The Future of Cloud Infrastructure: What's Next?

# Advanced serverless architecture with event-driven patterns
AWSTemplateFormatVersion: "2010-09-09"
Description: "Future-ready serverless infrastructure"

Resources:
  # Event-driven data processing pipeline
  DataProcessingFunction:
    Type: AWS::Lambda::Function
    Properties:
      FunctionName: data-processor
      Runtime: nodejs18.x
      Handler: index.handler
      Code:
        ZipFile: |
          exports.handler = async (event) => {
            // AI-powered data processing
            const processedData = await processWithAI(event.Records);
            return { statusCode: 200, body: JSON.stringify(processedData) };
          };
      Environment:
        Variables:
          AI_MODEL_ENDPOINT: !Ref AIModelEndpoint
      Timeout: 300
      MemorySize: 1024

  # AI/ML integration
  AIModelEndpoint:
    Type: AWS::SageMaker::Endpoint
    Properties:
      EndpointConfigName: !Ref EndpointConfig
      EndpointName: ml-prediction-endpoint

  # EventBridge for complex event routing
  EventBus:
    Type: AWS::Events::EventBus
    Properties:
      Name: application-events

  # Step Functions for complex workflows
  WorkflowStateMachine:
    Type: AWS::StepFunctions::StateMachine
    Properties:
      Definition:
        StartAt: ProcessData
        States:
          ProcessData:
            Type: Task
            Resource: !GetAtt DataProcessingFunction.Arn
            Next: AnalyzeResults
          AnalyzeResults:
            Type: Task
            Resource: !GetAtt AIModelEndpoint.Arn
            End: true

# Kubernetes Edge Computing Configuration
apiVersion: v1
kind: ConfigMap
metadata:
  name: edge-computing-config
data:
  edge-config.yaml: |
    edge:
      nodes:
        - name: edge-node-1
          location: "us-west-1"
          capabilities:
            - ai-inference
            - real-time-processing
            - low-latency
        - name: edge-node-2
          location: "us-east-1"
          capabilities:
            - data-filtering
            - caching
            - analytics

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: edge-ai-service
spec:
  replicas: 5
  selector:
    matchLabels:
      app: edge-ai
  template:
    metadata:
      labels:
        app: edge-ai
    spec:
      containers:
        - name: ai-inference
          image: edge-ai-model:latest
          ports:
            - containerPort: 8080
          resources:
            requests:
              memory: "512Mi"
              cpu: "500m"
            limits:
              memory: "1Gi"
              cpu: "1000m"
          env:
            - name: EDGE_NODE_ID
              valueFrom:
                fieldRef:
                  fieldPath: spec.nodeName
            - name: 5G_ENABLED
              value: "true"

# Terraform configuration for AI/ML infrastructure
resource "aws_sagemaker_domain" "ml_domain" {
  domain_name = "ml-workspace"
  auth_mode   = "IAM"
  vpc_id      = aws_vpc.main.id
  subnet_ids  = aws_subnet.private[*].id

  default_user_settings {
    execution_role = aws_iam_role.sagemaker_execution_role.arn

    jupyter_server_app_settings {
      default_resource_spec {
        instance_type       = "ml.t3.medium"
        sagemaker_image_arn = data.aws_sagemaker_prebuilt_ecr_image.jupyter.registry_path
      }
    }

    kernel_gateway_app_settings {
      default_resource_spec {
        instance_type       = "ml.t3.medium"
        sagemaker_image_arn = data.aws_sagemaker_prebuilt_ecr_image.kernel.registry_path
      }
    }
  }
}

# Auto-scaling ML inference endpoints
resource "aws_sagemaker_endpoint_configuration" "ml_endpoint" {
  name = "ml-inference-endpoint"

  production_variants {
    variant_name           = "primary"
    model_name            = aws_sagemaker_model.ml_model.name
    instance_type         = "ml.m5.large"
    initial_instance_count = 1

    auto_scaling_config {
      min_capacity = 1
      max_capacity = 10
      target_value = 0.7
    }
  }
}

# GPU-optimized instances for training
resource "aws_instance" "gpu_training" {
  ami           = data.aws_ami.deep_learning.id
  instance_type = "p3.2xlarge"  # NVIDIA V100 GPU

  root_block_device {
    volume_size = 100
    volume_type = "gp3"
  }

  tags = {
    Name = "ml-training-instance"
    Purpose = "deep-learning"
  }
}

# Quantum computing integration example
import qiskit
from qiskit import QuantumCircuit, Aer, execute
from qiskit.algorithms import VQE
from qiskit.algorithms.optimizers import SPSA

class QuantumOptimizer:
    def __init__(self):
        self.backend = Aer.get_backend('qasm_simulator')

    def optimize_infrastructure(self, constraints):
        # Quantum algorithm for infrastructure optimization
        circuit = QuantumCircuit(4, 4)

        # Apply quantum gates for optimization
        circuit.h(range(4))
        circuit.measure_all()

        # Execute on quantum backend
        job = execute(circuit, self.backend, shots=1000)
        result = job.result()

        return self.interpret_results(result)

    def quantum_load_balancing(self, server_loads):
        # Quantum-inspired load balancing algorithm
        optimized_distribution = self.quantum_optimize_distribution(server_loads)
        return optimized_distribution

# Integration with classical infrastructure
class HybridInfrastructureManager:
    def __init__(self):
        self.quantum_optimizer = QuantumOptimizer()
        self.classical_orchestrator = KubernetesOrchestrator()

    def optimize_deployment(self, application_requirements):
        # Use quantum computing for complex optimization problems
        quantum_solution = self.quantum_optimizer.optimize_infrastructure(
            application_requirements
        )

        # Apply results to classical infrastructure
        return self.classical_orchestrator.deploy(quantum_solution)

# Green computing configuration
apiVersion: v1
kind: ConfigMap
metadata:
  name: green-computing-config
data:
  sustainability-policy.yaml: |
    green_computing:
      carbon_footprint_tracking: true
      renewable_energy_preference: true
      resource_optimization:
        cpu_throttling: true
        memory_compression: true
        storage_tiering: true
      scheduling:
        prefer_green_datacenters: true
        carbon_aware_scheduling: true

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: carbon-aware-scheduler
spec:
  replicas: 1
  selector:
    matchLabels:
      app: carbon-scheduler
  template:
    metadata:
      labels:
        app: carbon-scheduler
    spec:
      containers:
        - name: scheduler
          image: carbon-aware-scheduler:latest
          env:
            - name: CARBON_TRACKING_ENABLED
              value: "true"
            - name: RENEWABLE_ENERGY_THRESHOLD
              value: "80"
          volumeMounts:
            - name: carbon-data
              mountPath: /data/carbon
      volumes:
        - name: carbon-data
          persistentVolumeClaim:
            claimName: carbon-data-pvc

# Multi-cloud infrastructure with intelligent routing
resource "aws_route53_health_check" "multi_cloud" {
  fqdn              = "api.example.com"
  port              = 443
  type              = "HTTPS"
  resource_path     = "/health"
  failure_threshold = "3"
  request_interval  = "30"

  tags = {
    Name = "multi-cloud-health-check"
  }
}

# Cross-cloud load balancing
resource "aws_route53_record" "multi_cloud" {
  zone_id = aws_route53_zone.main.zone_id
  name    = "api.example.com"
  type    = "A"

  alias {
    name                   = aws_lb.multi_cloud.dns_name
    zone_id                = aws_lb.multi_cloud.zone_id
    evaluate_target_health = true
  }

  health_check_id = aws_route53_health_check.multi_cloud.id
}

# Cloud-agnostic container orchestration
resource "kubernetes_deployment" "cloud_agnostic" {
  metadata {
    name = "cloud-agnostic-app"
  }

  spec {
    replicas = 3

    selector {
      match_labels = {
        app = "cloud-agnostic"
      }
    }

    template {
      metadata {
        labels = {
          app = "cloud-agnostic"
        }
      }

      spec {
        container {
          image = "cloud-agnostic-app:latest"
          name  = "app"

          env {
            name  = "CLOUD_PROVIDER"
            value = "auto-detect"
          }

          env {
            name  = "MULTI_CLOUD_ENABLED"
            value = "true"
          }
        }
      }
    }
  }
}

# Autonomous infrastructure management system
apiVersion: v1
kind: ConfigMap
metadata:
  name: autonomous-infra-config
data:
  ai-controller.yaml: |
    autonomous_management:
      enabled: true
      learning_mode: true
      decision_threshold: 0.85
      actions:
        - auto_scaling
        - resource_optimization
        - security_patching
        - cost_optimization
      safety_limits:
        max_scale_up: 200%
        max_scale_down: 50%
        maintenance_window: "02:00-04:00"

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ai-infrastructure-controller
spec:
  replicas: 1
  selector:
    matchLabels:
      app: ai-controller
  template:
    metadata:
      labels:
        app: ai-controller
    spec:
      containers:
        - name: ai-controller
          image: ai-infrastructure-controller:latest
          env:
            - name: AI_MODEL_PATH
              value: "/models/infrastructure-ai"
            - name: LEARNING_RATE
              value: "0.001"
            - name: DECISION_CONFIDENCE
              value: "0.9"
          volumeMounts:
            - name: ai-models
              mountPath: /models
            - name: infrastructure-data
              mountPath: /data
      volumes:
        - name: ai-models
          persistentVolumeClaim:
            claimName: ai-models-pvc
        - name: infrastructure-data
          persistentVolumeClaim:
            claimName: infra-data-pvc

// Decentralized infrastructure management
const { ethers } = require('ethers');
const InfrastructureContract = require('./contracts/Infrastructure.json');

class DecentralizedInfrastructure {
  constructor() {
    this.provider = new ethers.providers.Web3Provider(window.ethereum);
    this.contract = new ethers.Contract(
      InfrastructureContract.address,
      InfrastructureContract.abi,
      this.provider.getSigner()
    );
  }

  async deployService(serviceConfig) {
    // Deploy service to decentralized infrastructure
    const tx = await this.contract.deployService(
      serviceConfig.name,
      serviceConfig.resources,
      serviceConfig.price
    );

    const receipt = await tx.wait();
    return receipt;
  }

  async rentCompute(resourceRequirements) {
    // Rent compute resources from decentralized network
    const availableResources = await this.contract.getAvailableResources();

    const bestMatch = this.findOptimalResource(availableResources, resourceRequirements);

    const tx = await this.contract.rentCompute(
      bestMatch.id,
      resourceRequirements.duration,
      { value: bestMatch.price }
    );

    return await tx.wait();
  }

  async contributeResources(resourceSpec) {
    // Contribute idle resources to the network
    const tx = await this.contract.contributeResources(
      resourceSpec.cpu,
      resourceSpec.memory,
      resourceSpec.storage,
      resourceSpec.price
    );

    return await tx.wait();
  }
}

// Smart contract for infrastructure management
contract InfrastructureMarketplace {
    struct Resource {
        address owner;
        uint256 cpu;
        uint256 memory;
        uint256 storage;
        uint256 price;
        bool available;
    }

    mapping(uint256 => Resource) public resources;
    uint256 public resourceCount;

    event ResourceAdded(uint256 indexed resourceId, address indexed owner);
    event ResourceRented(uint256 indexed resourceId, address indexed renter);

    function addResource(uint256 _cpu, uint256 _memory, uint256 _storage, uint256 _price) external {
        resourceCount++;
        resources[resourceCount] = Resource({
            owner: msg.sender,
            cpu: _cpu,
            memory: _memory,
            storage: _storage,
            price: _price,
            available: true
        });

        emit ResourceAdded(resourceCount, msg.sender);
    }

    function rentResource(uint256 _resourceId) external payable {
        Resource storage resource = resources[_resourceId];
        require(resource.available, "Resource not available");
        require(msg.value >= resource.price, "Insufficient payment");

        resource.available = false;
        payable(resource.owner).transfer(msg.value);

        emit ResourceRented(_resourceId, msg.sender);
    }
}

# AI-powered security configuration
apiVersion: v1
kind: ConfigMap
metadata:
  name: ai-security-config
data:
  security-policy.yaml: |
    ai_security:
      enabled: true
      threat_detection:
        behavioral_analysis: true
        anomaly_detection: true
        zero_day_protection: true
      response_automation:
        auto_containment: true
        threat_hunting: true
        incident_response: true
      learning:
        adaptive_thresholds: true
        threat_intelligence: true
        pattern_recognition: true

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ai-security-controller
spec:
  replicas: 1
  selector:
    matchLabels:
      app: ai-security
  template:
    metadata:
      labels:
        app: ai-security
    spec:
      containers:
        - name: security-ai
          image: ai-security-controller:latest
          env:
            - name: THREAT_MODEL_PATH
              value: "/models/threat-detection"
            - name: RESPONSE_AUTOMATION
              value: "true"
            - name: LEARNING_ENABLED
              value: "true"
          securityContext:
            capabilities:
              add:
                - NET_ADMIN
                - SYS_ADMIN
          volumeMounts:
            - name: security-models
              mountPath: /models
            - name: network-data
              mountPath: /data/network
      volumes:
        - name: security-models
          persistentVolumeClaim:
            claimName: security-models-pvc
        - name: network-data
          persistentVolumeClaim:
            claimName: network-data-pvc