# GPM-037: Loop Optimization Engine

This PRD outlines the initial set of stories for implementing a foundational Loop Optimization Engine within AIVA. Building upon the Infinite Loop Controller (GPM-029), this engine aims to enhance the efficiency, adaptability, and intelligence of AIVA's perpetual operational loop. The focus is on reducing resource consumption, enabling dynamic behavior adjustments, and laying the groundwork for self-optimizing capabilities.

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### GPM-038: Dynamic Iteration Rate Adjustment
**Goal:** Enable the AIVA loop controller to dynamically adjust its iteration frequency based on configurable policies or detected system load, allowing AIVA to adapt its processing speed.
**Acceptance Criteria:**
- The `InfiniteLoopController` exposes an API or method to update its iteration delay/rate.
- A default iteration rate can be configured at system startup.
- The loop's actual iteration delay demonstrably changes and adheres to the new rate within a 5% tolerance after an adjustment.
- Changes to the iteration rate do not cause the loop to halt or enter an unstable state.
**Files:**
- `aiva/core/loop_controller.py` (modification to expose rate adjustment)
- `aiva/optimizer/rate_manager.py` (new module for managing and applying rates)
- `aiva/configs/optimizer_settings.yaml` (new configuration for default rate)
**Spawns:** GPM-044 (Adaptive Iteration Rate based on Load)

### GPM-039: Resource Consumption Profiler
**Goal:** Implement robust monitoring to track and log CPU and memory usage for each significant segment or full iteration of the AIVA loop, providing critical data for performance analysis.
**Acceptance Criteria:**
- A new `ResourceProfiler` module can be activated to measure CPU and memory consumption.
- For each loop iteration (or configurable interval), profiled data (timestamp, CPU %, Memory MB consumed) is captured and logged.
- The profiling mechanism introduces less than 5% overhead to the overall loop execution time.
- Configuration options exist to enable/disable the profiler and set its granularity.
**Files:**
- `aiva/optimizer/resource_profiler.py` (new module)
- `aiva/data/telemetry_models.py` (new data models for resource metrics)
- `aiva/core/loop_controller.py` (integration points for profiling)
- `aiva/logs/performance.log` (destination for profiled data)
**Spawns:** GPM-045 (Performance Metric Aggregation)

### GPM-040: Conditional Task Skipping
**Goal:** Introduce a mechanism within the AIVA loop to conditionally skip specific tasks or sub-routines based on predefined conditions (e.g., system state, data availability), thereby reducing unnecessary processing cycles.
**Acceptance Criteria:**
- Any task registered with the `LoopController` can be marked as 'skippable'.
- A `SkipConditionEvaluator` module can be configured with rules (e.g., "skip_task_X if state_Y is 'inactive'").
- If a task is skipped, its execution is bypassed, and a log entry is created indicating the task and the reason for skipping.
- The overall loop execution time demonstrably decreases when skippable tasks are bypassed compared to always executing them.
**Files:**
- `aiva/optimizer/skip_condition_evaluator.py` (new module)
- `aiva/core/task_manager.py` (modification to support skippable tasks)
- `aiva/configs/task_skip_rules.json` (new configuration for skip logic)
**Spawns:** GPM-046 (Context-Aware Task Prioritization)

### GPM-041: State-Aware Micro-Batching
**Goal:** Optimize loop efficiency by grouping similar, non-critical operations into micro-batches when AIVA's internal state indicates low urgency or sufficient buffer, processing them together instead of individually per iteration.
**Acceptance Criteria:**
- A new `MicroBatchProcessor` module can queue multiple, compatible operations.
- Operations are processed as a single batch when a configurable threshold (e.g., number of items, elapsed time) is met, or a specific AIVA internal state is observed (e.g., `idle_state=true`).
- The batching mechanism correctly processes all operations within a batch, maintaining data integrity.
- The average processing time per operation is reduced when micro-batching is active compared to individual processing.
**Files:**
- `aiva/optimizer/micro_batch_processor.py` (new module)
- `aiva/core/state_manager.py` (modification to expose relevant state for batching)
- `aiva/data/operation_queue.py` (new internal queue for batchable operations)
- `aiva/configs/batching_policies.yaml` (new configuration for batch thresholds)
**Spawns:** GPM-047 (Dynamic Batch Sizing based on Throughput)

### GPM-042: Basic Anomaly Detection for Iteration Performance
**Goal:** Establish a baseline and trigger alerts or log entries when individual loop iterations significantly deviate from expected performance metrics (e.g., execution time), indicating potential bottlenecks or issues.
**Acceptance Criteria:**
- A configurable baseline for average loop iteration time can be set via configuration.
- An iteration whose total execution time exceeds a predefined percentage deviation (e.g., 20%) from the baseline is identified as an anomaly.
- Detected anomalies are logged with relevant context (timestamp, deviation amount, current iteration rate, top 3 resource consumers).
- The detection mechanism minimizes false positives under normal, expected load fluctuations.
**Files:**
- `aiva/optimizer/anomaly_detector.py` (new module)
- `aiva/data/performance_metrics.py` (data model for iteration performance)
- `aiva/configs/performance_thresholds.json` (new configuration for baselines and thresholds)
- `aiva/core/loop_controller.py` (modification to report iteration times)
**Spawns:** GPM-048 (Adaptive Performance Baselines)

### GPM-043: External Optimization Policy Configuration
**Goal:** Allow AIVA's loop optimization parameters (e.g., iteration rates, skip rules, batching thresholds) to be defined and updated via external configuration files or a management interface, enabling flexible deployment and tuning.
**Acceptance Criteria:**
- All optimization parameters introduced in GPM-038, GPM-040, and GPM-041 can be loaded from an external configuration source (e.g., a single `optimization_policy.yaml` file).
- Changes to the external configuration file are recognized and applied by the AIVA system within a configurable refresh interval without requiring a full system restart.
- A validation mechanism ensures that loaded configurations are syntactically correct and that parameter values fall within acceptable operational ranges.
- An API endpoint or command-line interface exists to trigger an immediate reload of the optimization policies.
**Files:**
- `aiva/optimizer/policy_loader.py` (new module for loading and validating policies)
- `aiva/configs/global_optimization_policy.yaml` (unified configuration file)
- `aiva/core/configuration_manager.py` (modification to integrate policy loading)
- `aiva/api/optimizer_endpoints.py` (new endpoint for policy reload)
**Spawns:** GPM-049 (A/B Testing for Optimization Policies)