noise

README

Noise Processor Plugin

The Noise processor is used to add noise to numerical field values. For each field a noise is generated using a defined probability densitiy function and added to the value. The function type can be configured as Laplace, Gaussian or Uniform. Depending on the function, various parameters need to be configured:

Global configuration options

In addition to the plugin-specific configuration settings, plugins support additional global and plugin configuration settings. These settings are used to modify metrics, tags, and field or create aliases and configure ordering, etc. See the CONFIGURATION.md for more details.

Configuration

# Adds noise to numerical fields
[[processors.noise]]
  ## Specified the type of the random distribution.
  ## Can be "laplacian", "gaussian" or "uniform".
  # type = "laplacian

  ## Center of the distribution.
  ## Only used for Laplacian and Gaussian distributions.
  # mu = 0.0

  ## Scale parameter for the Laplacian or Gaussian distribution
  # scale = 1.0

  ## Upper and lower bound of the Uniform distribution
  # min = -1.0
  # max = 1.0

  ## Apply the noise only to numeric fields matching the filter criteria below.
  ## Excludes takes precedence over includes.
  # include_fields = []
  # exclude_fields = []

Depending on the choice of the distribution function, the respective parameters must be set. Default settings are noise_type = "laplacian" with mu = 0.0 and scale = 1.0:

Using the include_fields and exclude_fields options a filter can be configured to apply noise only to numeric fields matching it. The following distribution functions are available.

Laplacian

• noise_type = laplacian
• scale: also referred to as diversity parameter, regulates the width & height of the function, a bigger scale value means a higher probability of larger noise, default set to 1.0
• mu: location of the curve, default set to 0.0

Gaussian

• noise_type = gaussian
• mu: mean value, default set to 0.0
• scale: standard deviation, default set to 1.0

Uniform

• noise_type = uniform
• min: minimal interval value, default set to -1.0
• max: maximal interval value, default set to 1.0

Example

Add noise to each value the inputs.cpu plugin generates, except for the usage_steal, usage_user, uptime_format, usage_idle field and all fields of the metrics swap, disk and net:

[[inputs.cpu]]
  percpu = true
  totalcpu = true
  collect_cpu_time = false
  report_active = false

[[processors.noise]]
  scale = 1.0
  mu = 0.0
  noise_type = "laplacian"
  include_fields = []
  exclude_fields = ["usage_steal", "usage_user", "uptime_format", "usage_idle" ]
  namedrop = ["swap", "disk", "net"]

Result of noise added to the cpu metric:

- cpu map[cpu:cpu11 host:98d5b8dbad1c] map[usage_guest:0 usage_guest_nice:0 usage_idle:94.3999999994412 usage_iowait:0 usage_irq:0.1999999999998181 usage_nice:0 usage_softirq:0.20000000000209184 usage_steal:0 usage_system:1.2000000000080036 usage_user:4.000000000014552]
+ cpu map[cpu:cpu11 host:98d5b8dbad1c] map[usage_guest:1.0078071583066057 usage_guest_nice:0.523063861602435 usage_idle:95.53920223476884 usage_iowait:0.5162661526251292 usage_irq:0.7138529816101375 usage_nice:0.6119678488887954 usage_softirq:0.5573585443688622 usage_steal:0.2006120911289802 usage_system:1.2954475820198437 usage_user:6.885664792615023]

Documentation

Index

• type Noise
  • func (p *Noise) Apply(metrics ...telegraf.Metric) []telegraf.Metric
  • func (p *Noise) Init() error
  • func (*Noise) SampleConfig() string

Types

type Noise

type Noise struct {
	Scale         float64         `toml:"scale"`
	Min           float64         `toml:"min"`
	Max           float64         `toml:"max"`
	Mu            float64         `toml:"mu"`
	IncludeFields []string        `toml:"include_fields"`
	ExcludeFields []string        `toml:"exclude_fields"`
	NoiseType     string          `toml:"type"`
	Log           telegraf.Logger `toml:"-"`
	// contains filtered or unexported fields
}

func (*Noise) Apply

func (p *Noise) Apply(metrics ...telegraf.Metric) []telegraf.Metric

func (*Noise) Init

func (p *Noise) Init() error

Creates a filter for Include and Exclude fields and sets the desired noise distribution

func (*Noise) SampleConfig

func (*Noise) SampleConfig() string