## PRD: Enhanced Jitter Strategies for Task Scheduling

This PRD expands on the jitter implementation in task scheduling by introducing different jitter strategies tailored to specific task types. This will further randomize execution patterns and make AIVA's behavior less predictable.

### GPM-063: Implement Uniform Jitter Strategy
**Goal:** Implement a uniform distribution jitter strategy for task scheduling.
**Acceptance Criteria:**
- A new `UniformJitterStrategy` class exists.
- The `UniformJitterStrategy` class accepts a min and max jitter value during initialization.
- The `get_jitter()` method of the `UniformJitterStrategy` returns a random value between the min and max values, inclusive.
- Unit tests cover the functionality and boundary conditions of the `UniformJitterStrategy`.
**Files:** `aiva/scheduling/jitter_strategies.py`
**Spawns:** Explore other jitter distribution strategies like Gaussian or Exponential.

### GPM-064: Implement Gaussian Jitter Strategy
**Goal:** Implement a Gaussian (Normal) distribution jitter strategy for task scheduling.
**Acceptance Criteria:**
- A new `GaussianJitterStrategy` class exists.
- The `GaussianJitterStrategy` class accepts a mean and standard deviation during initialization.
- The `get_jitter()` method of the `GaussianJitterStrategy` returns a random value from a Gaussian distribution with the specified mean and standard deviation.
- Values returned by `get_jitter()` should be clipped to a configurable range to prevent extreme values.
- Unit tests cover the functionality and boundary conditions of the `GaussianJitterStrategy`, including clipping behavior.
**Files:** `aiva/scheduling/jitter_strategies.py`
**Spawns:** Implement an adaptive jitter strategy that adjusts its parameters based on system load.

### GPM-065: Implement Exponential Jitter Strategy
**Goal:** Implement an Exponential distribution jitter strategy for task scheduling.
**Acceptance Criteria:**
- A new `ExponentialJitterStrategy` class exists.
- The `ExponentialJitterStrategy` class accepts a lambda (rate parameter) during initialization.
- The `get_jitter()` method of the `ExponentialJitterStrategy` returns a random value from an Exponential distribution with the specified rate parameter.
- Values returned by `get_jitter()` should be clipped to a configurable range to prevent extreme values.
- Unit tests cover the functionality and boundary conditions of the `ExponentialJitterStrategy`, including clipping behavior.
**Files:** `aiva/scheduling/jitter_strategies.py`
**Spawns:** Investigate using different seeds for the random number generators for each strategy.

### GPM-066: Configure Jitter Strategy per Task Type
**Goal:** Allow different task types to be configured with different jitter strategies.
**Acceptance Criteria:**
- The task scheduler configuration allows specifying a jitter strategy for each task type.
- The task scheduler uses the configured jitter strategy when scheduling a task of that type.
- A default jitter strategy is used for task types that are not explicitly configured.
- Unit tests verify that tasks are scheduled with the correct jitter strategy based on their type.
**Files:** `aiva/scheduling/task_scheduler.py`, `aiva/config/task_scheduler_config.py`
**Spawns:** Implement a UI for configuring jitter strategies per task type.

### GPM-067: Refactor Jitter Application
**Goal:** Refactor the jitter application logic to be more modular and testable.
**Acceptance Criteria:**
- The jitter application is separated from the core scheduling logic.
- A dedicated class or function is responsible for applying the jitter value to the task's next execution time.
- Unit tests specifically target the jitter application logic.
- The refactoring does not introduce any regressions in the existing functionality.
**Files:** `aiva/scheduling/task_scheduler.py`, `aiva/scheduling/jitter_application.py`
**Spawns:** Explore using a plugin architecture for different jitter application methods.

### GPM-068: Implement Jitter Strategy Selection via Configuration
**Goal:** Enable the selection of a jitter strategy (Uniform, Gaussian, Exponential) through configuration.
**Acceptance Criteria:**
- The configuration file or system allows specifying which jitter strategy to use for a given task type.
- The system correctly instantiates and uses the selected jitter strategy based on the configuration.
- Error handling is in place to handle invalid or missing jitter strategy configurations.
- Unit tests verify correct strategy selection based on configuration.
**Files:** `aiva/config/task_scheduler_config.py`, `aiva/scheduling/task_scheduler.py`
**Spawns:** Support dynamically changing jitter strategies at runtime.

### GPM-069: Add Logging and Monitoring for Jitter Application
**Goal:** Add logging and monitoring to track the jitter values being applied to tasks.
**Acceptance Criteria:**
- The system logs the jitter value applied to each task's next execution time.
- The logs include the task type and the jitter strategy used.
- Metrics are exposed to monitor the distribution of jitter values being applied across different task types.
- The logging level is configurable.
**Files:** `aiva/scheduling/task_scheduler.py`, `aiva/monitoring/metrics.py`
**Spawns:** Develop visualizations to analyze the effectiveness of different jitter strategies.
