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Create configuration settings rule

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Create configuration settings rule

Configuration settings affect how metric data is processed. Configuration settings rules override the default values of these configuration settings, letting you build efficient statistical models for different CI classes.

Before you begin

Role required: evt_mgmt_admin

About this task

A number of configuration settings determine the behavior of Operational Intelligence MID Servers. In the base system, these configuration settings are configured with default values, data types, and range of valid values. You cannot directly modify these configuration settings or add new ones. However, you can create a metric configuration rule with new configuration settings that override the default values on the MID Servers.

Then, manually apply these rules to all Operational Intelligence MID Servers in a single synchronization operation, or rely on an hourly system job to perform the synchronization.

To take effect, Operational Intelligence MID Servers must be synchronized with the updates to the configuration settings rules.

It is valid to have multiple rules for a setting that affect the same CIs, in which case:
  • Rules in which a filter is defined take precedence over a global rule in which no filter has been defined.
  • If multiple rules that affect the same set of CIs have the same priority, then only the latest rule to be defined is applied.
  • If multiple rules with different priorities affect the same set of CIs, then rules with the highest priority are applied.

Procedure

  1. Navigate to Operational Intelligence > Configure > Metric Config Rules.
  2. On the Metric Configuration Rules pane, click New, and fill out the form.
    Table 1. Metric Configuration Rules form
    Field Description
    Name Rule name.
    Order Rule priority within all other rules. Higher numbers represent higher priorities.
    Filter by

    Check box for displaying the Rule field, where you can specify conditions that CIs must meet for the rule to apply. For example, in the choose field list, select Add Related Fields and then add the filter [class][is][Linux Server].

    If clear, the rule applies globally to all CIs in the Metric To CI Mapping [sa_metric_map] table.

  3. Right-click the form title, and click Save.
  4. In the Metric overridden configurations form section click New, fill out the form, and then click Submit.
    Table 2. Metric overridden configurations form
    Field Description
    Name Configuration setting for which to override its value.

    Click the Event Management icon icon to display the list of all configuration settings.

    Click the Event Management icon icon to display the Metric Settings dialog with details such as range of possible values.

    See the following tables (Configuration Settings and Internal Configuration Settings) for details about configuration settings.

    Rule Rule to which the created configuration setting applies.
    Value New value that overrides the default value for the specified configuration setting.
    You can modify the following configuration settings in the Name field.
    Note: The filter specified in the metric configuration rule does not apply to settings with a global scope.
    Table 3. Configuration Settings
    Name and Description Values Default Data Type Scope

    anomaly_detection_enabled

    Enable/disable anomaly detection.

    Note: If anomaly_detection_action_level is set, then anomaly_detection_enabled is ignored.
    NA true boolean CI/Metric

    anomaly_detection_action_level

    Action level of anomaly analysis and processing.

    For more information, see Configure the action level of anomaly detection.

    choices:
    • Metrics Only
    • Bounds
    • Anomaly Scores
    • Anomaly Alerts
    • IT Alerts
    • New records: Bounds
    • Upgraded records: Anomaly Alerts
    choice CI/Metric

    buffer_anomaly_eviction_size

    Maximum number of anomalies at individual metric level that can be stored in internal buffer before sending them to instance for every CI/Metric pair.

    60–1440 60 integer Global

    buffer_ci_score_eviction_size

    Maximum number of anomalies at CI level that can be stored in internal buffer before sending them to instance (Currently not being used)

    60–1440 60 integer Global

    buffer_metric_eviction_size

    Maximum number of metrics that can be stored in internal buffer before sending them to instance for every CI/Metric pair.

    60–1440 60 integer Global

    connection_login_timeout_secs

    Maximum time in seconds to log in to the local database on MID Server.

    30–60 30 integer Global

    corrupt_data_count_threshold

    Minimum number of training points (15-minute averages) required to do any statistical analysis.

    10–100 30 integer Global

    deprioritize_early_batching_of_anomalous_ci

    Send anomalous CI information immediately or at regular interval.

    NA false boolean Global
    max_pool_connections_size

    Maximum number of connections for local database pool.

    10–50 25 integer Global

    observation_time_min

    Expected minimum metric observation interval.

    1–1440 1 integer CI/Metric

    robust_central_percentage

    Percentage of the residual data to compute the residual standard deviation, used for outlier detection. When set to 100 - uses the regular sample standard deviation.

    50–100 90 double Global
    sparse_gap_fraction_threshold

    If more than this percentage of data is missing and no other class has been identified, classify as SPARSE. Do not attempt to fit a WEEKLY model.

    0–100 50 double Global
    weekly_model_min_days

    Number of days for which data must be available in order to consider only a WEEKLY seasonality decomposition.

    14-90 15 integer CI/Metric
    daily_model_min_days

    Number of days for which data must be available in order to consider only a DAILY seasonality decomposition.

    2-90 3 integer CI/Metric
    build_snpm_model

    Enable/disable building an SNPM data model.

    NA true boolean CI/Metric

    snpm_minimum_data_count

    Minimum number of data points required for building a stationary nonparametric model.

    0 – 1e9 5000 integer

    CI/Metric

    The following configuration settings are for internal usage.

    Table 4. Internal Configuration Settings
    Name and Description Values Default Data Type Scope

    anomaly_memory_time_min

    Anomaly score calculator parameter: Memory time for abnormal situation.

    1–600 45 double CI/Metric

    excess_z_score

    Anomaly score calculator parameter: Minimal anomalousness accumulated for outlier.

    0–3 0.8 double CI/Metric

    linear_accumulator_threshold

    Decision Tree Threshold: ACCUMULATOR analysis

    0.5–5 1 double Global

    low_freq_power_threshold

    Decision Tree Threshold: WEEKLY analysis

    0–100 50 double Global

    low_variability_threshold

    Decision Tree Threshold: TRENDY analysis

    0.0000000001–0.001 0.0001 double Global

    mid_freq_power_threshold

    Decision Tree Threshold: WEEKLY analysis

    0–100 33 double Global

    multinomial_count_threshold

    Decision Tree Threshold: MULTINOMIAL analysis

    1–1000 40 integer Global

    non_zero_diff_threshold

    Decision Tree Threshold: NEAR_CONSTANT analysis

    0–100 5 double Global

    normal_memory_time_min

    Anomaly score calculator parameter: Memory time for normal situation.

    1–600 1 double CI/Metric

    normal_probability_ewma_timescale_min

    Anomaly score calculator parameter: Normal assessment time-scale.

    1–600 15 double CI/Metric

    normal_probability_weight

    Anomaly score calculator parameter: Normal assessment adjustment factor.

    0–1 1 double CI/Metric

    sigmoid_offset

    Anomaly score calculator parameter: Anomalousness to score conversion.

    0–5 2.1 double CI/Metric

    sigmoid_weight

    Anomaly score calculator parameter: Anomalousness to score conversion.

    0–5 1.2 double CI/Metric

    tiny_variability_threshold

    Decision Tree Threshold: NEAR_CONSTANT analysis

    0–0.001 0.0000000001 double Global

    weekly_peak_hi_limit

    Decision Tree Threshold: WEEKLY analysis.

    7–14 10 double Global

    weekly_peak_lo_limit

    Decision Tree Threshold: Weekly analysis.

    0.5–7 0.7 double Global

    weekly_vs_daily_log_likelihood_threshold

    By how much log likelihood of weekly needs to be larger than daily, to be the preferred statistical model.

    100–1000 200 double

    CI/Metric

    daily_vs_noisy_log_likelihood_threshold

    By how much log likelihood of daily needs to be larger than noisy, to be the preferred statistical model.

    20–1000 200 double

    CI/Metric

    weekly_vs_noisy_log_likelihood_treshold

    By how much log likelihood of weekly needs to be larger than noisy, to be the preferred statistical model.

    100–1000 200 double

    CI/Metric

    trendy_vs_noisy_log_likelihood_threshold

    By how much log likelihood of trendy needs to be larger than noisy, to be the preferred statistical model.

    10–1000 50 double

    CI/Metric

    seasonal_loess_width_in_hours

    Applied to the seasonal component of a weekly or daily model before making a forecast of future behavior. If set to 0, each data point in the seasonal model becomes independent of the rest of the data points.

    6–24 12 double

    CI/Metric

    robustness

    Affects how outliers contribute to seasonal and trend calculations.

    NA true boolean

    CI/Metric

    snpm_min_value_threshold

    Minimum value of data required for building an SNPM model.

    -1e9 – 1e9 0 double

    CI/Metric

    snpm_max_observation_interval_in_sec

    Maximum expected observation interval required for building an SNPM model.

    60 – 600000 120 integer

    CI/Metric

    min_std_jump_fraction

    Minimum ratio of locally calculated observation noise level to typical jump size that justifies recalculating a larger observation noise variance.

    0.0 – 1.0 0.2 double

    CI/Metric

    dynamic_threshold_error_smoothing

    Whether to use exponentially weighted moving average to smooth the residuals in the dynamic threshold analysis.

    NA true boolean

    CI/Metric

    ewma_alpha

    The alpha value of the exponentially weighted moving average in dynamic threshold analysis.

    1e-15 – 1.0 0.02739726027 double

    CI/Metric

    dynamic_threshold_beginning_smoothing_length

    Number of smoothed data points to set to the mean of double the smoothing length.

    0 – 10000 250 integer

    CI/Metric

    dynamic_threshold_error_buffer_minutes

    Number of data points around each outlier to group together.

    1 – 1000 30 integer

    CI/Metric

    dynamic_threshold_search_start

    Start value at which the optimal control factor is looked for.

    0.5 – 20.0

    3.0

    double

    CI/Metric

    dynamic_threshold_search_interval

    Interval between search values of optimal control factor.

    0.1 – 5.0 0.5 double

    CI/Metric

    dynamic_threshold_search_count

    Number of values required for searching for optimal control factor.

    1 – 50 19 integer

    CI/Metric

    dynamic_threshold_error_sequence_limit

    Maximum number of error groups for a particular control factor value when searching.

    1 – 20 5 integer

    CI/Metric

    dynamic_threshold_minimum_data_count

    Minimum number of raw data points needed before attempting dynamic thresholding.

    1 – 10000 5000 integer

    CI/Metric

    linear_seasonal_log_likelihood_threshold

    Threshold used in deciding whether to prefer a fitted model with linear seasonality over a model with a periodic component.

    10-5000 1000 integer

    CI/Metric

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