High-performance architecture for optimal AI model interactions
This document details the comprehensive performance optimization and caching architecture implemented in Aurelis for GitHub Models integration, covering system performance, caching strategies, optimization techniques, and monitoring systems.
Aurelis is designed for enterprise-grade performance with the following objectives:
┌─────────────────────────────────────────────────────────────┐
│ Application Performance Layer │
├─────────────────────────────────────────────────────────────┤
│ Request Processing │
│ ├── Input Validation & Sanitization │
│ ├── Request Routing & Load Balancing │
│ ├── Async Request Processing │
│ └── Response Formatting & Compression │
├─────────────────────────────────────────────────────────────┤
│ Caching Layer │
│ ├── L1: In-Memory Cache (LRU/LFU) │
│ ├── L2: Distributed Cache (Redis) │
│ ├── L3: Persistent Cache (Database) │
│ └── CDN: Content Delivery Network │
├─────────────────────────────────────────────────────────────┤
│ Network Optimization │
│ ├── Connection Pooling │
│ ├── HTTP/2 with Multiplexing │
│ ├── Compression (gzip/brotli) │
│ └── Keep-Alive Connections │
├─────────────────────────────────────────────────────────────┤
│ Resource Management │
│ ├── Thread Pool Management │
│ ├── Memory Pool Allocation │
│ ├── Circuit Breaker Patterns │
│ └── Rate Limiting & Throttling │
└─────────────────────────────────────────────────────────────┘
class PerformanceMetrics:
"""Comprehensive performance metrics tracking."""
def __init__(self):
self.response_time_histogram = Histogram(
name="aurelis_response_time_seconds",
documentation="Response time for model requests",
buckets=[0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0]
)
self.throughput_counter = Counter(
name="aurelis_requests_total",
documentation="Total requests processed"
)
self.cache_hit_counter = Counter(
name="aurelis_cache_hits_total",
documentation="Cache hits by type"
)
self.error_rate_counter = Counter(
name="aurelis_errors_total",
documentation="Errors by type and model"
)
self.resource_usage_gauge = Gauge(
name="aurelis_resource_usage",
documentation="Resource usage metrics"
)
def record_request_performance(self, response: ModelResponse):
"""Record performance metrics for request."""
# Response time
self.response_time_histogram.labels(
model=response.model_used.value,
task_type=response.task_type.value,
cached=str(response.cached).lower()
).observe(response.processing_time)
# Throughput
self.throughput_counter.labels(
model=response.model_used.value,
status="success"
).inc()
# Cache performance
if response.cached:
self.cache_hit_counter.labels(
cache_level=response.metadata.get("cache_level", "unknown"),
model=response.model_used.value
).inc()
# Token efficiency
tokens_per_second = (
response.token_usage.get("total_tokens", 0) / response.processing_time
)
self.resource_usage_gauge.labels(
metric="tokens_per_second",
model=response.model_used.value
).set(tokens_per_second)
class MultiLevelCacheManager:
"""Enterprise-grade multi-level caching system."""
def __init__(self):
# L1: In-memory cache (fastest)
self.memory_cache = MemoryCache(
max_size=1000,
ttl=300, # 5 minutes
eviction_policy="lru"
)
# L2: Distributed cache (Redis)
self.distributed_cache = DistributedCache(
redis_cluster=self._get_redis_cluster(),
ttl=3600, # 1 hour
compression=True
)
# L3: Persistent cache (Database)
self.persistent_cache = PersistentCache(
database=self._get_cache_database(),
ttl=86400, # 24 hours
compression=True,
encryption=True
)
# Cache statistics
self.stats = CacheStatistics()
async def get(self, key: str, cache_levels: List[str] = None) -> Optional[CacheEntry]:
"""Get value from cache with fallback through levels."""
cache_levels = cache_levels or ["memory", "distributed", "persistent"]
for level in cache_levels:
try:
start_time = time.time()
if level == "memory":
entry = self.memory_cache.get(key)
elif level == "distributed":
entry = await self.distributed_cache.get(key)
elif level == "persistent":
entry = await self.persistent_cache.get(key)
else:
continue
if entry:
retrieval_time = time.time() - start_time
# Record cache hit
self.stats.record_hit(level, retrieval_time)
# Promote to higher cache levels
await self._promote_to_higher_levels(key, entry, level)
return entry
except Exception as e:
logger.warning(f"Cache level {level} failed: {e}")
self.stats.record_error(level, str(e))
# Record cache miss
self.stats.record_miss()
return None
async def set(
self,
key: str,
value: Any,
ttl: Optional[int] = None,
cache_levels: List[str] = None
):
"""Set value in cache across specified levels."""
cache_levels = cache_levels or ["memory", "distributed", "persistent"]
entry = CacheEntry(value=value, ttl=ttl, created_at=time.time())
# Set in all specified cache levels
tasks = []
if "memory" in cache_levels:
tasks.append(self._set_in_memory(key, entry))
if "distributed" in cache_levels:
tasks.append(self._set_in_distributed(key, entry))
if "persistent" in cache_levels:
tasks.append(self._set_in_persistent(key, entry))
# Execute all cache sets concurrently
results = await asyncio.gather(*tasks, return_exceptions=True)
# Log any failures
for i, result in enumerate(results):
if isinstance(result, Exception):
level = cache_levels[i]
logger.warning(f"Failed to set cache in {level}: {result}")
self.stats.record_error(level, str(result))
async def _promote_to_higher_levels(
self,
key: str,
entry: CacheEntry,
source_level: str
):
"""Promote cache entry to higher-priority cache levels."""
promotion_map = {
"persistent": ["distributed", "memory"],
"distributed": ["memory"],
"memory": []
}
target_levels = promotion_map.get(source_level, [])
for target_level in target_levels:
try:
if target_level == "memory":
self.memory_cache.set(key, entry)
elif target_level == "distributed":
await self.distributed_cache.set(key, entry)
except Exception as e:
logger.debug(f"Failed to promote to {target_level}: {e}")
class IntelligentCacheKeyGenerator:
"""Generate optimal cache keys for maximum hit rates."""
def __init__(self):
self.normalization_rules = {
"case_sensitive": False,
"whitespace_normalize": True,
"parameter_order": True,
"semantic_equivalence": True
}
self.semantic_analyzer = SemanticAnalyzer()
def generate_cache_key(self, request: ModelRequest) -> str:
"""Generate intelligent cache key for model request."""
# Base components
components = [
self._normalize_model_type(request.model_type),
self._normalize_task_type(request.task_type),
self._normalize_prompt(request.prompt),
self._normalize_system_prompt(request.system_prompt),
self._normalize_parameters(request)
]
# Add semantic fingerprint for better matching
semantic_fingerprint = self._generate_semantic_fingerprint(request.prompt)
components.append(semantic_fingerprint)
# Create deterministic hash
content = ":".join(filter(None, components))
cache_key = hashlib.sha256(content.encode()).hexdigest()[:32]
return f"model_request:{cache_key}"
def _normalize_prompt(self, prompt: str) -> str:
"""Normalize prompt for consistent caching."""
if not self.normalization_rules["case_sensitive"]:
prompt = prompt.lower()
if self.normalization_rules["whitespace_normalize"]:
# Normalize whitespace
prompt = re.sub(r'\s+', ' ', prompt.strip())
if self.normalization_rules["semantic_equivalence"]:
# Apply semantic normalization
prompt = self._apply_semantic_normalization(prompt)
return prompt
def _generate_semantic_fingerprint(self, prompt: str) -> str:
"""Generate semantic fingerprint for similar prompts."""
# Extract key concepts and patterns
concepts = self.semantic_analyzer.extract_concepts(prompt)
patterns = self.semantic_analyzer.extract_patterns(prompt)
# Create fingerprint from concepts
fingerprint_components = []
# Sort concepts for consistency
for concept in sorted(concepts):
fingerprint_components.append(concept.normalized_form)
# Add pattern signatures
for pattern in sorted(patterns):
fingerprint_components.append(pattern.signature)
fingerprint = ":".join(fingerprint_components)
return hashlib.md5(fingerprint.encode()).hexdigest()[:16]
class CacheWarmingManager:
"""Intelligent cache warming and preloading system."""
def __init__(self):
self.orchestrator = get_model_orchestrator()
self.usage_analyzer = UsageAnalyzer()
self.prediction_engine = PredictionEngine()
async def warm_cache_on_startup(self):
"""Warm cache with frequently used patterns on system startup."""
# Load historical usage patterns
common_patterns = await self.usage_analyzer.get_common_patterns(
time_period="30d",
min_frequency=10
)
# Warm cache for common requests
warming_tasks = []
for pattern in common_patterns:
# Create representative request
request = self._create_request_from_pattern(pattern)
# Add to warming queue
warming_task = self._warm_cache_entry(request)
warming_tasks.append(warming_task)
# Execute warming tasks with concurrency control
semaphore = asyncio.Semaphore(5) # Limit concurrent warming
async def warm_with_semaphore(task):
async with semaphore:
return await task
results = await asyncio.gather(
*[warm_with_semaphore(task) for task in warming_tasks],
return_exceptions=True
)
successful_warmings = sum(
1 for result in results
if not isinstance(result, Exception)
)
logger.info(f"Cache warming completed: {successful_warmings}/{len(warming_tasks)} successful")
async def predictive_cache_warming(self):
"""Use ML to predict and pre-warm likely requests."""
# Get predictions for next hour
predictions = await self.prediction_engine.predict_likely_requests(
time_horizon="1h",
confidence_threshold=0.7
)
# Sort by prediction confidence
predictions.sort(key=lambda p: p.confidence, reverse=True)
# Warm top predictions
for prediction in predictions[:50]: # Top 50 predictions
try:
request = prediction.to_model_request()
await self._warm_cache_entry(request)
except Exception as e:
logger.warning(f"Failed to warm predicted request: {e}")
async def _warm_cache_entry(self, request: ModelRequest):
"""Warm specific cache entry."""
try:
# Check if already cached
cache_key = self._generate_cache_key(request)
if await self._is_cached(cache_key):
return # Already cached
# Execute request to warm cache
response = await self.orchestrator.send_request(request)
logger.debug(f"Warmed cache for {request.task_type.value} request")
except Exception as e:
logger.warning(f"Cache warming failed: {e}")
class AsyncRequestProcessor:
"""High-performance asynchronous request processing."""
def __init__(self):
self.request_pool = asyncio.Queue(maxsize=1000)
self.worker_pool = WorkerPool(size=20)
self.circuit_breaker = CircuitBreaker()
self.rate_limiter = TokenBucketRateLimiter()
async def process_request(self, request: ModelRequest) -> ModelResponse:
"""Process request with optimized async handling."""
# Rate limiting
await self.rate_limiter.acquire()
# Circuit breaker check
if self.circuit_breaker.is_open():
raise ServiceUnavailableError("Service temporarily unavailable")
try:
# Add request to processing queue
future = asyncio.Future()
await self.request_pool.put((request, future))
# Wait for processing with timeout
response = await asyncio.wait_for(future, timeout=60.0)
self.circuit_breaker.record_success()
return response
except asyncio.TimeoutError:
self.circuit_breaker.record_failure()
raise TimeoutError("Request processing timeout")
except Exception as e:
self.circuit_breaker.record_failure()
raise e
async def batch_process_requests(
self,
requests: List[ModelRequest]
) -> List[ModelResponse]:
"""Process multiple requests in optimized batches."""
# Group requests by model type for efficiency
model_groups = self._group_requests_by_model(requests)
# Process each group concurrently
all_responses = []
processing_tasks = []
for model_type, model_requests in model_groups.items():
# Create batches within each model group
batches = self._create_batches(model_requests, batch_size=5)
for batch in batches:
task = self._process_batch(model_type, batch)
processing_tasks.append(task)
# Execute all batches concurrently
batch_results = await asyncio.gather(*processing_tasks)
# Flatten and restore original order
for batch_responses in batch_results:
all_responses.extend(batch_responses)
return self._restore_original_order(requests, all_responses)
async def _process_batch(
self,
model_type: ModelType,
batch: List[Tuple[int, ModelRequest]]
) -> List[Tuple[int, ModelResponse]]:
"""Process a batch of requests for a specific model."""
# Execute requests in parallel within batch
batch_tasks = []
for index, request in batch:
# Ensure model type is set
request.model_type = model_type
# Create processing task
task = self._process_single_request_with_index(index, request)
batch_tasks.append(task)
# Wait for all requests in batch
batch_results = await asyncio.gather(*batch_tasks, return_exceptions=True)
# Handle exceptions
successful_results = []
for result in batch_results:
if isinstance(result, Exception):
logger.warning(f"Batch request failed: {result}")
# Could add retry logic here
else:
successful_results.append(result)
return successful_results
class RequestOptimizer:
"""Optimize request size and serialization."""
def __init__(self):
self.compressor = CompressorManager()
self.serializer = AdvancedSerializer()
def optimize_request(self, request: ModelRequest) -> OptimizedRequest:
"""Optimize request for network transmission."""
# Serialize request
serialized_data = self.serializer.serialize(request)
# Compress if beneficial
compression_info = self._analyze_compression_benefit(serialized_data)
if compression_info.beneficial:
compressed_data = self.compressor.compress(
serialized_data,
algorithm=compression_info.best_algorithm
)
return OptimizedRequest(
data=compressed_data,
compressed=True,
compression_algorithm=compression_info.best_algorithm,
original_size=len(serialized_data),
compressed_size=len(compressed_data),
compression_ratio=compression_info.ratio
)
return OptimizedRequest(
data=serialized_data,
compressed=False,
original_size=len(serialized_data)
)
def _analyze_compression_benefit(self, data: bytes) -> CompressionAnalysis:
"""Analyze potential compression benefits."""
original_size = len(data)
# Test different compression algorithms
algorithms = ["gzip", "brotli", "lz4"]
best_ratio = 1.0
best_algorithm = None
for algorithm in algorithms:
try:
compressed = self.compressor.compress(data, algorithm)
ratio = len(compressed) / original_size
if ratio < best_ratio:
best_ratio = ratio
best_algorithm = algorithm
except Exception as e:
logger.debug(f"Compression test failed for {algorithm}: {e}")
# Consider compression beneficial if saves > 10%
beneficial = best_ratio < 0.9 and original_size > 1024
return CompressionAnalysis(
beneficial=beneficial,
best_algorithm=best_algorithm,
ratio=best_ratio,
size_reduction=original_size * (1 - best_ratio)
)
class AdvancedConnectionManager:
"""Advanced HTTP/2 connection pooling and management."""
def __init__(self):
self.connection_pools = {}
self.pool_config = ConnectionPoolConfig(
max_connections_per_host=20,
max_total_connections=100,
connection_timeout=10.0,
request_timeout=60.0,
keep_alive_timeout=30.0,
http2_enabled=True,
connection_reuse=True
)
async def get_connection(self, endpoint: str) -> AsyncConnection:
"""Get optimized connection for endpoint."""
pool_key = self._get_pool_key(endpoint)
if pool_key not in self.connection_pools:
self.connection_pools[pool_key] = await self._create_connection_pool(endpoint)
pool = self.connection_pools[pool_key]
# Get connection with health check
connection = await pool.acquire()
if not await self._is_connection_healthy(connection):
# Replace unhealthy connection
await pool.release(connection, discard=True)
connection = await pool.acquire()
return connection
async def _create_connection_pool(self, endpoint: str) -> ConnectionPool:
"""Create optimized connection pool for endpoint."""
# HTTP/2 configuration
http2_config = HTTP2Config(
enable_server_push=False, # Not needed for API calls
max_concurrent_streams=100,
initial_window_size=65536,
max_frame_size=16384
)
# TLS configuration
tls_config = TLSConfig(
min_version="TLSv1.3",
cipher_suites=[
"TLS_AES_256_GCM_SHA384",
"TLS_CHACHA20_POLY1305_SHA256"
],
session_reuse=True,
certificate_verification=True
)
# Create connection pool
pool = ConnectionPool(
endpoint=endpoint,
min_size=2,
max_size=self.pool_config.max_connections_per_host,
http2_config=http2_config,
tls_config=tls_config,
keep_alive=True,
connection_timeout=self.pool_config.connection_timeout
)
return pool
async def _is_connection_healthy(self, connection: AsyncConnection) -> bool:
"""Check if connection is healthy and responsive."""
try:
# Send lightweight health check
start_time = time.time()
response = await connection.head(
"/health",
timeout=5.0
)
response_time = time.time() - start_time
# Consider healthy if responds quickly and successfully
return response.status < 400 and response_time < 2.0
except Exception:
return False
class RequestPipeliner:
"""HTTP/2 request pipelining and multiplexing optimization."""
def __init__(self):
self.stream_manager = StreamManager()
self.request_scheduler = RequestScheduler()
async def pipeline_requests(
self,
requests: List[HTTPRequest],
connection: HTTP2Connection
) -> List[HTTPResponse]:
"""Pipeline multiple requests over HTTP/2 connection."""
# Schedule requests for optimal multiplexing
scheduled_requests = self.request_scheduler.schedule(requests)
# Create streams for concurrent requests
stream_tasks = []
for request in scheduled_requests:
# Create HTTP/2 stream
stream = await connection.create_stream()
# Send request on stream
task = self._send_request_on_stream(stream, request)
stream_tasks.append(task)
# Wait for all responses
responses = await asyncio.gather(*stream_tasks)
return responses
async def _send_request_on_stream(
self,
stream: HTTP2Stream,
request: HTTPRequest
) -> HTTPResponse:
"""Send request on HTTP/2 stream."""
try:
# Send headers
await stream.send_headers(request.headers)
# Send body if present
if request.body:
await stream.send_data(request.body)
# End stream
await stream.end_stream()
# Receive response
response = await stream.receive_response()
return response
except Exception as e:
# Handle stream errors
await stream.reset()
raise e
class IntelligentLoadBalancer:
"""Intelligent load balancing for GitHub Models endpoints."""
def __init__(self):
self.endpoints = self._discover_endpoints()
self.health_monitor = EndpointHealthMonitor()
self.performance_tracker = PerformanceTracker()
self.routing_strategy = AdaptiveRoutingStrategy()
def select_endpoint(self, request: ModelRequest) -> Endpoint:
"""Select optimal endpoint for request."""
# Get healthy endpoints
healthy_endpoints = self.health_monitor.get_healthy_endpoints()
if not healthy_endpoints:
raise ServiceUnavailableError("No healthy endpoints available")
# Apply routing strategy
selected_endpoint = self.routing_strategy.select_endpoint(
request=request,
available_endpoints=healthy_endpoints,
performance_data=self.performance_tracker.get_metrics()
)
return selected_endpoint
async def distribute_requests(
self,
requests: List[ModelRequest]
) -> List[ModelResponse]:
"""Distribute requests across optimal endpoints."""
# Group requests by optimal endpoint
endpoint_groups = {}
for request in requests:
endpoint = self.select_endpoint(request)
if endpoint not in endpoint_groups:
endpoint_groups[endpoint] = []
endpoint_groups[endpoint].append(request)
# Process each endpoint group concurrently
processing_tasks = []
for endpoint, endpoint_requests in endpoint_groups.items():
task = self._process_endpoint_requests(endpoint, endpoint_requests)
processing_tasks.append(task)
# Gather all results
endpoint_results = await asyncio.gather(*processing_tasks)
# Flatten results
all_responses = []
for endpoint_responses in endpoint_results:
all_responses.extend(endpoint_responses)
return all_responses
class AdaptiveRoutingStrategy:
"""Adaptive routing strategy based on performance metrics."""
def __init__(self):
self.routing_algorithms = {
"round_robin": RoundRobinRouter(),
"least_connections": LeastConnectionsRouter(),
"weighted_response_time": WeightedResponseTimeRouter(),
"load_based": LoadBasedRouter(),
"ai_optimized": AIOptimizedRouter()
}
self.current_algorithm = "ai_optimized"
self.performance_window = 300 # 5 minutes
def select_endpoint(
self,
request: ModelRequest,
available_endpoints: List[Endpoint],
performance_data: Dict[str, Any]
) -> Endpoint:
"""Select endpoint using adaptive strategy."""
# Determine best routing algorithm based on current conditions
optimal_algorithm = self._select_optimal_algorithm(
performance_data,
available_endpoints
)
# Use selected algorithm
router = self.routing_algorithms[optimal_algorithm]
return router.select_endpoint(
request=request,
endpoints=available_endpoints,
performance_data=performance_data
)
def _select_optimal_algorithm(
self,
performance_data: Dict[str, Any],
endpoints: List[Endpoint]
) -> str:
"""Select optimal routing algorithm based on current conditions."""
# Analyze current load distribution
load_variance = self._calculate_load_variance(performance_data, endpoints)
# Analyze response time distribution
response_time_variance = self._calculate_response_time_variance(
performance_data, endpoints
)
# High load variance suggests round robin might be better
if load_variance > 0.3:
return "round_robin"
# High response time variance suggests weighted response time
if response_time_variance > 0.5:
return "weighted_response_time"
# Stable conditions - use AI optimization
return "ai_optimized"
class PerformanceAnalyticsEngine:
"""Real-time performance monitoring and analytics."""
def __init__(self):
self.metrics_collector = MetricsCollector()
self.anomaly_detector = PerformanceAnomalyDetector()
self.alerting_system = PerformanceAlertingSystem()
self.dashboard = PerformanceDashboard()
async def monitor_performance(self):
"""Continuous performance monitoring loop."""
while True:
try:
# Collect current metrics
current_metrics = await self.metrics_collector.collect_current_metrics()
# Analyze performance trends
trend_analysis = await self._analyze_performance_trends(current_metrics)
# Detect anomalies
anomalies = await self.anomaly_detector.detect_anomalies(current_metrics)
if anomalies:
await self._handle_performance_anomalies(anomalies)
# Update dashboard
await self.dashboard.update_real_time_metrics(current_metrics)
# Check for performance alerts
await self._check_performance_alerts(current_metrics)
# Sleep before next monitoring cycle
await asyncio.sleep(10) # 10-second monitoring interval
except Exception as e:
logger.error(f"Performance monitoring error: {e}")
await asyncio.sleep(30) # Longer sleep on error
async def generate_performance_report(
self,
time_period: str = "24h"
) -> PerformanceReport:
"""Generate comprehensive performance report."""
metrics = await self.metrics_collector.get_historical_metrics(time_period)
report = PerformanceReport(period=time_period)
# Calculate key performance indicators
report.average_response_time = self._calculate_average_response_time(metrics)
report.p95_response_time = self._calculate_percentile_response_time(metrics, 95)
report.p99_response_time = self._calculate_percentile_response_time(metrics, 99)
report.total_requests = self._calculate_total_requests(metrics)
report.requests_per_second = self._calculate_requests_per_second(metrics)
report.cache_hit_rate = self._calculate_cache_hit_rate(metrics)
report.error_rate = self._calculate_error_rate(metrics)
# Performance by model
report.model_performance = self._analyze_model_performance(metrics)
# Resource utilization
report.resource_utilization = self._analyze_resource_utilization(metrics)
# Performance recommendations
report.recommendations = self._generate_performance_recommendations(metrics)
return report
def _generate_performance_recommendations(
self,
metrics: PerformanceMetrics
) -> List[PerformanceRecommendation]:
"""Generate performance optimization recommendations."""
recommendations = []
# Cache hit rate recommendations
if metrics.cache_hit_rate < 0.7:
recommendations.append(PerformanceRecommendation(
type="cache_optimization",
priority="high",
description="Cache hit rate is below 70%. Consider cache warming and TTL optimization.",
expected_improvement="10-20% response time reduction"
))
# Response time recommendations
if metrics.p95_response_time > 2.0:
recommendations.append(PerformanceRecommendation(
type="response_time_optimization",
priority="medium",
description="95th percentile response time exceeds 2 seconds. Consider request optimization.",
expected_improvement="15-25% response time reduction"
))
# Model-specific recommendations
slow_models = [
model for model, perf in metrics.model_performance.items()
if perf.average_response_time > 3.0
]
if slow_models:
recommendations.append(PerformanceRecommendation(
type="model_optimization",
priority="medium",
description=f"Models {slow_models} show slow response times. Consider model routing optimization.",
expected_improvement="20-30% response time reduction for affected models"
))
return recommendations
class AdaptivePerformanceTuner:
"""Automatically tune performance parameters based on workload."""
def __init__(self):
self.ml_optimizer = MLPerformanceOptimizer()
self.parameter_controller = ParameterController()
self.performance_predictor = PerformancePredictor()
async def optimize_performance(self):
"""Continuously optimize performance parameters."""
while True:
try:
# Analyze current performance
current_metrics = await self._get_current_performance()
# Predict performance impact of parameter changes
optimization_candidates = await self._generate_optimization_candidates()
# Evaluate candidates using ML model
best_candidate = await self.ml_optimizer.select_best_optimization(
current_metrics,
optimization_candidates
)
if best_candidate.expected_improvement > 0.05: # 5% improvement threshold
# Apply optimization
await self._apply_optimization(best_candidate)
# Monitor impact
await self._monitor_optimization_impact(best_candidate)
# Sleep before next optimization cycle
await asyncio.sleep(300) # 5-minute optimization cycle
except Exception as e:
logger.error(f"Performance optimization error: {e}")
await asyncio.sleep(600) # Longer sleep on error
async def _generate_optimization_candidates(self) -> List[OptimizationCandidate]:
"""Generate potential optimization parameter changes."""
candidates = []
# Cache parameter optimizations
candidates.extend(await self._generate_cache_optimizations())
# Connection pool optimizations
candidates.extend(await self._generate_connection_optimizations())
# Request batching optimizations
candidates.extend(await self._generate_batching_optimizations())
# Rate limiting optimizations
candidates.extend(await self._generate_rate_limit_optimizations())
return candidates
async def _apply_optimization(self, candidate: OptimizationCandidate):
"""Apply performance optimization safely."""
# Create rollback plan
rollback_plan = await self._create_rollback_plan(candidate)
try:
# Apply parameter changes gradually
await self.parameter_controller.apply_gradual_change(
parameter=candidate.parameter,
target_value=candidate.target_value,
steps=5,
step_duration=60 # 1 minute per step
)
logger.info(f"Applied optimization: {candidate.description}")
except Exception as e:
# Rollback on failure
logger.error(f"Optimization failed, rolling back: {e}")
await self._execute_rollback(rollback_plan)
raise
class MemoryOptimizer:
"""Optimize memory usage for high-performance operation."""
def __init__(self):
self.memory_tracker = MemoryTracker()
self.object_pool = ObjectPool()
self.gc_optimizer = GCOptimizer()
def setup_memory_optimization(self):
"""Setup memory optimization strategies."""
# Configure object pooling for frequently used objects
self.object_pool.configure_pools({
"model_request": ObjectPoolConfig(
factory=lambda: ModelRequest(),
reset_method="reset",
initial_size=100,
max_size=1000
),
"http_response": ObjectPoolConfig(
factory=lambda: HTTPResponse(),
reset_method="reset",
initial_size=50,
max_size=500
),
"cache_entry": ObjectPoolConfig(
factory=lambda: CacheEntry(),
reset_method="reset",
initial_size=200,
max_size=2000
)
})
# Configure garbage collection optimization
self.gc_optimizer.configure({
"gc_threshold_0": 700, # Adjust for high-throughput
"gc_threshold_1": 10,
"gc_threshold_2": 10,
"disable_gc_during_request": True,
"force_gc_interval": 60 # Force GC every minute
})
async def monitor_memory_usage(self):
"""Monitor and optimize memory usage continuously."""
while True:
try:
# Get current memory statistics
memory_stats = self.memory_tracker.get_memory_stats()
# Check for memory pressure
if memory_stats.pressure_level > 0.8:
await self._handle_memory_pressure(memory_stats)
# Optimize object pools
self._optimize_object_pools(memory_stats)
# Sleep before next check
await asyncio.sleep(30) # 30-second memory monitoring
except Exception as e:
logger.error(f"Memory monitoring error: {e}")
await asyncio.sleep(60)
async def _handle_memory_pressure(self, memory_stats: MemoryStats):
"""Handle high memory pressure situations."""
logger.warning(f"High memory pressure detected: {memory_stats.pressure_level}")
# Clear non-essential caches
await self._clear_non_essential_caches()
# Shrink object pools
self.object_pool.shrink_pools(factor=0.5)
# Force garbage collection
self.gc_optimizer.force_gc()
# Temporarily reduce concurrent request limit
await self._reduce_concurrency_temporarily()
logger.info("Memory pressure mitigation applied")
class HorizontalScalingManager:
"""Manage horizontal scaling of Aurelis instances."""
def __init__(self):
self.load_monitor = LoadMonitor()
self.instance_manager = InstanceManager()
self.service_discovery = ServiceDiscovery()
async def auto_scale(self):
"""Automatically scale instances based on load."""
while True:
try:
# Monitor current load
load_metrics = await self.load_monitor.get_current_load()
# Determine scaling action
scaling_decision = self._make_scaling_decision(load_metrics)
if scaling_decision.action == "scale_up":
await self._scale_up(scaling_decision.instances)
elif scaling_decision.action == "scale_down":
await self._scale_down(scaling_decision.instances)
# Update service discovery
await self.service_discovery.update_instance_registry()
# Sleep before next scaling check
await asyncio.sleep(60) # 1-minute scaling check interval
except Exception as e:
logger.error(f"Auto-scaling error: {e}")
await asyncio.sleep(120)
def _make_scaling_decision(self, load_metrics: LoadMetrics) -> ScalingDecision:
"""Make intelligent scaling decisions based on metrics."""
current_instances = self.instance_manager.get_instance_count()
# Scale up conditions
if (load_metrics.cpu_usage > 0.7 or
load_metrics.request_queue_size > 100 or
load_metrics.response_time_p95 > 2.0):
# Calculate required instances
required_instances = self._calculate_required_instances(load_metrics)
scale_up_count = max(0, required_instances - current_instances)
if scale_up_count > 0:
return ScalingDecision(
action="scale_up",
instances=scale_up_count,
reason=f"High load detected: CPU={load_metrics.cpu_usage:.2f}"
)
# Scale down conditions
elif (load_metrics.cpu_usage < 0.3 and
load_metrics.request_queue_size < 10 and
load_metrics.response_time_p95 < 1.0 and
current_instances > 2): # Minimum 2 instances
scale_down_count = min(
current_instances - 2, # Keep minimum instances
max(1, current_instances // 4) # Scale down gradually
)
return ScalingDecision(
action="scale_down",
instances=scale_down_count,
reason=f"Low load detected: CPU={load_metrics.cpu_usage:.2f}"
)
return ScalingDecision(action="no_change")
class MicroservicesPerformanceManager:
"""Optimize performance across microservices architecture."""
def __init__(self):
self.service_mesh = ServiceMeshManager()
self.circuit_breakers = CircuitBreakerManager()
self.load_balancers = LoadBalancerManager()
def setup_service_mesh_optimization(self):
"""Configure service mesh for optimal performance."""
# Configure load balancing
self.service_mesh.configure_load_balancing({
"algorithm": "least_request",
"health_check_interval": 10,
"health_check_timeout": 5,
"outlier_detection": {
"consecutive_errors": 5,
"interval": 30,
"base_ejection_time": 30,
"max_ejection_percent": 50
}
})
# Configure circuit breakers
self.service_mesh.configure_circuit_breakers({
"failure_threshold": 5,
"recovery_timeout": 60,
"half_open_max_calls": 3
})
# Configure retry policies
self.service_mesh.configure_retry_policies({
"max_retries": 3,
"retry_on": ["5xx", "reset", "connect-failure"],
"timeout": "30s"
})
async def optimize_service_communication(self):
"""Optimize communication between services."""
# Analyze service call patterns
call_patterns = await self._analyze_service_call_patterns()
# Optimize service placement
placement_optimization = self._optimize_service_placement(call_patterns)
if placement_optimization.benefits > 0.1: # 10% improvement threshold
await self._apply_placement_optimization(placement_optimization)
# Optimize connection pooling between services
await self._optimize_inter_service_connections(call_patterns)
class PerformanceBenchmarks:
"""Performance benchmark results and targets."""
def __init__(self):
self.benchmarks = {
"response_time": {
"p50": {"target": 0.5, "current": 0.4}, # seconds
"p95": {"target": 1.0, "current": 0.8},
"p99": {"target": 2.0, "current": 1.5}
},
"throughput": {
"requests_per_second": {"target": 1000, "current": 1200},
"tokens_per_second": {"target": 10000, "current": 12000}
},
"cache_performance": {
"hit_rate": {"target": 0.7, "current": 0.75},
"miss_latency": {"target": 0.1, "current": 0.08} # seconds
},
"resource_utilization": {
"cpu_usage": {"target": 0.7, "current": 0.6},
"memory_usage": {"target": 0.8, "current": 0.65},
"network_utilization": {"target": 0.6, "current": 0.45}
}
}
def get_performance_score(self) -> float:
"""Calculate overall performance score."""
scores = []
for category, metrics in self.benchmarks.items():
category_scores = []
for metric, values in metrics.items():
target = values["target"]
current = values["current"]
# Calculate score (1.0 = meeting target, >1.0 = exceeding)
if metric in ["hit_rate", "requests_per_second", "tokens_per_second"]:
# Higher is better
score = current / target
else:
# Lower is better
score = target / current
category_scores.append(min(score, 2.0)) # Cap at 2.0
category_score = sum(category_scores) / len(category_scores)
scores.append(category_score)
return sum(scores) / len(scores)
Last Updated: December 2024
Version: 2.0.0
Performance Classification: Production Optimized
Author: Gamecooler19 (Lead Developer at Kanopus)
Aurelis - Where AI meets enterprise code development