Resource Management Capabilities¶

Overview¶

AIDDDMAP's resource management system provides comprehensive capabilities for managing and optimizing system resources across the platform. The system handles compute resources, memory allocation, storage management, and network utilization.

Core Features¶

1. Resource Allocation Best Practices¶

CPU Management
Memory Management
Storage Management
Network Management

2. Workload Handling¶

Task Scheduling
Load Balancing
Resource Monitoring
Performance Optimization

3. Resource Optimization Strategies¶

Dynamic Scaling
Resource Pooling
Cache Management
Power Management

4. Resource Security¶

Access Control
Resource Isolation
Usage Monitoring
Security Policies

Implementation¶

1. Resource Configuration¶

interface ResourceConfig {
  compute: ComputeConfig;
  memory: MemoryConfig;
  storage: StorageConfig;
  network: NetworkConfig;
}

interface ComputeConfig {
  allocation: AllocationStrategy;
  limits: ResourceLimits;
  scheduling: SchedulingPolicy;
}

enum AllocationStrategy {
  STATIC = "static",
  DYNAMIC = "dynamic",
  HYBRID = "hybrid",
  CUSTOM = "custom",
}

2. Resource Management¶

interface ResourceManager {
  allocation: AllocationManager;
  monitoring: MonitoringConfig;
  optimization: OptimizationConfig;
}

interface AllocationManager {
  strategy: string;
  policies: Policy[];
  constraints: ResourceConstraints;
}

interface MonitoringConfig {
  metrics: string[];
  interval: number;
  alerts: AlertConfig[];
}

3. Workload Management¶

interface WorkloadManager {
  scheduler: SchedulerConfig;
  balancer: BalancerConfig;
  monitor: MonitorConfig;
}

interface SchedulerConfig {
  algorithm: string;
  queue: QueueConfig;
  priority: PriorityConfig;
}

interface BalancerConfig {
  strategy: string;
  thresholds: Thresholds;
  actions: BalancerAction[];
}

Resource Allocation¶

1. Compute Management¶

interface ComputeManager {
  allocation: CPUAllocation;
  scheduling: TaskScheduling;
  monitoring: PerformanceMonitoring;
}

interface CPUAllocation {
  cores: number[];
  affinity: AffinityConfig;
  priority: PriorityLevels;
}

interface TaskScheduling {
  algorithm: SchedulingAlgorithm;
  queue: TaskQueue;
  priority: PriorityQueue;
}

2. Memory Management¶

interface MemoryManager {
  allocation: MemoryAllocation;
  paging: PagingConfig;
  caching: CacheConfig;
}

interface MemoryAllocation {
  strategy: AllocationStrategy;
  limits: MemoryLimits;
  policies: MemoryPolicy[];
}

Integration Examples¶

1. Resource Allocation System¶

// Configure resource manager
const resourceManager = new ResourceManager({
  compute: {
    allocation: {
      strategy: "dynamic",
      cores: {
        min: 2,
        max: 8,
        preferred: 4,
      },
      scheduling: {
        algorithm: "fair-share",
        quantum: 100,
      },
    },
    limits: {
      cpu: {
        usage: 80,
        temperature: 85,
      },
      memory: {
        usage: 90,
        swap: 50,
      },
    },
    monitoring: {
      interval: 1000,
      metrics: ["cpu", "memory", "temperature"],
      thresholds: {
        cpu: 80,
        memory: 90,
        temperature: 85,
      },
    },
  },
  memory: {
    allocation: {
      strategy: "dynamic",
      initial: "1GB",
      maximum: "8GB",
    },
    paging: {
      enabled: true,
      size: "4KB",
      policy: "lru",
    },
    caching: {
      enabled: true,
      size: "256MB",
      policy: "lru",
    },
  },
});

// Allocate resources
const allocation = await resourceManager.allocate({
  requirements: {
    cpu: 2,
    memory: "2GB",
    priority: "high",
  },
  constraints: {
    location: "local",
    timeout: 30000,
  },
});

2. Workload Management¶

// Configure workload manager
const workloadManager = new WorkloadManager({
  scheduler: {
    algorithm: "priority",
    queues: [
      {
        name: "high",
        priority: 3,
        quantum: 100,
      },
      {
        name: "medium",
        priority: 2,
        quantum: 200,
      },
      {
        name: "low",
        priority: 1,
        quantum: 300,
      },
    ],
    preemption: {
      enabled: true,
      threshold: "high",
    },
  },
  balancer: {
    strategy: "least-loaded",
    metrics: ["cpu", "memory", "network"],
    thresholds: {
      cpu: 80,
      memory: 90,
      network: 70,
    },
    interval: 5000,
  },
  monitoring: {
    enabled: true,
    interval: 1000,
    metrics: ["cpu", "memory", "network", "queue"],
  },
});

// Submit workload
const task = await workloadManager.submit({
  type: "computation",
  priority: "high",
  resources: {
    cpu: 2,
    memory: "2GB",
  },
  constraints: {
    deadline: Date.now() + 3600000,
    location: "any",
  },
});

3. Resource Optimization¶

// Configure resource optimizer
const resourceOptimizer = new ResourceOptimizer({
  strategies: {
    compute: {
      type: "dynamic-scaling",
      metrics: ["utilization", "performance"],
      thresholds: {
        scale_up: 80,
        scale_down: 20,
      },
    },
    memory: {
      type: "adaptive-caching",
      policy: "lru",
      size: {
        min: "256MB",
        max: "1GB",
      },
    },
    storage: {
      type: "tiered",
      tiers: [
        {
          type: "ssd",
          size: "100GB",
          priority: "high",
        },
        {
          type: "hdd",
          size: "1TB",
          priority: "low",
        },
      ],
    },
  },
  monitoring: {
    interval: 5000,
    metrics: ["utilization", "performance", "efficiency"],
  },
});

// Optimize resources
const optimization = await resourceOptimizer.optimize({
  target: "performance",
  constraints: {
    power: "efficient",
    cost: "minimal",
  },
});

Performance Optimization¶

1. Resource Monitoring¶

interface ResourceMonitor {
  metrics: MetricConfig[];
  collection: CollectionConfig;
  analysis: AnalysisConfig;
}

interface MetricConfig {
  name: string;
  type: MetricType;
  interval: number;
  aggregation: AggregationType;
}

2. Performance Tuning¶

interface PerformanceTuner {
  policies: TuningPolicy[];
  parameters: TuningParameter[];
  optimization: OptimizationConfig;
}

Best Practices¶

1. Resource Allocation¶

Implement proper limits
Monitor usage patterns
Handle resource conflicts
Regular optimization

2. Performance¶

Optimize allocation
Balance workloads
Monitor metrics
Handle bottlenecks

3. Security¶

Access control
Resource isolation
Usage monitoring
Policy enforcement

Future Enhancements¶

Planned Features
Advanced scheduling
Predictive scaling
Resource analytics
Automated optimization
Research Areas
Machine learning optimization
Quantum resource management
Edge computing resources
Green computing