sec * 1.5))
+        try:
+            proc = await asyncio.create_subprocess_exec(
+                *cmd,
+                stdout=asyncio.subprocess.PIPE,
+                stderr=asyncio.subprocess.PIPE,
+            )
+            try:
+                stdout, stderr = await asyncio.wait_for(
+                    proc.communicate(), timeout=timeout
+                )
+            except asyncio.TimeoutError:
+                proc.kill()
+                await proc.communicate()
+                logger.error("genai-perf timed out")
+                raise RuntimeError("Load generation timed out")
+
+            end_time = time.time()
+            actual_duration = end_time - start_time
+
+            # Persist logs for debugging
+            try:
+                with open(
+                    os.path.join(artifact_dir, "genai_perf.stdout.log"), "wb"
+                ) as f:
+                    f.write(stdout or b"")
+                with open(
+                    os.path.join(artifact_dir, "genai_perf.stderr.log"), "wb"
+                ) as f:
+                    f.write(stderr or b"")
+            except Exception:
+                pass
+
+            if proc.returncode == 0:
+                logger.info("Load generation completed successfully")
+                logger.info(f"Actual duration: {actual_duration:.2f}s")
+                results = self._parse_genai_perf_results(artifact_dir)
+                results.update(
+                    {
+                        "requested_req_per_sec": req_per_sec,
+                        "actual_duration": actual_duration,
+                        "target_duration": duration_sec,
+                        "genai_perf_params": params,
+                        "artifact_dir": artifact_dir,
+                        "success": True,
+                    }
+                )
+                return results
+            else:
+                logger.error(f"genai-perf failed with return code {proc.returncode}")
+                raise RuntimeError("genai-perf failed; see logs in artifact dir")
+        except RuntimeError:
+            raise
+        except Exception as e:
+            logger.error(f"genai-perf execution error: {e}")
+            raise
+
+    def _parse_genai_perf_results(self, artifact_dir: str) -> Dict[str, Any]:
+        """Parse genai-perf results from artifact directory."""
+        try:
+            # Look for the profile_export_genai_perf.json file
+            json_files = [f for f in os.listdir(artifact_dir) if f.endswith(".json")]
+            if not json_files:
+                logger.warning("No JSON results found in artifact directory")
+                return {}
+
+            # Main results file
+            results_file = None
+            for json_file in json_files:
+                if "profile_export" in json_file or "genai_perf" in json_file:
+                    results_file = os.path.join(artifact_dir, json_file)
+                    break
+
+            if not results_file:
+                results_file = os.path.join(artifact_dir, json_files[0])
+
+            logger.info(f"Parsing results from: {results_file}")
+
+            with open(results_file, "r") as f:
+                data = json.load(f)
+
+            results = {}
+            if "experiments" in data and data["experiments"]:
+                exp = data["experiments"][0]
+                if "perf_metrics" in exp:
+                    metrics = exp["perf_metrics"]
+                    results.update(
+                        {
+                            "throughput": metrics.get("throughput", {}).get("avg", 0),
+                            "ttft_mean": metrics.get("ttft", {}).get("avg", 0),
+                            "itl_mean": metrics.get("inter_token_latency", {}).get(
+                                "avg", 0
+                            ),
+                            "end_to_end_latency_mean": metrics.get(
+                                "request_latency", {}
+                            ).get("avg", 0),
+                        }
+                    )
+            if not results and "profile_export_genai_perf" in data:
+                summary = data.get("summary", {})
+                results.update(
+                    {
+                        "throughput": summary.get("throughput", 0),
+                        "ttft_mean": summary.get("time_to_first_token_ms", 0),
+                        "itl_mean": summary.get("inter_token_latency_ms", 0),
+                    }
+                )
+
+            logger.info(f"Parsed results: {results}")
+            return results
+
+        except Exception as e:
+            logger.warning(f"Failed to parse genai-perf results: {e}")
+            return {}
+
+    async def run_scaling_test(self) -> Dict[str, Any]:
+        """
+        Run a multi-phase graduated scaling test for prefill scaling.
+
+        Uses a conservative graduated approach:
+        - Phase 1: 5 req/s (baseline, should work)
+        - Phase 2: 10 req/s (moderate load)
+        - Phase 3: 18 req/s (should trigger prefill scaling to 2P1D)
+
+        Returns:
+            Dictionary with complete test results
+        """
+        logger.info(
+            "Starting graduated prefill scaling test scenario (targeting 1P1D -> 2P1D)"
+        )
+        logger.info("Using conservative graduated approach with metric generation")
+
+        # Graduated test parameters (optimized for prefill scaling)
+        phases: List[Dict[str, Any]] = [
+            {"rate": 8.0, "duration": 90, "name": "baseline"},
+            {"rate": 15.0, "duration": 120, "name": "moderate"},
+            {"rate": 25.0, "duration": 180, "name": "prefill_scaling_trigger"},
+        ]
+        transition_delay = 30
+
+        # Create artifact directory
+        timestamp = int(time.time())
+        if self.save_results:
+            script_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+            base_dir = os.path.join(
+                script_dir, "e2e_scaling_results", f"scaling_test_{timestamp}"
+            )
+        else:
+            base_dir = f"/tmp/scaling_test_{timestamp}"
+
+        os.makedirs(base_dir, exist_ok=True)
+        logger.info(f"Saving results to: {base_dir}")
+
+        results = {
+            "test_timestamp": timestamp,
+            "config": {
+                "approach": "graduated_scaling",
+                "phases": phases,
+                "transition_delay": transition_delay,
+                "isl": self.isl,
+                "osl": self.osl,
+                "model": self.model,
+            },
+        }
+
+        try:
+            phase_results = {}
+
+            for i, phase in enumerate(phases):
+                phase_name = f"phase{i+1}_{phase['name']}"
+                logger.info(
+                    f"Starting {phase_name}: {phase['rate']} req/s for {phase['duration']}s"
+                )
+
+                phase_dir = os.path.join(base_dir, phase_name)
+                phase_result = await self.generate_load(
+                    req_per_sec=phase["rate"],
+                    duration_sec=phase["duration"],
+                    artifact_dir=phase_dir,
+                )
+                phase_results[phase_name] = phase_result
+
+                # Add transition delay except after last phase
+                if i < len(phases) - 1:
+                    logger.info(f"Transition delay: {transition_delay}s")
+                    await asyncio.sleep(transition_delay)
+
+            results["phase_results"] = phase_results
+            logger.info("Graduated scaling test completed successfully")
+
+        except Exception as e:
+            logger.error(f"Scaling test failed: {e}")
+            results["error"] = str(e)
+            raise
+
+        # Save results
+        results_file = os.path.join(base_dir, "scaling_test_results.json")
+        with open(results_file, "w") as f:
+            json.dump(results, f, indent=2)
+
+        logger.info(f"Test results saved to: {results_file}")
+        return results
+
+
+async def main():
+    """Main function for scaling test execution."""
+    parser = argparse.ArgumentParser(
+        description="SLA Planner Graduated Scaling Test - Optimized for 2P1D prefill scaling"
+    )
+    parser.add_argument(
+        "--base-url",
+        default="http://localhost:8000",
+        help="Service URL (default: http://localhost:8000)",
+    )
+    parser.add_argument(
+        "--model",
+        default="nvidia/Llama-3.1-8B-Instruct-FP8",
+        help="Model name (default: nvidia/Llama-3.1-8B-Instruct-FP8)",
+    )
+    parser.add_argument(
+        "--isl",
+        type=int,
+        default=4000,
+        help="Input sequence length - optimized for prefill scaling (default: 4000)",
+    )
+    parser.add_argument(
+        "--osl",
+        type=int,
+        default=150,
+        help="Output sequence length - optimized for prefill scaling (default: 150)",
+    )
+    parser.add_argument(
+        "--save-results",
+        action="store_true",
+        help="Save results to tests/planner/e2e_scaling_results instead of /tmp",
+    )
+
+    args = parser.parse_args()
+
+    generator = LoadGenerator(
+        base_url=args.base_url,
+        model=args.model,
+        isl=args.isl,
+        osl=args.osl,
+        save_results=args.save_results,
+    )
+
+    print("Starting SLA Planner Graduated Scaling Test...")
+    print(f"Parameters: ISL={args.isl}, OSL={args.osl}")
+    print(
+        "Test phases: 8 -> 15 -> 25 req/s (optimized for 1P1D -> 2P1D prefill scaling)"
+    )
+
+    results = await generator.run_scaling_test()
+
+    print("\n" + "=" * 60)
+    print("SCALING TEST COMPLETED")
+    print("=" * 60)
+
+    # Print results summary
+    phase_results = results.get("phase_results", {})
+    for phase_name, phase_data in phase_results.items():
+        ok = isinstance(phase_data, dict) and phase_data.get(
+            "success", bool(phase_data)
+        )
+        if ok:
+            duration = phase_data.get("actual_duration")
+            if isinstance(duration, (int, float)):
+                print(f"{phase_name}: {duration:.1f}s duration - SUCCESS")
+            else:
+                print(f"{phase_name}: SUCCESS")
+        else:
+            print(f"{phase_name}: FAILED")
+    print("\nResults saved to scaling test directory")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())