GPU optimiser replicas not scaling

### 🐛 Describe the bug

I have deployed llama 8b on A100 with optimizer-based scaling. I have followed the steps to generate the benchmark data and added the same to redis. But even if I scale the concurrency to 500, the replica count is still 0.

### Steps to Reproduce

Used below yaml to deploy the model and optimizer
```apiVersion: apps/v1
kind: Deployment
metadata:
labels:
model.aibrix.ai/name: llama-3-1-8b-instruct # Note: The label value `model.aibrix.ai/name` here must match with the service name.
model.aibrix.ai/port: "8000"
adapter.model.aibrix.ai/enabled: "true"
name: llama-3-1-8b-instruct
namespace: default
spec:
# replicas: 1
selector:
matchLabels:
model.aibrix.ai/name: llama-3-1-8b-instruct
template:
metadata:
labels:
model.aibrix.ai/name: llama-3-1-8b-instruct
spec:
runtimeClassName: nvidia
nodeSelector:
kubernetes.io/hostname: a100
containers:
- command:
- python3
- -m
- vllm.entrypoints.openai.api_server
- --host
- "0.0.0.0"
- --port
- "8000"
- --uvicorn-log-level
- warning
- --model
- meta-llama/Llama-3.1-8B-Instruct
- --served-model-name
# Note: The `--served-model-name` argument value must also match the Service name and the Deployment label `model.aibrix.ai/name`
- llama-3-1-8b-instruct
- --enable-lora
- --max_lora_rank
- "256"
# - --max-model-len
# - "8192"
image: aibrix/vllm-openai:v0.7.3.self.post1
imagePullPolicy: Always
name: vllm-openai
env:
- name: VLLM_ALLOW_RUNTIME_LORA_UPDATING
value: "True"
- name: HF_TOKEN
value: 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
ports:
- containerPort: 8000
protocol: TCP
resources:
limits:
nvidia.com/gpu: "1"
requests:
nvidia.com/gpu: "1"
- name: aibrix-runtime
image: aibrix/runtime:v0.2.1
command:
- aibrix_runtime
- --port
- "8080"
env:
- name: INFERENCE_ENGINE
value: vllm
- name: INFERENCE_ENGINE_ENDPOINT
value: http://localhost:8000
ports:
- containerPort: 8080
protocol: TCP
livenessProbe:
httpGet:
path: /health
port: 8000
initialDelaySeconds: 60
periodSeconds: 10
readinessProbe:
httpGet:
path: /health
port: 8000
initialDelaySeconds: 60
periodSeconds: 5

---

apiVersion: v1
kind: Service
metadata:
labels:
model.aibrix.ai/name: llama-3-1-8b-instruct
prometheus-discovery: "true"
annotations:
prometheus.io/scrape: "true"
prometheus.io/port: "8080"
name: llama-3-1-8b-instruct # Note: The Service name must match the label value `model.aibrix.ai/name` in the Deployment
namespace: default
spec:
ports:
- name: serve
port: 8000
protocol: TCP
targetPort: 8000
- name: http
port: 8080
protocol: TCP
targetPort: 8080
selector:
model.aibrix.ai/name: llama-3-1-8b-instruct
type: ClusterIP

---
apiVersion: autoscaling.aibrix.ai/v1alpha1
kind: PodAutoscaler
metadata:
name: llama-3-1-8b-instruct-optimizer-scaling
namespace: default
labels:
app.kubernetes.io/name: aibrix
app.kubernetes.io/managed-by: kustomize
kpa.autoscaling.aibrix.ai/scale-down-delay: 0s
spec:
scalingStrategy: KPA
minReplicas: 1
maxReplicas: 4
metricsSources:
- endpoint: aibrix-gpu-optimizer.aibrix-system.svc.cluster.local:8080
metricSourceType: domain
path: /metrics/default/llama-3-1-8b-instruct
protocolType: http
targetMetric: vllm:deployment_replicas
targetValue: "100"
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: llama-3-1-8b-instruct
```

### Expected behavior

The replicas should scale based on the concurrency.

### Environment

v0.2.1