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| """ | |
| Configuration data models for the Cloud Resource Allocation RL environment. | |
| This module defines dataclasses for environment and reward configuration parameters, | |
| providing type-safe configuration with sensible defaults for the RL simulation. | |
| """ | |
| from dataclasses import dataclass | |
| from pydantic import BaseModel, Field | |
| class EnvConfig: | |
| """ | |
| Environment configuration parameters. | |
| This dataclass encapsulates all parameters controlling the cloud resource | |
| allocation environment's behavior, including episode length, initial conditions, | |
| request dynamics, and resource constraints. | |
| Attributes: | |
| max_steps: Maximum episode length (number of time steps before termination) | |
| initial_resources: Starting number of server instances at episode start | |
| base_request_rate: Baseline incoming request rate (requests per time step) | |
| request_rate_std: Standard deviation for stochastic request rate fluctuation | |
| cpu_per_request: CPU utilization cost per request (percentage points) | |
| memory_per_request: Memory utilization cost per request (percentage points) | |
| resource_capacity: Processing capacity per server instance | |
| termination_threshold: Utilization percentage triggering episode termination | |
| """ | |
| max_steps: int = 100 | |
| initial_resources: int = 2 | |
| base_request_rate: int = 50 | |
| request_rate_std: float = 10.0 | |
| cpu_per_request: float = 0.5 | |
| memory_per_request: float = 0.45 | |
| resource_capacity: float = 30.0 | |
| termination_threshold: float = 95.0 | |
| class RewardConfig: | |
| """ | |
| Reward calculation configuration parameters. | |
| This dataclass defines the reward shaping parameters that guide the RL agent | |
| toward efficient resource allocation. It specifies optimal utilization ranges | |
| and penalty weights for over-provisioning and under-provisioning. | |
| Attributes: | |
| target_cpu_min: Lower bound of optimal CPU utilization range (percentage) | |
| target_cpu_max: Upper bound of optimal CPU utilization range (percentage) | |
| target_memory_min: Lower bound of optimal memory utilization range (percentage) | |
| target_memory_max: Upper bound of optimal memory utilization range (percentage) | |
| resource_cost_weight: Penalty weight for resource usage (cost per instance) | |
| over_provision_penalty: Penalty applied when resources exceed system needs | |
| under_provision_penalty: Penalty applied when resources are insufficient | |
| """ | |
| target_cpu_min: float = 40.0 | |
| target_cpu_max: float = 70.0 | |
| target_memory_min: float = 40.0 | |
| target_memory_max: float = 70.0 | |
| resource_cost_weight: float = 0.1 | |
| over_provision_penalty: float = -0.5 | |
| under_provision_penalty: float = -1.0 | |
| class EnvState(BaseModel): | |
| """ | |
| Typed environment state representation for OpenEnv compliance. | |
| This Pydantic model provides validated, typed access to environment state | |
| with automatic field validation ensuring values are within valid ranges. | |
| Attributes: | |
| cpu: CPU utilization percentage in range [0.0, 100.0] | |
| memory: Memory utilization percentage in range [0.0, 100.0] | |
| request_rate: Incoming request rate, non-negative float | |
| resources: Allocated server instances, minimum 1 | |
| """ | |
| cpu: float = Field(ge=0.0, le=100.0, description="CPU utilization percentage") | |
| memory: float = Field(ge=0.0, le=100.0, description="Memory utilization percentage") | |
| request_rate: float = Field(ge=0.0, description="Incoming request rate") | |
| resources: int = Field(ge=1, description="Allocated server instances") | |
| # Task difficulty configurations for OpenEnv compliance | |
| TASKS = { | |
| "easy": { | |
| "base_request_rate": 40, | |
| "request_rate_std": 5.0, | |
| "description": "Low request rate with minimal variance - ideal for learning basic resource allocation" | |
| }, | |
| "medium": { | |
| "base_request_rate": 60, | |
| "request_rate_std": 15.0, | |
| "description": "Moderate request rate with medium variance - balanced challenge for standard training" | |
| }, | |
| "hard": { | |
| "base_request_rate": 80, | |
| "request_rate_std": 25.0, | |
| "description": "High request rate with high variance - challenging scenario requiring robust policies" | |
| } | |
| } | |