Huawei Cloud Agentic Infra: A Deep Dive into the New Paradigm for Enterprise AI Infrastructure

# Python: AICS Cluster Resource Scheduler Simulation
from dataclasses import dataclass
from typing import List, Dict, Optional
from enum import Enum
import asyncio
from heapq import heappush, heappop

class NodeStatus(Enum):
    IDLE = "idle"
    RUNNING = "running"
    DRAINING = "draining"
    MAINTENANCE = "maintenance"

class WorkloadPriority(Enum):
    LOW = 3
    NORMAL = 2
    HIGH = 1
    CRITICAL = 0

@dataclass
class ComputeNode:
    node_id: str
    gpu_count: int
    gpu_memory_gb: int
    bandwidth_gbps: float
    status: NodeStatus = NodeStatus.IDLE
    current_load: float = 0.0
    
    def can_allocate(self, required_gpus: int, required_memory: int) -> bool:
        """Check if node can satisfy resource requirements"""
        return (self.status == NodeStatus.IDLE and
                self.gpu_count >= required_gpus and
                self.gpu_memory_gb >= required_memory)
    
    def allocation_score(self, required_gpus: int, priority: WorkloadPriority) -> float:
        """Calculate allocation score for scheduling decision"""
        # Prefer nodes with better bandwidth for high-priority tasks
        bandwidth_factor = self.bandwidth_gbps / 800.0
        # Prefer less loaded nodes for load balancing
        load_factor = 1.0 - self.current_load
        # Priority boost for critical workloads
        priority_factor = 1.0 / (priority.value + 1)
        
        return (0.4 * bandwidth_factor + 
                0.4 * load_factor + 
                0.2 * priority_factor)

@dataclass
class TaskRequest:
    task_id: str
    required_gpus: int
    required_memory: int
    priority: WorkloadPriority
    estimated_duration_sec: float
    gang_members: int = 1  # For gang scheduling
    
    def __lt__(self, other):
        # Priority queue: lower priority value = higher priority
        return self.priority.value < other.priority.value

class AICSIntelligentScheduler:
    """
    AICS Intelligent Scheduler
    
    Key Features:
    - Gang Scheduling: All-or-Nothing for distributed training
    - Bin-packing: Compact allocation to reduce fragmentation
    - DRFQ: Dominant Resource Fairness for multi-tenant environments
    - Load-aware: Real-time load consideration
    """
    
    def __init__(self):
        self.nodes: Dict[str, ComputeNode] = {}
        self.task_queue: List[TaskRequest] = []
        self.running_tasks: Dict[str, str] = {}  # task_id -> node_id
        self.task_history: List[Dict] = []
        
    def register_node(self, node: ComputeNode) -> None:
        """Register a compute node with the cluster"""
        self.nodes[node.node_id] = node
        print(f"[AICS] Registered node {node.node_id}: "
              f"{node.gpu_count} GPUs, {node.bandwidth_gbps} Gbps")
    
    async def submit_task(self, task: TaskRequest) -> Optional[str]:
        """
        Submit task and automatically schedule to optimal node
        
        Scheduling Algorithm:
        1. Priority-based queue ordering
        2. Resource matching with gang scheduling support
        3. Affinity optimization for intra-task GPU communication
        """
        # Find suitable nodes
        candidates = []
        for node_id, node in self.nodes.items():
            if node.can_allocate(task.required_gpus, task.required_memory):
                # For gang scheduling, check if we can allocate contiguous GPUs
                if task.gang_members > 1:
                    if not self._check_gpu_affinity(node, task.required_gpus, 
                                                     task.gang_members):
                        continue
                
                score = node.allocation_score(task.required_gpus, task.priority)
                candidates.append((node_id, score, node))
        
        if not candidates:
            # No suitable node - add to queue
            heappush(self.task_queue, task)
            print(f"[AICS] Task {task.task_id} queued (no suitable node)")
            return None
        
        # Select best candidate
        candidates.sort(key=lambda x: x[1], reverse=True)
        selected_node_id = candidates[0][0]
        
        return await self._allocate_task(task, selected_node_id)
    
    async def _allocate_task(self, task: TaskRequest, node_id: str) -> str:
        """Execute task allocation"""
        node = self.nodes[node_id]
        node.status = NodeStatus.RUNNING
        node.current_load = min(1.0, node.current_load + 
                                task.required_gpus / node.gpu_count)
        self.running_tasks[task.task_id] = node_id
        
        print(f"[AICS] Task {task.task_id} allocated to {node_id}")
        print(f"[AICS] Estimated completion: {task.estimated_duration_sec}s")
        
        # Log task submission
        self.task_history.append({
            "task_id": task.task_id,
            "node_id": node_id,
            "timestamp": asyncio.get_event_loop().time(),
            "priority": task.priority.name
        })
        
        return node_id
    
    def _check_gpu_affinity(self, node: ComputeNode, 
                            gpus_per_task: int, 
                            gang_size: int) -> bool:
        """
        Check GPU affinity for distributed training
        
        GPUs connected via NVSwitch provide all-to-all connectivity
        GPUs on same PCIe switch have high bandwidth
        """
        available = node.gpu_count
        # Simplified: just check if we have enough contiguous GPUs
        return available >= gpus_per_task * gang_size
    
    def get_cluster_metrics(self) -> Dict:
        """Get comprehensive cluster metrics"""
        total_gpus = sum(n.gpu_count for n in self.nodes.values())
        running_gpus = sum(
            n.gpu_count * n.current_load for n in self.nodes.values()
        )
        
        return {
            "total_nodes": len(self.nodes),
            "total_gpus": total_gpus,
            "gpu_utilization": running_gpus / total_gpus if total_gpus > 0 else 0,
            "queued_tasks": len(self.task_queue),
            "running_tasks": len(self.running_tasks),
            "average_load": sum(n.current_load for n in self.nodes.values()) / 
                           len(self.nodes) if self.nodes else 0
        }
    
    async def process_queue(self):
        """Process queued tasks as nodes become available"""
        while self.task_queue:
            # Sort queue by priority
            sorted_queue = sorted(self.task_queue)
            
            for task in sorted_queue:
                node_id = await self.submit_task(task)
                if node_id:
                    self.task_queue.remove(task)
                    break
            
            await asyncio.sleep(0.1)  # Prevent tight loop

async def demo_aics_cluster():
    """Demonstrate AICS cluster scheduling"""
    scheduler = AICSIntelligentScheduler()
    
    # Register a large-scale cluster (simulated)
    for i in range(50):
        node = ComputeNode(
            node_id=f"compute-node-{i:03d}",
            gpu_count=8,
            gpu_memory_gb=640,  # 80GB per GPU
            bandwidth_gbps=800.0,
            status=NodeStatus.IDLE
        )
        scheduler.register_node(node)
    
    # Submit diverse AI workloads
    workloads = [
        # Distributed training jobs (gang scheduling)
        TaskRequest("train-llm-001", required_gpus=32, required_memory=2560,
                   priority=WorkloadPriority.HIGH, 
                   estimated_duration_sec=3600.0, gang_members=4),
        
        # Batch inference jobs
        TaskRequest("infer-batch-001", required_gpus=8, required_memory=640,
                   priority=WorkloadPriority.NORMAL,
                   estimated_duration_sec=600.0),
        
        # Real-time inference services
        TaskRequest("infer-realtime-001", required_gpus=4, required_memory=320,
                   priority=WorkloadPriority.CRITICAL,
                   estimated_duration_sec=0.0),  # Long-running service
        
        # Fine-tuning tasks
        TaskRequest("finetune-001", required_gpus=16, required_memory=1280,
                   priority=WorkloadPriority.NORMAL,
                   estimated_duration_sec=1800.0),
    ]
    
    print("\n" + "="*60)
    print("AICS Cluster Scheduling Demo")
    print("="*60 + "\n")
    
    for workload in workloads:
        await scheduler.submit_task(workload)
    
    # Display cluster metrics
    metrics = scheduler.get_cluster_metrics()
    print(f"\n[AICS] Cluster Metrics:")
    print(f"  Total GPUs: {metrics['total_gpus']}")
    print(f"  GPU Utilization: {metrics['gpu_utilization']:.2%}")
    print(f"  Queued Tasks: {metrics['queued_tasks']}")
    print(f"  Running Tasks: {metrics['running_tasks']}")

asyncio.run(demo_aics_cluster())

# Python: AMS Long-Horizon Memory Management
from dataclasses import dataclass, field
from typing import List, Dict, Optional, Any
from datetime import datetime, timedelta
from enum import Enum
import json
import hashlib

class MemoryTier(Enum):
    """Memory hierarchy levels"""
    WORKING = "working"        # Current session context
    EPISODIC = "episodic"      # Event sequences
    SEMANTIC = "semantic"      # Knowledge graph
    LONG_TERM = "long_term"   # Persistent storage

@dataclass
class MemoryEntry:
    """Single memory entry with rich metadata"""
    entry_id: str
    content: str
    tier: MemoryTier
    embedding: List[float] = field(default_factory=list)
    importance: float = 0.5  # 0.0 to 1.0
    created_at: datetime = field(default_factory=datetime.now)
    last_accessed: datetime = field(default_factory=datetime.now)
    access_count: int = 0
    metadata: Dict[str, Any] = field(default_factory=dict)
    
    def access(self) -> None:
        """Update access statistics"""
        self.last_accessed = datetime.now()
        self.access_count += 1
        # Boost importance for frequently accessed memories
        self.importance = min(1.0, self.importance + 0.01)

@dataclass
class MemoryCheckpoint:
    """Checkpoint for long-horizon task state"""
    checkpoint_id: str
    task_id: str
    timestamp: datetime
    state: Dict[str, Any]
    memories: List[str]  # Memory IDs included
    reflection: str  # Agent's self-reflection

class AgentMemoryStore:
    """
    Agentic Memory Store - Core Interface
    
    Design Philosophy:
    - "Memory is Context, Context is Intelligence"
    - Multi-tier architecture for cost-performance balance
    - Importance-weighted retention and retrieval
    """
    
    def __init__(self, vector_dim: int = 1536):
        self.vector_dim = vector_dim
        
        # Storage layers (simplified simulation)
        self.working_memory: Dict[str, MemoryEntry] = {}  # In-memory
        self.episodic_memory: Dict[str, MemoryEntry] = {}  # Redis-like
        self.semantic_memory: Dict[str, MemoryEntry] = {}   # Vector DB
        self.long_term_memory: Dict[str, MemoryEntry] = {} # Distributed storage
        
        # Index structures
        self.embedding_index: Dict[str, List[float]] = {}
        self.importance_index: List[str] = []  # Sorted by importance
        
    def store(self, content: str, tier: MemoryTier, 
              metadata: Optional[Dict] = None) -> MemoryEntry:
        """Store a new memory entry"""
        entry_id = self._generate_id(content)
        
        entry = MemoryEntry(
            entry_id=entry_id,
            content=content,
            tier=tier,
            metadata=metadata or {}
        )
        
        # Generate embedding (simplified)
        entry.embedding = self._generate_embedding(content)
        
        # Store in appropriate tier
        storage = self._get_storage(tier)
        storage[entry_id] = entry
        
        # Update indices
        self.embedding_index[entry_id] = entry.embedding
        self._update_importance_index(entry)
        
        print(f"[AMS] Stored memory {entry_id[:8]}... in {tier.value} tier")
        return entry
    
    def retrieve(self, query: str, top_k: int = 5,
                 tiers: Optional[List[MemoryTier]] = None) -> List[MemoryEntry]:
        """
        Retrieve relevant memories using hybrid search
        
        Combines:
        1. Semantic similarity (vector search)
        2. Recency boost
        3. Importance weighting
        """
        query_embedding = self._generate_embedding(query)
        candidates = []
        
        # Search across specified tiers (default: all)
        search_tiers = tiers or list(MemoryTier)
        
        for tier in search_tiers:
            storage = self._get_storage(tier)
            for entry in storage.values():
                similarity = self._cosine_similarity(
                    query_embedding, entry.embedding
                )
                
                # Composite score: similarity + recency + importance
                recency_score = self._recency_score(entry)
                importance_weight = entry.importance
                
                composite_score = (
                    0.5 * similarity + 
                    0.3 * recency_score + 
                    0.2 * importance_weight
                )
                
                candidates.append((entry, composite_score))
        
        # Sort by composite score and return top-k
        candidates.sort(key=lambda x: x[1], reverse=True)
        results = [entry for entry, _ in candidates[:top_k]]
        
        # Update access statistics
        for entry in results:
            entry.access()
        
        return results
    
    def create_checkpoint(self, task_id: str, state: Dict,
                         reflection: str) -> MemoryCheckpoint:
        """Create a task checkpoint for recovery"""
        # Gather all relevant memories for this task
        task_memories = [
            entry.entry_id for entry in self.working_memory.values()
            if entry.metadata.get("task_id") == task_id
        ]
        
        checkpoint = MemoryCheckpoint(
            checkpoint_id=f"cp_{task_id}_{len(task_memories)}",
            task_id=task_id,
            timestamp=datetime.now(),
            state=state,
            memories=task_memories,
            reflection=reflection
        )
        
        print(f"[AMS] Created checkpoint {checkpoint.checkpoint_id}")
        return checkpoint
    
    def compress_memories(self, session_id: str, 
                         max_memories: int = 100,
                         min_importance: float = 0.3) -> int:
        """
        Compress memories to prevent context overflow
        
        Strategy:
        1. Remove low-importance memories
        2. Merge similar memories
        3. Retain recent memories
        4. Enforce maximum count
        """
        session_memories = [
            entry for entry in self.working_memory.values()
            if entry.metadata.get("session_id") == session_id
        ]
        
        initial_count = len(session_memories)
        
        # Filter by importance
        important_memories = [
            m for m in session_memories 
            if m.importance >= min_importance
        ]
        
        # Sort by importance and recency
        important_memories.sort(
            key=lambda m: (m.importance, m.last_accessed),
            reverse=True
        )
        
        # Truncate to max
        retained_memories = important_memories[:max_memories]
        
        # Update storage
        to_remove = set(m.entry_id for m in session_memories) - \
                   set(m.entry_id for m in retained_memories)
        
        for entry_id in to_remove:
            del self.working_memory[entry_id]
        
        removed_count = initial_count - len(retained_memories)
        print(f"[AMS] Compressed {removed_count} memories, "
              f"retained {len(retained_memories)}")
        
        return removed_count
    
    def _generate_id(self, content: str) -> str:
        """Generate unique memory ID"""
        return hashlib.sha256(
            f"{content}{datetime.now().isoformat()}".encode()
        ).hexdigest()
    
    def _generate_embedding(self, text: str) -> List[float]:
        """Generate embedding vector (simplified)"""
        # In production: use proper embedding model
        import random
        return [random.random() for _ in range(self.vector_dim)]
    
    def _cosine_similarity(self, a: List[float], b: List[float]) -> float:
        """Calculate cosine similarity between vectors"""
        dot = sum(x * y for x, y in zip(a, b))
        norm_a = sum(x * x for x in a) ** 0.5
        norm_b = sum(x * x for x in b) ** 0.5
        return dot / (norm_a * norm_b + 1e-8)
    
    def _recency_score(self, entry: MemoryEntry) -> float:
        """Calculate recency score (0-1)"""
        age = datetime.now() - entry.last_accessed
        # Exponential decay with 1-hour half-life
        return 0.5 ** (age.total_seconds() / 3600)
    
    def _get_storage(self, tier: MemoryTier) -> Dict[str, MemoryEntry]:
        """Get storage dict for memory tier"""
        return {
            MemoryTier.WORKING: self.working_memory,
            MemoryTier.EPISODIC: self.episodic_memory,
            MemoryTier.SEMANTIC: self.semantic_memory,
            MemoryTier.LONG_TERM: self.long_term_memory,
        }[tier]
    
    def _update_importance_index(self, entry: MemoryEntry):
        """Update importance-sorted index"""
        # In production: use proper priority queue
        if entry.entry_id not in self.importance_index:
            self.importance_index.append(entry.entry_id)
        self.importance_index.sort(
            key=lambda e: self.working_memory.get(e, MemoryEntry("", "", MemoryTier.WORKING)).importance,
            reverse=True
        )

# Demonstration: Long-horizon task with memory
async def demo_long_horizon_task():
    """
    Demonstrate long-horizon task with memory management
    
    Simulates a multi-day data analysis project where
    the agent needs to maintain context across sessions
    """
    memory_store = AgentMemoryStore()
    
    print("="*60)
    print("Long-Horizon Task Memory Demo")
    print("="*60 + "\n")
    
    # Session 1: Initial data collection
    print("Session 1: Data Collection")
    memory_store.store(
        "Collected 5 data sources totaling 1.2TB of raw data",
        tier=MemoryTier.EPISODIC,
        metadata={"task": "data_analysis", "session": 1}
    )
    memory_store.store(
        "Data quality looks good, but need to supplement with third-party data",
        tier=MemoryTier.SEMANTIC,
        metadata={"task": "data_analysis", "insight": True}
    )
    
    # Session 2: Data cleaning
    print("\nSession 2: Data Cleaning")
    memory_store.store(
        "Completed data cleaning, quality score 0.85, data reduced to 800GB",
        tier=MemoryTier.EPISODIC,
        metadata={"task": "data_analysis", "session": 2}
    )
    
    # Retrieve relevant context before continuing
    context = memory_store.retrieve("What data have we collected?", top_k=3)
    print(f"\nRetrieved context: {len(context)} relevant memories")
    
    # Create checkpoint for potential recovery
    checkpoint = memory_store.create_checkpoint(
        task_id="data_analysis",
        state={"phase": "cleaning", "quality_score": 0.85},
        reflection="Data quality is acceptable. Need to handle missing values."
    )
    
    # Session 3: Feature engineering
    print("\nSession 3: Feature Engineering")
    memory_store.store(
        "Completed 156 feature constructions including temporal and cross features",
        tier=MemoryTier.EPISODIC,
        metadata={"task": "data_analysis", "session": 3}
    )
    
    # Query overall task progress
    progress_context = memory_store.retrieve(
        "data analysis project progress",
        top_k=5
    )
    
    print(f"\nTask Progress Summary:")
    for entry in progress_context:
        print(f"  - [{entry.tier.value}] {entry.content[:60]}...")
    
    # Memory compression check
    memory_store.compress_memories(
        session_id="data_analysis",
        max_memories=50,
        min_importance=0.4
    )

asyncio.run(demo_long_horizon_task())

# Python: VolcanoNext Scheduling Strategy Implementation
from typing import List, Tuple, Dict, Optional
from dataclasses import dataclass
from enum import Enum
import numpy as np

class WorkloadCategory(Enum):
    TRAINING = "training"       # Distributed training
    INFERENCE = "inference"     # Real-time inference
    FINE_TUNING = "fine_tuning" # Model fine-tuning
    BATCH = "batch"            # Batch processing

@dataclass
class SchedulingDecision:
    """Scheduling decision result"""
    pod_id: str
    node_id: str
    gpu_allocation: List[int]
    composite_score: float
    wait_time: float

@dataclass
class SchedulingPolicy:
    """Configurable scheduling policy weights"""
    utilization_weight: float = 0.30
    affinity_weight: float = 0.25
    locality_weight: float = 0.20
    fairness_weight: float = 0.15
    wait_time_weight: float = 0.10

class VolcanoScheduler:
    """
    VolcanoNext Intelligent Scheduler
    
    Core Algorithms:
    1. Gang Scheduling: All-or-Nothing for distributed training
    2. Bin-packing: Compact allocation to minimize fragmentation
    3. DRFQ: Dominant Resource Fairness for multi-tenant
    4. Load-aware: Consider real-time node load
    5. Topology-aware: NVLink/NVSwitch optimization
    """
    
    def __init__(self, cluster_state: Dict, policy: Optional[SchedulingPolicy] = None):
        self.cluster = cluster_state
        self.policy = policy or SchedulingPolicy()
        self.pending_queue: List[Dict] = []
        self.schedule_history: List[SchedulingDecision] = []
        
    def schedule(self, pod_request: Dict) -> Optional[SchedulingDecision]:
        """
        Main scheduling entry point
        
        Algorithm Flow:
        1. Pre-filter: Remove nodes that can't satisfy requirements
        2. Score: Multi-dimensional scoring of candidates
        3. Allocate: Select highest-scoring node
        """
        workload_type = WorkloadCategory(pod_request.get("workload_type"))
        required_gpus = pod_request.get("required_gpus", 0)
        
        # Phase 1: Pre-filtering
        candidates = self._pre_filter(pod_request)
        if not candidates:
            self.pending_queue.append(pod_request)
            print(f"[VolcanoNext] No suitable nodes for {pod_request['pod_id']}, queued")
            return None
        
        # Phase 2: Multi-dimensional scoring
        scored_candidates = []
        for node in candidates:
            score = self._compute_composite_score(
                node, pod_request, workload_type
            )
            scored_candidates.append((node, score))
        
        # Phase 3: Select optimal node
        scored_candidates.sort(key=lambda x: x[1], reverse=True)
        selected_node, best_score = scored_candidates[0]
        
        # Phase 4: Execute allocation
        decision = self._allocate(pod_request, selected_node, best_score)
        return decision
    
    def _pre_filter(self, request: Dict) -> List[Dict]:
        """Filter nodes that can satisfy requirements"""
        required_gpus = request.get("required_gpus", 0)
        required_memory = request.get("required_memory", 0)
        
        candidates = []
        for node in self.cluster["nodes"].values():
            if (node["available_gpus"] >= required_gpus and
                node["available_memory"] >= required_memory and
                node["status"] == "healthy"):
                
                # Gang scheduling validation for training
                if request.get("workload_type") == "training":
                    if not self._validate_gang_scheduling(node, required_gpus):
                        continue
                
                candidates.append(node)
        
        return candidates
    
    def _validate_gang_scheduling(self, node: Dict, 
                                  required: int) -> bool:
        """Validate if node can support gang scheduling"""
        available = node["available_gpu_ids"]
        if len(available) < required:
            return False
        
        # Check for contiguous GPU allocation
        sorted_gpus = sorted(available)
        for i in range(len(sorted_gpus) - required + 1):
            if sorted_gpus[i + required - 1] - sorted_gpus[i] == required - 1:
                return True
        return False
    
    def _compute_composite_score(self, node: Dict, request: Dict,
                                 workload: WorkloadCategory) -> float:
        """
        Compute multi-dimensional composite score
        
        Score Components:
        - Utilization: Prefer balanced load distribution
        - Affinity: Training prefers same-switch GPUs
        - Locality: Inference prefers GPU-direct memory
        - Fairness: DRFQ-based fairness
        - Wait Time: Priority boost for waiting tasks
        """
        scores = {}
        
        # Utilization score (prefer 70% utilization target)
        gpu_util = node["gpu_utilization"]
        scores["utilization"] = 1.0 - abs(gpu_util - 0.7) / 0.7
        
        # Affinity score (training workloads)
        if workload == WorkloadCategory.TRAINING:
            scores["affinity"] = self._compute_affinity_score(node, request)
        else:
            scores["affinity"] = 0.5  # Neutral for non-training
        
        # Memory locality score (inference workloads)
        if workload == WorkloadCategory.INFERENCE:
            scores["locality"] = node.get("gpu_direct_memory_ratio", 0.5)
        else:
            scores["locality"] = 0.5
        
        # Fairness score (DRFQ)
        scores["fairness"] = self._compute_fairness_score(node, request)
        
        # Wait time compensation
        wait_time = request.get("wait_time", 0)
        scores["wait_time"] = min(wait_time / 300, 1.0)  # Max 5-minute boost
        
        # Weighted composite
        w = self.policy
        composite = (
            w.utilization_weight * scores["utilization"] +
            w.affinity_weight * scores["affinity"] +
            w.locality_weight * scores["locality"] +
            w.fairness_weight * scores["fairness"] +
            w.wait_time_weight * scores["wait_time"]
        )
        
        return composite
    
    def _compute_affinity_score(self, node: Dict, 
                                request: Dict) -> float:
        """Compute GPU affinity score for training workloads"""
        # Simplified: prefer nodes with more available GPUs
        # Production: consider NVLink topology, switch domain
        gpu_count = node["available_gpus"]
        required = request.get("required_gpus", 1)
        
        if gpu_count == required:
            return 1.0  # Perfect fit
        elif gpu_count > required * 2:
            return 0.7  # Some waste
        else:
            return 0.9
    
    def _compute_fairness_score(self, node: Dict, 
                                request: Dict) -> float:
        """
        Compute DRFQ-based fairness score
        
        Dominant Resource Fairness ensures each tenant gets
        fair share of their dominant resource
        """
        node_share = node["gpu_utilization"]
        # Prefer underutilized nodes for fairness
        return 1.0 - node_share
    
    def _allocate(self, request: Dict, node: Dict,
                  score: float) -> SchedulingDecision:
        """Execute resource allocation"""
        required_gpus = request.get("required_gpus", 0)
        allocated_gpus = node["available_gpu_ids"][:required_gpus]
        
        # Update node state
        node["available_gpus"] -= required_gpus
        node["available_memory"] -= request.get("required_memory", 0)
        node["gpu_utilization"] = (
            (node["gpu_count"] - node["available_gpus"]) / node["gpu_count"]
        )
        node["available_gpu_ids"] = node["available_gpu_ids"][required_gpus:]
        
        decision = SchedulingDecision(
            pod_id=request["pod_id"],
            node_id=node["node_id"],
            gpu_allocation=allocated_gpus,
            composite_score=score,
            wait_time=request.get("wait_time", 0)
        )
        
        self.schedule_history.append(decision)
        return decision

# Scheduling Comparison Demonstration
def demo_scheduling_comparison():
    """Compare VolcanoNext vs Traditional Scheduler"""
    print("="*60)
    print("VolcanoNext vs Traditional Scheduling")
    print("="*60 + "\n")
    
    # Simulated cluster
    cluster = {
        "nodes": {
            f"node-{i}": {
                "node_id": f"node-{i}",
                "gpu_count": 8,
                "available_gpus": 8,
                "available_memory": 640,
                "gpu_utilization": 0.5,
                "gpu_direct_memory_ratio": 0.9,
                "status": "healthy",
                "available_gpu_ids": list(range(8))
            }
            for i in range(8)
        }
    }
    
    # Diverse workload mix
    workloads = [
        {"pod_id": "train-dist-1", "workload_type": "training",
         "required_gpus": 8, "priority": "high"},
        {"pod_id": "infer-rt-1", "workload_type": "inference",
         "required_gpus": 1, "priority": "critical"},
        {"pod_id": "train-dist-2", "workload_type": "training",
         "required_gpus": 4, "priority": "high"},
        {"pod_id": "infer-batch-1", "workload_type": "inference",
         "required_gpus": 2, "priority": "normal"},
        {"pod_id": "finetune-1", "workload_type": "fine_tuning",
         "required_gpus": 2, "priority": "normal"},
        {"pod_id": "batch-proc-1", "workload_type": "batch",
         "required_gpus": 4, "priority": "low"},
    ]
    
    scheduler = VolcanoScheduler(cluster)
    
    print("Scheduling Results:")
    print("-"*60)
    for workload in workloads:
        decision = scheduler.schedule(workload)
        if decision:
            print(f"{workload['pod_id']:15} -> {decision.node_id:10} "
                  f"(Score: {decision.composite_score:.3f})")
        else:
            print(f"{workload['pod_id']:15} -> [QUEUED]")
    
    # Cluster utilization analysis
    total_gpus = 64  # 8 nodes * 8 GPUs
    used_gpus = sum(8 - n["available_gpus"] for n in cluster["nodes"].values())
    utilization = used_gpus / total_gpus
    
    print(f"\nCluster Utilization: {utilization:.1%}")
    print(f"Queued Workloads: {len(scheduler.pending_queue)}")
    print(f"Total Scheduled: {len(scheduler.schedule_history)}")

demo_scheduling_comparison()

package agentsphere

import (
    "context"
    "fmt"
    "time"
    "sync"
    
    "github.com/google/uuid"
)

// RuntimeState Agent runtime state
type RuntimeState int

const (
    StateInitializing RuntimeState = iota
    StateReady
    StateRunning
    StatePaused
    StateTerminating
    StateTerminated
)

func (s RuntimeState) String() string {
    return []string{
        "initializing", "ready", "running", 
        "paused", "terminating", "terminated",
    }[s]
}

// Sandbox security configuration
type SandboxConfig struct {
    // Resource limits
    CPUPeriod       int64  // CPU quota period (microseconds)
    CPUQuota        int64  // CPU quota
    MemoryLimit     int64  // Memory limit (bytes)
    GPULimit        float64 // GPU quota (fraction)
    
    // Network isolation
    NetworkMode     string // "bridge", "host", "container"
    AllowedPorts    []int  // Allowed inbound ports
    
    // Security profiles
    SeccompProfile  string // Seccomp profile path
    ApparmorProfile string // AppArmor profile
    
    // Capabilities
    DropCapabilities []string // Capabilities to drop
    AddCapabilities  []string // Capabilities to add (rare)
}

// SecurityPolicy Security policy definition
type SecurityPolicy struct {
    mu sync.RWMutex
    
    // Tool whitelist
    allowedTools map[string]bool
    
    // Resource access limits
    MaxMemoryAccess    int64 // Max memory bytes per operation
    MaxNetworkCalls    int   // Max outbound calls per minute
    MaxFileOperations   int   // Max file operations per minute
    
    // Data boundaries
    AllowDataExfiltration bool
    AllowedDataTypes      []string
    
    // Audit configuration
    AuditLevel       AuditLevel
    AuditLogEndpoint string
}

type AuditLevel int

const (
    AuditNone AuditLevel = iota
    AuditErrors
    AuditWarnings
    AuditAll
)

// AgentRuntime Core agent runtime
type AgentRuntime struct {
    runtimeID string
    
    // Core components
    sandbox        *Sandbox
    memoryManager  *MemoryManager
    toolRegistry   *ToolRegistry
    securityPolicy *SecurityPolicy
    monitor        *RuntimeMonitor
    
    // State
    state     RuntimeState
    stateMu   sync.RWMutex
    
    // Execution context
    currentContext *ExecutionContext
}

// ToolCall represents a tool invocation request
type ToolCall struct {
    ToolName      string
    Arguments     map[string]interface{}
    Timeout       time.Duration
    CorrelationID string // For distributed tracing
}

// ToolResult represents tool execution result
type ToolResult struct {
    Success    bool
    Output     interface{}
    Error      error
    Duration   time.Duration
    AuditTrail []AuditEntry
}

// CreateRuntime creates a new agent runtime
func (s *AgentSphere) CreateRuntime(ctx context.Context, 
    config *RuntimeConfig) (*AgentRuntime, error) {
    
    runtimeID := uuid.New().String()
    
    // 1. Create isolated sandbox
    sandbox, err := s.createSandbox(ctx, &config.Sandbox)
    if err != nil {
        return nil, fmt.Errorf("sandbox creation failed: %w", err)
    }
    
    // 2. Initialize security policy
    securityPolicy := s.createSecurityPolicy(config.SecurityLevel)
    
    // 3. Create runtime instance
    runtime := &AgentRuntime{
        runtimeID:      runtimeID,
        sandbox:        sandbox,
        memoryManager:  NewMemoryManager(),
        toolRegistry:   s.globalRegistry.Clone(),
        securityPolicy: securityPolicy,
        monitor:        NewMonitor(runtimeID),
        state:          StateInitializing,
    }
    
    // 4. Start monitoring
    go runtime.monitor.Start(ctx)
    
    // 5. Health check
    if err := runtime.healthCheck(ctx); err != nil {
        runtime.Cleanup(ctx)
        return nil, fmt.Errorf("health check failed: %w", err)
    }
    
    runtime.setState(StateReady)
    return runtime, nil
}

// ExecuteTool safely executes a tool within the sandbox
func (r *AgentRuntime) ExecuteTool(ctx context.Context, 
    call *ToolCall) (*ToolResult, error) {
    
    // 1. Security validation
    if err := r.securityPolicy.Validate(call); err != nil {
        r.monitor.RecordSecurityEvent("tool_rejected", call.ToolName)
        return nil, fmt.Errorf("security policy violation: %w", err)
    }
    
    // 2. Tool resolution
    tool := r.toolRegistry.Get(call.ToolName)
    if tool == nil {
        return nil, fmt.Errorf("tool not found: %s", call.ToolName)
    }
    
    // 3. Resource check
    if err := r.checkResources(call); err != nil {
        return nil, fmt.Errorf("resource limit exceeded: %w", err)
    }
    
    // 4. Execute within sandbox
    start := time.Now()
    result, err := r.sandbox.Execute(ctx, tool, call.Arguments)
    duration := time.Since(start)
    
    // 5. Record monitoring metrics
    r.monitor.RecordToolExecution(call.ToolName, duration, err == nil)
    
    // 6. Build result with audit trail
    toolResult := &ToolResult{
        Success:  err == nil,
        Output:   result,
        Error:    err,
        Duration: duration,
        AuditTrail: r.monitor.GetRecentAudit(call.CorrelationID),
    }
    
    return toolResult, nil
}

// Validate performs security validation for tool call
func (p *SecurityPolicy) Validate(call *ToolCall) error {
    p.mu.RLock()
    defer p.mu.RUnlock()
    
    // Check tool whitelist
    if !p.allowedTools[call.ToolName] {
        return fmt.Errorf("tool %s not in whitelist", call.ToolName)
    }
    
    // Additional validation can be added here
    return nil
}

// Cleanup terminates the runtime and releases resources
func (r *AgentRuntime) Cleanup(ctx context.Context) {
    r.setState(StateTerminating)
    
    // 1. Stop monitoring
    r.monitor.Stop()
    
    // 2. Destroy sandbox
    if r.sandbox != nil {
        r.sandbox.Destroy(ctx)
    }
    
    // 3. Cleanup memory
    r.memoryManager.Cleanup()
    
    r.setState(StateTerminated)
}

func (r *AgentRuntime) setState(state RuntimeState) {
    r.stateMu.Lock()
    defer r.stateMu.Unlock()
    r.state = state
}

# Python: ModelArtsNext Intelligent Model Router
from typing import List, Dict, Optional
from dataclasses import dataclass
from enum import Enum

class TaskType(Enum):
    GENERATION = "generation"
    REASONING = "reasoning"
    CLASSIFICATION = "classification"
    EXTRACTION = "extraction"
    SUMMARIZATION = "summarization"

class ModelComplexity(Enum):
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"

@dataclass
class ModelSpec:
    """Model specification with performance characteristics"""
    model_id: str
    model_name: str
    capabilities: List[TaskType]
    cost_per_1k_tokens: float
    latency_p50_ms: float
    accuracy_score: float
    max_context_tokens: int
    version: str

class ModelRouter:
    """
    ModelArtsNext Intelligent Model Router
    
    Routing Strategy:
    1. Task-Model Matching: Match task type to model capability
    2. Cost Optimization: Select lowest-cost model meeting requirements
    3. Load Balancing: Consider current model load
    4. Fallback Strategy: Automatic failover on model unavailability
    """
    
    def __init__(self, model_catalog: List[ModelSpec]):
        self.catalog = {m.model_id: m for m in model_catalog}
        self.usage_stats = {
            m.model_id: {"requests": 0, "errors": 0, "avg_latency": 0}
            for m in model_catalog
        }
        self.request_history: List[Dict] = []
    
    def route(self, request: Dict) -> str:
        """
        Intelligent routing decision
        
        Request format:
        {
            "task_type": TaskType,
            "complexity": ModelComplexity,
            "max_latency_ms": float,
            "max_cost_per_1k": float,
            "context_length": int,
            "quality_preference": float  # 0.0 to 1.0
        }
        """
        task_type = request.get("task_type", TaskType.GENERATION)
        complexity = request.get("complexity", ModelComplexity.MEDIUM)
        max_latency = request.get("max_latency_ms", float('inf'))
        max_cost = request.get("max_cost_per_1k", float('inf'))
        context_length = request.get("context_length", 0)
        quality_pref = request.get("quality_preference", 0.5)
        
        # Filter eligible models
        candidates = self._filter_models(
            task_type, context_length, max_latency, max_cost
        )
        
        if not candidates:
            return self._get_default_model()
        
        # Score candidates
        scored_models = [
            (model_id, self._compute_score(model_id, request, complexity))
            for model_id in candidates
        ]
        
        # Select best model
        scored_models.sort(key=lambda x: x[1], reverse=True)
        selected = scored_models[0][0]
        
        # Update statistics
        self._record_request(selected, request)
        
        return selected
    
    def _filter_models(self, task_type: TaskType, context_length: int,
                      max_latency: float, max_cost: float) -> List[str]:
        """Filter models meeting hard requirements"""
        candidates = []
        
        for model_id, model in self.catalog.items():
            # Capability match
            if task_type not in model.capabilities:
                continue
            
            # Context length
            if context_length > model.max_context_tokens:
                continue
            
            # Latency constraint
            if model.latency_p50_ms > max_latency:
                continue
            
            # Cost constraint
            if model.cost_per_1k_tokens > max_cost:
                continue
            
            candidates.append(model_id)
        
        return candidates
    
    def _compute_score(self, model_id: str, request: Dict,
                      complexity: ModelComplexity) -> float:
        """Compute composite routing score"""
        model = self.catalog[model_id]
        quality_pref = request.get("quality_preference", 0.5)
        
        # Latency score (lower is better)
        latency_score = 1.0 - min(model.latency_p50_ms / 500, 1.0)
        
        # Cost score (lower is better)
        cost_score = 1.0 - min(model.cost_per_1k_tokens / 0.1, 1.0)
        
        # Accuracy score
        accuracy_score = model.accuracy_score
        
        # Complexity match bonus
        complexity_match = self._complexity_match_score(model, complexity)
        
        # Weighted combination
        # For high quality preference: emphasize accuracy
        # For low quality preference: emphasize cost/latency
        if quality_pref > 0.7:
            weights = {"latency": 0.2, "cost": 0.2, "accuracy": 0.4, "match": 0.2}
        elif quality_pref < 0.3:
            weights = {"latency": 0.4, "cost": 0.4, "accuracy": 0.1, "match": 0.1}
        else:
            weights = {"latency": 0.3, "cost": 0.25, "accuracy": 0.3, "match": 0.15}
        
        return (
            weights["latency"] * latency_score +
            weights["cost"] * cost_score +
            weights["accuracy"] * accuracy_score +
            weights["match"] * complexity_match
        )
    
    def _complexity_match_score(self, model: ModelSpec,
                                 complexity: ModelComplexity) -> float:
        """Score based on model-complexity match"""
        # Simple heuristic: larger models for higher complexity
        model_tier = "large" if model.max_context_tokens > 16000 else "medium"
        model_tier = "small" if model.max_context_tokens < 8000 else model_tier
        
        expected_tier = {
            ModelComplexity.LOW: "small",
            ModelComplexity.MEDIUM: "medium",
            ModelComplexity.HIGH: "large"
        }[complexity]
        
        return 1.0 if model_tier == expected_tier else 0.6
    
    def _get_default_model(self) -> str:
        """Return default fallback model"""
        return "pangu-lite"
    
    def _record_request(self, model_id: str, request: Dict):
        """Record request for analytics"""
        self.usage_stats[model_id]["requests"] += 1
        self.request_history.append({
            "model_id": model_id,
            "task_type": request.get("task_type"),
            "timestamp": "now"
        })

def demo_model_routing():
    """Demonstrate ModelArtsNext intelligent routing"""
    # Model catalog
    models = [
        ModelSpec("pangu-pro-32k", "Pangu Pro 32K",
                 [TaskType.GENERATION, TaskType.REASONING],
                 cost_per_1k_tokens=0.05, latency_p50_ms=50,
                 accuracy_score=0.92, max_context_tokens=32768),
        ModelSpec("pangu-pro-8k", "Pangu Pro 8K",
                 [TaskType.GENERATION],
                 cost_per_1k_tokens=0.02, latency_p50_ms=30,
                 accuracy_score=0.88, max_context_tokens=8192),
        ModelSpec("pangu-lite", "Pangu Lite",
                 [TaskType.GENERATION, TaskType.CLASSIFICATION],
                 cost_per_1k_tokens=0.01, latency_p50_ms=20,
                 accuracy_score=0.85, max_context_tokens=4096),
        ModelSpec("ernie-enterprise", "ERNIE Enterprise",
                 [TaskType.GENERATION, TaskType.CLASSIFICATION, 
                  TaskType.SUMMARIZATION],
                 cost_per_1k_tokens=0.03, latency_p50_ms=40,
                 accuracy_score=0.90, max_context_tokens=16384),
    ]
    
    router = ModelRouter(models)
    
    print("="*60)
    print("ModelArtsNext Intelligent Routing Demo")
    print("="*60 + "\n")
    
    # Diverse routing scenarios
    scenarios = [
        {
            "name": "Low-cost generation",
            "task_type": TaskType.GENERATION,
            "complexity": ModelComplexity.LOW,
            "max_latency_ms": 50,
            "max_cost_per_1k": 0.02,
            "quality_preference": 0.3
        },
        {
            "name": "High-quality reasoning",
            "task_type": TaskType.REASONING,
            "complexity": ModelComplexity.HIGH,
            "max_latency_ms": 100,
            "max_cost_per_1k": 0.1,
            "quality_preference": 0.9
        },
        {
            "name": "Balanced classification",
            "task_type": TaskType.CLASSIFICATION,
            "complexity": ModelComplexity.MEDIUM,
            "max_latency_ms": 60,
            "max_cost_per_1k": 0.05,
            "quality_preference": 0.5
        },
    ]
    
    for scenario in scenarios:
        selected = router.route(scenario)
        model = router.catalog[selected]
        print(f"Scenario: {scenario['name']}")
        print(f"  Selected: {model.model_name}")
        print(f"  Cost: ${model.cost_per_1k_tokens:.3f}/1K tokens")
        print(f"  Latency: {model.latency_p50_ms}ms")
        print(f"  Accuracy: {model.accuracy_score:.2%}")
        print()

demo_model_routing()