Commit 3e7bde97 authored by Waleed Akbar's avatar Waleed Akbar
Browse files

feat: Update SLA policy and InfluxDB fetcher to use... 

feat: Update SLA policy and InfluxDB fetcher to use "forecast_sample_interval_sec" and "forecast_sample_count".
parent d18cfa85

src/tests/mwc26-f5ga/AI_analytics_engine/ai_model/sla_policy.py

+8 −8
Original line number Diff line number Diff line
@@ -33,15 +33,15 @@ class SLAPolicyConfig: 
        bandwidth_utilization_threshold_pct: Maximum acceptable bandwidth

            utilization as a percentage (0-100).

        time_window_seconds: Time window in seconds for data analysis.

        sample_interval_sec: Sampling interval in seconds for data collection.

        sample_count: Minimum number of samples to fetch from database.

        forecast_sample_interval_sec: Sampling interval in seconds for data collection.

        forecast_sample_count: Minimum number of samples to fetch from database.

    """

    simap_id: str

    latency_threshold_ms: float|None

    bandwidth_utilization: float|None

    time_window_seconds: int

    sample_interval_sec: int

    sample_count: int

    forecast_sample_interval_sec: int

    forecast_sample_count: int



    @classmethod

    def from_dict(cls, data: Dict[str, Any]) -> SLAPolicyConfig:
@@ -68,16 +68,16 @@ class SLAPolicyConfig: 
            latency_threshold_ms = float(metrics['latency_threshold_ms'])

            bandwidth_threshold  = float(metrics.get('bandwidth_utilization', 0.0))

            time_window          = int(data['history_window_size_sec'])

            sample_interval      = int(data['sample_interval_sec'])

            sample_count         = int(data['sample_count'])

            sample_interval      = int(data['forecast_sample_interval_sec'])

            forecast_sample_count         = int(data['forecast_sample_count'])

            

            return cls(

                simap_id              = simap_id,

                latency_threshold_ms  = latency_threshold_ms,

                bandwidth_utilization = bandwidth_threshold,

                time_window_seconds   = time_window,

                sample_interval_sec   = sample_interval,

                sample_count          = sample_count

                forecast_sample_interval_sec   = sample_interval,

                forecast_sample_count          = forecast_sample_count

            )

        except KeyError as e:

            raise KeyError(f"Missing required field: {e.args[0]}") from e

src/tests/mwc26-f5ga/AI_analytics_engine/clients/influxdb_fetcher.py

+6 −6
Original line number Diff line number Diff line
@@ -156,7 +156,7 @@ class InfluxDBFetcher: 
        ensures resilience against transient failures.

        

        If the initial query returns fewer samples than required by 

        sla_policy.sample_count, the method will automatically fetch 

        sla_policy.forecast_sample_count, the method will automatically fetch 

        older data with an extended time window until the required 

        sample count is met or max attempts are reached.
@@ -185,7 +185,7 @@ class InfluxDBFetcher: 
        LOGGER.debug(

            f"Fetching performance data for simap_id={sla_policy.simap_id}, "

            f"time_window={sla_policy.time_window_seconds}s, "

            f"required_samples={sla_policy.sample_count}"

            f"required_samples={sla_policy.forecast_sample_count}"

        )

        

        try:
@@ -213,11 +213,11 @@ class InfluxDBFetcher: 
                

                LOGGER.info(

                    f"Attempt {attempt}: Fetched {samples_fetched} samples "

                    f"(required: {sla_policy.sample_count})"

                    f"(required: {sla_policy.forecast_sample_count})"

                )

                

                # Check if we have enough samples

                if samples_fetched >= sla_policy.sample_count:

                if samples_fetched >= sla_policy.forecast_sample_count:

                    LOGGER.info(f"Required samples met")

                    break
@@ -227,7 +227,7 @@ class InfluxDBFetcher: 
                        # Calculate required time window based on sample density

                        # Formula: new_window = current_window * (required_samples / fetched_samples) * 1.2

                        # The 1.2 factor adds 20% buffer to account for non-uniform data distribution

                        ratio               = sla_policy.sample_count / samples_fetched

                        ratio               = sla_policy.forecast_sample_count / samples_fetched

                        current_time_window = int(current_time_window * ratio * 1.2)

                        LOGGER.debug(f"Extending time window to {current_time_window}s(ratio: {ratio:.2f})")

                    else:
@@ -239,7 +239,7 @@ class InfluxDBFetcher: 
                else:

                    LOGGER.warning(

                        f"Max attempts reached. Returning {samples_fetched} samples "

                        f"(required: {sla_policy.sample_count})"

                        f"(required: {sla_policy.forecast_sample_count})"

                    )

                    break

src/tests/mwc26-f5ga/AI_analytics_engine/tests/test_api.py

+3 −3
Original line number Diff line number Diff line
@@ -122,8 +122,8 @@ def test_analyze_endpoint(ai_engine_server): 
            "bandwidth_utilization": 0.0

        },

        "history_window_size_sec":      600,

        "sample_interval_sec":     5,

        "sample_count":            120,

        "forecast_sample_interval_sec": 5,

        "forecast_sample_count":        120,

    }



    LOGGER.info(f"Sending analyze request with payload: {payload}")