Performance optimization is critical for the LLM Guardian Cluster to deliver fast, efficient, and cost-effective AI services. This document covers comprehensive optimization strategies across all system layers, from hardware utilization to algorithmic improvements.
graph TB
subgraph "Request Optimization Layer"
CACHE[Intelligent Caching]
BATCH[Dynamic Batching]
ROUTE[Smart Routing]
PREFETCH[Predictive Prefetching]
end
subgraph "Model Optimization Layer"
QUANT[Model Quantization]
PRUNE[Model Pruning]
DISTILL[Knowledge Distillation]
COMPILE[Model Compilation]
end
subgraph "Infrastructure Optimization Layer"
GPU_OPT[GPU Optimization]
MEM_OPT[Memory Optimization]
NET_OPT[Network Optimization]
STORAGE_OPT[Storage Optimization]
end
subgraph "System Optimization Layer"
LOAD_BAL[Intelligent Load Balancing]
AUTO_SCALE[Auto-scaling]
RESOURCE_POOL[Resource Pooling]
SCHEDULER[Advanced Scheduling]
end
subgraph "Guardian Optimization Layer"
ASYNC_GUARD[Asynchronous Guardians]
GUARD_CACHE[Guardian Result Caching]
PARALLEL_EVAL[Parallel Evaluation]
SMART_SAMPLE[Smart Sampling]
end
CACHE --> QUANT
BATCH --> PRUNE
ROUTE --> DISTILL
PREFETCH --> COMPILE
QUANT --> GPU_OPT
PRUNE --> MEM_OPT
DISTILL --> NET_OPT
COMPILE --> STORAGE_OPT
GPU_OPT --> LOAD_BAL
MEM_OPT --> AUTO_SCALE
NET_OPT --> RESOURCE_POOL
STORAGE_OPT --> SCHEDULER
LOAD_BAL --> ASYNC_GUARD
AUTO_SCALE --> GUARD_CACHE
RESOURCE_POOL --> PARALLEL_EVAL
SCHEDULER --> SMART_SAMPLE
# llm_guardian_cluster/optimization/quantization.py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from torch.ao.quantization import quantize_dynamic
import bitsandbytes as bnb
from typing import Dict, Any, Optional
class ModelQuantizer:
def __init__(self):
self.quantization_configs = {
"int8": {
"method": "dynamic",
"dtype": torch.qint8,
"qconfig_spec": {
torch.nn.Linear: torch.ao.quantization.default_dynamic_qconfig
}
},
"int4": {
"method": "bitsandbytes",
"load_in_4bit": True,
"bnb_4bit_compute_dtype": torch.float16,
"bnb_4bit_use_double_quant": True,
"bnb_4bit_quant_type": "nf4"
},
"fp16": {
"method": "half_precision",
"dtype": torch.float16
}
}
def quantize_model(self, model_path: str, quantization_type: str = "int8") -> torch.nn.Module:
"""Quantize model for improved performance and memory efficiency"""
config = self.quantization_configs.get(quantization_type)
if not config:
raise ValueError(f"Unknown quantization type: {quantization_type}")
if config["method"] == "dynamic":
# Load model normally first
model = AutoModelForCausalLM.from_pretrained(model_path)
# Apply dynamic quantization
quantized_model = quantize_dynamic(
model,
qconfig_spec=config["qconfig_spec"],
dtype=config["dtype"]
)
elif config["method"] == "bitsandbytes":
# Load model with bitsandbytes quantization
quantized_model = AutoModelForCausalLM.from_pretrained(
model_path,
load_in_4bit=config["load_in_4bit"],
bnb_4bit_compute_dtype=config["bnb_4bit_compute_dtype"],
bnb_4bit_use_double_quant=config["bnb_4bit_use_double_quant"],
bnb_4bit_quant_type=config["bnb_4bit_quant_type"]
)
elif config["method"] == "half_precision":
# Load model in half precision
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=config["dtype"]
)
quantized_model = model.half()
return quantized_model
def benchmark_quantization(self, model_path: str, test_inputs: list) -> Dict[str, Dict[str, float]]:
"""Benchmark different quantization methods"""
results = {}
for quant_type in self.quantization_configs.keys():
print(f"Benchmarking {quant_type} quantization...")
model = self.quantize_model(model_path, quant_type)
tokenizer = AutoTokenizer.from_pretrained(model_path)
# Benchmark metrics
total_time = 0
memory_usage = 0
for test_input in test_inputs:
# Measure inference time
start_time = torch.cuda.Event(enable_timing=True)
end_time = torch.cuda.Event(enable_timing=True)
inputs = tokenizer(test_input, return_tensors="pt")
start_time.record()
with torch.no_grad():
outputs = model.generate(
inputs.input_ids,
max_length=100,
do_sample=False
)
end_time.record()
torch.cuda.synchronize()
inference_time = start_time.elapsed_time(end_time) / 1000 # Convert to seconds
total_time += inference_time
# Measure memory usage
if torch.cuda.is_available():
memory_usage = max(memory_usage, torch.cuda.max_memory_allocated())
avg_time = total_time / len(test_inputs)
memory_gb = memory_usage / (1024**3)
results[quant_type] = {
"avg_inference_time": avg_time,
"peak_memory_gb": memory_gb,
"model_size_mb": self._get_model_size(model)
}
# Clear cache
if torch.cuda.is_available():
torch.cuda.empty_cache()
return results
def _get_model_size(self, model: torch.nn.Module) -> float:
"""Calculate model size in MB"""
param_size = 0
buffer_size = 0
for param in model.parameters():
param_size += param.nelement() * param.element_size()
for buffer in model.buffers():
buffer_size += buffer.nelement() * buffer.element_size()
return (param_size + buffer_size) / (1024**2)# llm_guardian_cluster/optimization/batching.py
import asyncio
import time
from typing import List, Dict, Any, Optional, Callable
from dataclasses import dataclass
from collections import deque
import torch
@dataclass
class BatchRequest:
request_id: str
input_text: str
max_length: int
temperature: float
timestamp: float
future: asyncio.Future
priority: int = 0
class DynamicBatcher:
def __init__(self,
max_batch_size: int = 8,
max_wait_time: float = 0.1,
max_queue_size: int = 100):
self.max_batch_size = max_batch_size
self.max_wait_time = max_wait_time
self.max_queue_size = max_queue_size
self.request_queue = deque()
self.processing = False
self.tokenizer = None
self.model = None
async def add_request(self, request: BatchRequest) -> str:
"""Add request to batch queue"""
if len(self.request_queue) >= self.max_queue_size:
raise Exception("Request queue is full")
request.future = asyncio.Future()
self.request_queue.append(request)
if not self.processing:
asyncio.create_task(self._process_batches())
return await request.future
async def _process_batches(self):
"""Process requests in batches"""
self.processing = True
try:
while self.request_queue:
batch = self._create_batch()
if batch:
await self._process_batch(batch)
else:
# No valid batch, wait a bit
await asyncio.sleep(0.01)
finally:
self.processing = False
def _create_batch(self) -> Optional[List[BatchRequest]]:
"""Create optimal batch from queue"""
if not self.request_queue:
return None
batch = []
current_time = time.time()
# Priority-based batching
sorted_requests = sorted(
list(self.request_queue),
key=lambda r: (-r.priority, r.timestamp)
)
for request in sorted_requests:
# Check if request should be included in batch
if (len(batch) < self.max_batch_size and
(len(batch) == 0 or
current_time - batch[0].timestamp < self.max_wait_time)):
batch.append(request)
self.request_queue.remove(request)
# Force batch if max wait time exceeded
elif batch and current_time - batch[0].timestamp >= self.max_wait_time:
break
return batch if batch else None
async def _process_batch(self, batch: List[BatchRequest]):
"""Process a batch of requests"""
try:
# Group requests by similar parameters for efficiency
param_groups = self._group_by_parameters(batch)
for group in param_groups:
await self._process_parameter_group(group)
except Exception as e:
# Handle batch processing errors
for request in batch:
if not request.future.done():
request.future.set_exception(e)
def _group_by_parameters(self, batch: List[BatchRequest]) -> List[List[BatchRequest]]:
"""Group requests by similar generation parameters"""
groups = {}
for request in batch:
# Create key based on generation parameters
key = (request.max_length, request.temperature)
if key not in groups:
groups[key] = []
groups[key].append(request)
return list(groups.values())
async def _process_parameter_group(self, group: List[BatchRequest]):
"""Process a group of requests with similar parameters"""
# Prepare batch inputs
input_texts = [req.input_text for req in group]
batch_encoding = self.tokenizer(
input_texts,
padding=True,
truncation=True,
return_tensors="pt"
)
# Get generation parameters from first request (all similar)
max_length = group[0].max_length
temperature = group[0].temperature
# Generate responses
with torch.no_grad():
outputs = self.model.generate(
input_ids=batch_encoding.input_ids,
attention_mask=batch_encoding.attention_mask,
max_length=max_length,
temperature=temperature,
do_sample=temperature > 0,
pad_token_id=self.tokenizer.eos_token_id
)
# Decode and return results
for i, request in enumerate(group):
if not request.future.done():
response = self.tokenizer.decode(
outputs[i],
skip_special_tokens=True
)
request.future.set_result(response)# llm_guardian_cluster/optimization/compilation.py
import torch
import torch._dynamo as dynamo
from torch.ao.quantization import quantize_dynamic
from typing import Dict, Any, Optional
import tensorrt as trt
import torch_tensorrt
class ModelCompiler:
def __init__(self):
self.compilation_configs = {
"torch_compile": {
"backend": "inductor",
"mode": "reduce-overhead",
"dynamic": True
},
"tensorrt": {
"precision": torch.half,
"workspace_size": 1 << 25, # 32MB
"max_batch_size": 8
},
"openvino": {
"precision": "FP16",
"device": "CPU"
}
}
def compile_model(self, model: torch.nn.Module,
compilation_type: str = "torch_compile",
example_inputs: Optional[torch.Tensor] = None) -> torch.nn.Module:
"""Compile model for optimized inference"""
if compilation_type == "torch_compile":
return self._torch_compile(model)
elif compilation_type == "tensorrt":
return self._tensorrt_compile(model, example_inputs)
elif compilation_type == "openvino":
return self._openvino_compile(model, example_inputs)
else:
raise ValueError(f"Unknown compilation type: {compilation_type}")
def _torch_compile(self, model: torch.nn.Module) -> torch.nn.Module:
"""Compile model using PyTorch 2.0 compile"""
config = self.compilation_configs["torch_compile"]
# Configure compilation options
compiled_model = torch.compile(
model,
backend=config["backend"],
mode=config["mode"],
dynamic=config["dynamic"]
)
return compiled_model
def _tensorrt_compile(self, model: torch.nn.Module,
example_inputs: torch.Tensor) -> torch.nn.Module:
"""Compile model using TensorRT"""
if example_inputs is None:
raise ValueError("Example inputs required for TensorRT compilation")
config = self.compilation_configs["tensorrt"]
# Convert to TensorRT
trt_model = torch_tensorrt.compile(
model,
inputs=example_inputs,
enabled_precisions={config["precision"]},
workspace_size=config["workspace_size"],
max_batch_size=config["max_batch_size"]
)
return trt_model
def _openvino_compile(self, model: torch.nn.Module,
example_inputs: torch.Tensor) -> torch.nn.Module:
"""Compile model using OpenVINO"""
try:
import openvino as ov
from openvino.tools import mo
except ImportError:
raise ImportError("OpenVINO not installed")
if example_inputs is None:
raise ValueError("Example inputs required for OpenVINO compilation")
config = self.compilation_configs["openvino"]
# Convert to ONNX first
torch.onnx.export(
model,
example_inputs,
"temp_model.onnx",
opset_version=11,
do_constant_folding=True
)
# Convert to OpenVINO IR
ov_model = mo.convert_model("temp_model.onnx")
# Compile for target device
core = ov.Core()
compiled_model = core.compile_model(ov_model, config["device"])
return compiled_model# llm_guardian_cluster/optimization/caching.py
import hashlib
import json
import time
from typing import Dict, Any, Optional, List
from dataclasses import dataclass
from abc import ABC, abstractmethod
import redis
from sentence_transformers import SentenceTransformer
@dataclass
class CacheEntry:
key: str
value: Any
timestamp: float
ttl: float
access_count: int
semantic_embedding: Optional[List[float]] = None
class CacheBackend(ABC):
@abstractmethod
async def get(self, key: str) -> Optional[CacheEntry]:
pass
@abstractmethod
async def set(self, key: str, entry: CacheEntry) -> bool:
pass
@abstractmethod
async def delete(self, key: str) -> bool:
pass
class RedisBackend(CacheBackend):
def __init__(self, redis_url: str):
self.redis_client = redis.from_url(redis_url)
async def get(self, key: str) -> Optional[CacheEntry]:
data = await self.redis_client.get(key)
if data:
return CacheEntry(**json.loads(data))
return None
async def set(self, key: str, entry: CacheEntry) -> bool:
data = json.dumps(entry.__dict__)
return await self.redis_client.setex(key, int(entry.ttl), data)
async def delete(self, key: str) -> bool:
return await self.redis_client.delete(key) > 0
class IntelligentCache:
def __init__(self,
backend: CacheBackend,
semantic_similarity_threshold: float = 0.85,
default_ttl: float = 3600):
self.backend = backend
self.semantic_threshold = semantic_similarity_threshold
self.default_ttl = default_ttl
self.embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
def _create_cache_key(self, request: Dict[str, Any]) -> str:
"""Create deterministic cache key from request"""
# Normalize request for consistent hashing
normalized = {
'query': request.get('query', '').strip().lower(),
'specialist_type': request.get('specialist_type'),
'parameters': sorted(request.get('parameters', {}).items())
}
request_string = json.dumps(normalized, sort_keys=True)
return hashlib.sha256(request_string.encode()).hexdigest()
async def get_cached_response(self, request: Dict[str, Any]) -> Optional[Any]:
"""Get cached response for request"""
# Try exact match first
exact_key = self._create_cache_key(request)
exact_match = await self.backend.get(exact_key)
if exact_match and not self._is_expired(exact_match):
# Update access count
exact_match.access_count += 1
await self.backend.set(exact_key, exact_match)
return exact_match.value
# Try semantic similarity search
semantic_match = await self._find_semantic_match(request)
if semantic_match:
return semantic_match.value
return None
async def cache_response(self, request: Dict[str, Any], response: Any,
custom_ttl: Optional[float] = None) -> bool:
"""Cache response for future use"""
cache_key = self._create_cache_key(request)
ttl = custom_ttl if custom_ttl is not None else self.default_ttl
# Generate semantic embedding for the query
query = request.get('query', '')
embedding = self.embedding_model.encode(query).tolist()
entry = CacheEntry(
key=cache_key,
value=response,
timestamp=time.time(),
ttl=ttl,
access_count=1,
semantic_embedding=embedding
)
return await self.backend.set(cache_key, entry)
async def _find_semantic_match(self, request: Dict[str, Any]) -> Optional[CacheEntry]:
"""Find semantically similar cached responses"""
query = request.get('query', '')
if not query:
return None
query_embedding = self.embedding_model.encode(query)
# This is a simplified version - in production, you'd use a vector database
# for efficient similarity search
# For now, we'll implement a basic approach
# Note: This would be more efficient with a proper vector database
# like ChromaDB or Pinecone for large-scale similarity search
return None
def _is_expired(self, entry: CacheEntry) -> bool:
"""Check if cache entry is expired"""
return time.time() - entry.timestamp > entry.ttl
def _calculate_similarity(self, embedding1: List[float],
embedding2: List[float]) -> float:
"""Calculate cosine similarity between embeddings"""
import numpy as np
vec1 = np.array(embedding1)
vec2 = np.array(embedding2)
dot_product = np.dot(vec1, vec2)
norm1 = np.linalg.norm(vec1)
norm2 = np.linalg.norm(vec2)
if norm1 == 0 or norm2 == 0:
return 0
return dot_product / (norm1 * norm2)# llm_guardian_cluster/optimization/gpu_management.py
import torch
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
import gc
@dataclass
class GPUMemoryStats:
device_id: int
total_memory: int
allocated_memory: int
cached_memory: int
free_memory: int
utilization_percent: float
class GPUMemoryManager:
def __init__(self):
self.device_count = torch.cuda.device_count()
self.memory_pools = {}
for i in range(self.device_count):
self.memory_pools[i] = {
'reserved_blocks': set(),
'free_blocks': set(),
'allocation_history': []
}
def get_memory_stats(self, device_id: Optional[int] = None) -> Dict[int, GPUMemoryStats]:
"""Get memory statistics for GPU devices"""
stats = {}
devices = [device_id] if device_id is not None else range(self.device_count)
for dev_id in devices:
if torch.cuda.is_available():
with torch.cuda.device(dev_id):
total = torch.cuda.get_device_properties(dev_id).total_memory
allocated = torch.cuda.memory_allocated(dev_id)
cached = torch.cuda.memory_reserved(dev_id)
free = total - cached
utilization = (allocated / total) * 100
stats[dev_id] = GPUMemoryStats(
device_id=dev_id,
total_memory=total,
allocated_memory=allocated,
cached_memory=cached,
free_memory=free,
utilization_percent=utilization
)
return stats
def optimize_memory_allocation(self, target_device: int,
required_memory: int) -> bool:
"""Optimize memory allocation for a target device"""
if not torch.cuda.is_available():
return False
current_stats = self.get_memory_stats(target_device)[target_device]
# If sufficient free memory is available, no optimization needed
if current_stats.free_memory >= required_memory:
return True
# Try garbage collection first
self._force_garbage_collection(target_device)
# Check again after garbage collection
current_stats = self.get_memory_stats(target_device)[target_device]
if current_stats.free_memory >= required_memory:
return True
# Try memory defragmentation
self._defragment_memory(target_device)
# Final check
current_stats = self.get_memory_stats(target_device)[target_device]
return current_stats.free_memory >= required_memory
def _force_garbage_collection(self, device_id: int):
"""Force garbage collection and clear CUDA cache"""
with torch.cuda.device(device_id):
# Clear Python garbage
gc.collect()
# Clear CUDA cache
torch.cuda.empty_cache()
# Synchronize device
torch.cuda.synchronize()
def _defragment_memory(self, device_id: int):
"""Attempt to defragment GPU memory"""
with torch.cuda.device(device_id):
# Get current allocations
allocated_tensors = []
# This is a simplified version - in practice, you'd need
# to track tensors more systematically
for obj in gc.get_objects():
if isinstance(obj, torch.Tensor) and obj.is_cuda:
if obj.device.index == device_id:
allocated_tensors.append(obj)
# Clear cache and reallocate
torch.cuda.empty_cache()
# Trigger memory compaction if available
if hasattr(torch.cuda, 'memory_snapshot'):
snapshot = torch.cuda.memory_snapshot()
# Analyze fragmentation patterns
self._analyze_fragmentation(snapshot)
def _analyze_fragmentation(self, memory_snapshot: Dict):
"""Analyze memory fragmentation patterns"""
# Implementation would analyze the memory snapshot
# to identify fragmentation patterns and optimization opportunities
pass
def suggest_optimal_batch_size(self, model_memory: int,
device_id: int,
safety_margin: float = 0.8) -> int:
"""Suggest optimal batch size based on available memory"""
stats = self.get_memory_stats(device_id)[device_id]
available_memory = stats.free_memory * safety_margin
# Estimate memory per sample (this is model-dependent)
# This is a simplified calculation
estimated_per_sample = model_memory * 0.3 # Rough estimate
if estimated_per_sample == 0:
return 1
optimal_batch_size = int(available_memory / estimated_per_sample)
return max(1, optimal_batch_size)# llm_guardian_cluster/optimization/multi_gpu.py
import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel
from typing import List, Dict, Any, Optional
import asyncio
from concurrent.futures import ThreadPoolExecutor
class MultiGPUInferenceManager:
def __init__(self, model_path: str, devices: List[int]):
self.model_path = model_path
self.devices = devices
self.models = {}
self.load_balancer = LoadBalancer(devices)
self.executor = ThreadPoolExecutor(max_workers=len(devices))
async def initialize_models(self):
"""Initialize models on all specified GPUs"""
for device_id in self.devices:
await self._load_model_on_device(device_id)
async def _load_model_on_device(self, device_id: int):
"""Load model on specific GPU device"""
torch.cuda.set_device(device_id)
# Load model with device-specific optimizations
model = torch.load(self.model_path, map_location=f'cuda:{device_id}')
model.eval()
# Apply device-specific optimizations
if torch.cuda.get_device_capability(device_id)[0] >= 7:
# Enable Tensor Core optimizations for newer GPUs
model = model.half()
self.models[device_id] = model
async def parallel_inference(self, requests: List[Dict[str, Any]]) -> List[Any]:
"""Perform parallel inference across multiple GPUs"""
if not requests:
return []
# Distribute requests across available GPUs
device_assignments = self.load_balancer.assign_requests(requests)
# Create inference tasks for each device
tasks = []
for device_id, device_requests in device_assignments.items():
if device_requests:
task = asyncio.create_task(
self._process_device_batch(device_id, device_requests)
)
tasks.append(task)
# Wait for all tasks to complete
results = await asyncio.gather(*tasks)
# Flatten and reorder results to match original request order
flattened_results = []
for result_batch in results:
flattened_results.extend(result_batch)
return flattened_results
async def _process_device_batch(self, device_id: int,
requests: List[Dict[str, Any]]) -> List[Any]:
"""Process batch of requests on specific device"""
model = self.models[device_id]
# Prepare batch inputs
inputs = self._prepare_batch_inputs(requests, device_id)
# Run inference in thread pool to avoid blocking event loop
loop = asyncio.get_event_loop()
results = await loop.run_in_executor(
self.executor,
self._sync_inference,
model,
inputs,
device_id
)
return results
def _sync_inference(self, model: torch.nn.Module,
inputs: Dict[str, torch.Tensor],
device_id: int) -> List[Any]:
"""Synchronous inference on specific device"""
with torch.cuda.device(device_id):
with torch.no_grad():
outputs = model(**inputs)
# Convert outputs back to CPU for result processing
cpu_outputs = {k: v.cpu() for k, v in outputs.items()}
return self._process_outputs(cpu_outputs)
def _prepare_batch_inputs(self, requests: List[Dict[str, Any]],
device_id: int) -> Dict[str, torch.Tensor]:
"""Prepare batch inputs for specific device"""
# This would be implemented based on your specific model requirements
# Example implementation:
batch_texts = [req['input_text'] for req in requests]
# Tokenize batch
tokenized = self.tokenizer(
batch_texts,
padding=True,
truncation=True,
return_tensors='pt'
)
# Move to target device
device_inputs = {
k: v.to(f'cuda:{device_id}')
for k, v in tokenized.items()
}
return device_inputs
def _process_outputs(self, outputs: Dict[str, torch.Tensor]) -> List[Any]:
"""Process model outputs into final results"""
# Implementation depends on your specific model outputs
return []
class LoadBalancer:
def __init__(self, devices: List[int]):
self.devices = devices
self.device_loads = {device: 0 for device in devices}
def assign_requests(self, requests: List[Dict[str, Any]]) -> Dict[int, List[Dict[str, Any]]]:
"""Assign requests to devices based on current load"""
assignments = {device: [] for device in self.devices}
# Sort requests by estimated complexity (if available)
sorted_requests = sorted(
requests,
key=lambda r: r.get('estimated_complexity', 1),
reverse=True
)
for request in sorted_requests:
# Find device with lowest current load
best_device = min(self.devices, key=lambda d: self.device_loads[d])
assignments[best_device].append(request)
# Update load estimate
complexity = request.get('estimated_complexity', 1)
self.device_loads[best_device] += complexity
return assignments# llm_guardian_cluster/optimization/autoscaling.py
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
from datetime import datetime, timedelta
import asyncio
import numpy as np
from sklearn.linear_model import LinearRegression
@dataclass
class ScalingMetrics:
timestamp: datetime
request_rate: float
avg_response_time: float
queue_length: int
cpu_utilization: float
memory_utilization: float
gpu_utilization: float
error_rate: float
@dataclass
class ScalingDecision:
action: str # "scale_up", "scale_down", "no_action"
target_replicas: int
confidence: float
reasoning: str
class IntelligentAutoScaler:
def __init__(self,
min_replicas: int = 1,
max_replicas: int = 10,
target_utilization: float = 0.7,
scale_up_threshold: float = 0.8,
scale_down_threshold: float = 0.5,
prediction_window: int = 300): # 5 minutes
self.min_replicas = min_replicas
self.max_replicas = max_replicas
self.target_utilization = target_utilization
self.scale_up_threshold = scale_up_threshold
self.scale_down_threshold = scale_down_threshold
self.prediction_window = prediction_window
self.metrics_history: List[ScalingMetrics] = []
self.scaling_history: List[Tuple[datetime, ScalingDecision]] = []
self.predictor = LinearRegression()
async def evaluate_scaling_decision(self,
current_metrics: ScalingMetrics,
current_replicas: int) -> ScalingDecision:
"""Evaluate whether scaling action is needed"""
# Add current metrics to history
self.metrics_history.append(current_metrics)
# Keep only recent history
cutoff_time = datetime.now() - timedelta(seconds=self.prediction_window * 3)
self.metrics_history = [
m for m in self.metrics_history
if m.timestamp > cutoff_time
]
# Calculate various scaling signals
utilization_signal = self._calculate_utilization_signal(current_metrics)
trend_signal = self._calculate_trend_signal()
queue_signal = self._calculate_queue_signal(current_metrics)
error_signal = self._calculate_error_signal(current_metrics)
# Predict future load
predicted_load = self._predict_future_load()
# Make scaling decision
decision = self._make_scaling_decision(
current_replicas,
utilization_signal,
trend_signal,
queue_signal,
error_signal,
predicted_load
)
# Record decision
self.scaling_history.append((datetime.now(), decision))
return decision
def _calculate_utilization_signal(self, metrics: ScalingMetrics) -> float:
"""Calculate overall utilization signal"""
weights = {
'cpu': 0.3,
'memory': 0.3,
'gpu': 0.4
}
weighted_utilization = (
weights['cpu'] * metrics.cpu_utilization +
weights['memory'] * metrics.memory_utilization +
weights['gpu'] * metrics.gpu_utilization
)
return weighted_utilization
def _calculate_trend_signal(self) -> float:
"""Calculate trend signal from recent metrics"""
if len(self.metrics_history) < 5:
return 0.0
recent_metrics = self.metrics_history[-5:]
# Calculate trend in request rate
timestamps = [(m.timestamp - self.metrics_history[0].timestamp).total_seconds()
for m in recent_metrics]
request_rates = [m.request_rate for m in recent_metrics]
if len(set(request_rates)) == 1: # All values are the same
return 0.0
# Fit linear regression to detect trend
X = np.array(timestamps).reshape(-1, 1)
y = np.array(request_rates)
try:
self.predictor.fit(X, y)
slope = self.predictor.coef_[0]
# Normalize slope to meaningful scale
normalized_slope = slope / max(request_rates) if max(request_rates) > 0 else 0
return min(max(normalized_slope, -1.0), 1.0)
except:
return 0.0
def _calculate_queue_signal(self, metrics: ScalingMetrics) -> float:
"""Calculate signal based on queue length"""
# Normalize queue length signal
if metrics.queue_length == 0:
return 0.0
# Assume queue length > 10 indicates high pressure
normalized_queue = min(metrics.queue_length / 10.0, 1.0)
return normalized_queue
def _calculate_error_signal(self, metrics: ScalingMetrics) -> float:
"""Calculate signal based on error rate"""
# High error rate might indicate overload
if metrics.error_rate > 0.05: # 5% error rate threshold
return 1.0
elif metrics.error_rate > 0.01: # 1% error rate threshold
return metrics.error_rate / 0.05
return 0.0
def _predict_future_load(self) -> float:
"""Predict future load based on historical patterns"""
if len(self.metrics_history) < 10:
return 0.0
# Use recent metrics for prediction
recent_metrics = self.metrics_history[-10:]
# Extract features for prediction
timestamps = [(m.timestamp - recent_metrics[0].timestamp).total_seconds()
for m in recent_metrics]
request_rates = [m.request_rate for m in recent_metrics]
if len(set(request_rates)) == 1:
return 0.0
try:
X = np.array(timestamps).reshape(-1, 1)
y = np.array(request_rates)
self.predictor.fit(X, y)
# Predict load for next prediction window
future_time = timestamps[-1] + self.prediction_window
predicted_rate = self.predictor.predict([[future_time]])[0]
# Calculate relative change
current_rate = request_rates[-1]
if current_rate > 0:
relative_change = (predicted_rate - current_rate) / current_rate
return min(max(relative_change, -1.0), 1.0)
except:
pass
return 0.0
def _make_scaling_decision(self,
current_replicas: int,
utilization_signal: float,
trend_signal: float,
queue_signal: float,
error_signal: float,
predicted_load: float) -> ScalingDecision:
"""Make final scaling decision based on all signals"""
# Combine signals with weights
weights = {
'utilization': 0.4,
'trend': 0.2,
'queue': 0.2,
'error': 0.1,
'prediction': 0.1
}
combined_signal = (
weights['utilization'] * utilization_signal +
weights['trend'] * trend_signal +
weights['queue'] * queue_signal +
weights['error'] * error_signal +
weights['prediction'] * predicted_load
)
# Check recent scaling history to prevent thrashing
recent_scaling = self._check_recent_scaling()
if recent_scaling:
return ScalingDecision(
action="no_action",
target_replicas=current_replicas,
confidence=0.5,
reasoning="Recent scaling action detected, waiting for stabilization"
)
# Make scaling decision
if combined_signal > self.scale_up_threshold:
target_replicas = min(current_replicas + 1, self.max_replicas)
if target_replicas > current_replicas:
return ScalingDecision(
action="scale_up",
target_replicas=target_replicas,
confidence=combined_signal,
reasoning=f"High load detected (signal: {combined_signal:.2f})"
)
elif combined_signal < self.scale_down_threshold:
target_replicas = max(current_replicas - 1, self.min_replicas)
if target_replicas < current_replicas:
return ScalingDecision(
action="scale_down",
target_replicas=target_replicas,
confidence=1.0 - combined_signal,
reasoning=f"Low load detected (signal: {combined_signal:.2f})"
)
return ScalingDecision(
action="no_action",
target_replicas=current_replicas,
confidence=0.5,
reasoning=f"Load within acceptable range (signal: {combined_signal:.2f})"
)
def _check_recent_scaling(self) -> bool:
"""Check if scaling action happened recently"""
if not self.scaling_history:
return False
recent_cutoff = datetime.now() - timedelta(minutes=5)
recent_actions = [
decision for timestamp, decision in self.scaling_history
if timestamp > recent_cutoff and decision.action != "no_action"
]
return len(recent_actions) > 0This comprehensive performance optimization framework covers all major aspects of optimizing the LLM Guardian Cluster for maximum efficiency, speed, and cost-effectiveness. The implementation includes model optimization, intelligent caching, GPU management, and auto-scaling capabilities that work together to deliver optimal performance across all system components.
This completes the comprehensive Systems Architecture documentation for the LLM Guardian Cluster.