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RAINBOW Monitoring


The Rainbow Monitoring Agent provides a containerized service that captures monitoring metrics from the underlying fog node infrastructure, the containerized execution environments and/or performance metrics from the deployed IoT applications.In the initial implementation, NetData is used as the main metric collector and on top of that are built the sensing and disseminating functionalities of Rainbow’s project


  • Coordinates the metric collection process
  • Easy to reuse for various layers of the fog continuum with different metric collectors
  • Takes into consideration rapid changes that occur due to the enforcement of runtime scaling actions


  • Probes: The actual metric collectors that adhere to a common metric collection interface
  • Exporters: Exports the formatted or aggregated data to different endpoints
  • Controller: Orchistrates the execution of Sensing Units (Probes) and Dissemination Units (Exporters)


The architecture of the RAINBOW Monitoring follows an agent-based architecture that embraces the producer-consumer paradigm. This approach provides interoperable, scalable and real-time monitoring for extracting both infrastructure and application behaviour data from deployed IoT services.The RAINBOW Monitoring runs in a non-intrusive and transparent manner to underlying fog environments as neither the metric collection process nor the metric distribution and storage are dependent to underlying platform APIs (e.g., fog-node specific) and communication mechanisms. The following image depicts a high-level and abstract overview of the Monitoring Agent.


The Monitoring Agent consists of general interfaces for both probes and exporters. This can facilitate the process of adding new custom sensing and dissemination units that the user may want to use. In addition, users can configure their sensing and dissemination units through a YAML file, where they can specify the metric groups that they are mostly interested in collecting metrics for. At the first version of the monitoring agent the default monitoring unit used is Netdata.

In the following yaml example we can see that we have 3 main hierarchies:

node_id: is the unique identifier of the node

sensing-units: are the probes that will collect metrics from the node

dissemination-units: represents the exporters that will disseminate metrics

Regarding sensing-units, a user can define the general-periodicity, which is the general sensing rate of the probe. A user can define multiple sensing-units and they can specify the periodicity for each one of them. They can also specify which metric groups they don’t want the probe to collect metrics for with the disabled-metric-groups option, and with the metric-groups they can override sensing preferences on specific groups.

In the dissemination-units section it’s possible to configure basic information for each of the exporters the user wants to use, for example the port, hostname, periodicity, etc

node_id: raspberry_pi_4_in_region_3
sensing-units: # configuration for probes
    general-periodicity: 5s # general sensing rate
    DefaultMonitoring: # specific implementation of the sensing interface
        periodicity: 5s
        disabled-metric-groups: # metric groups that the system will not start at all (e.g. CPU, memory, disk, network)
            - "disk"
        metric-groups: #override sensing preferences on specific groups
            - name: "memory"
              periodicity: 15s
            - name: "cpu"
              periodicity: 1s
    UserDefinedMetrics: # specific implementation of sensing interface for user-defined metrics
        periodicity: 5s
        - "/rainbow-metrics/"
        dissemination-units: # configuration for exporters
         port: 4200
         path: /api/metrics
         hostname: ignite-server
         port: 50000
         delivery: push
         periodicity: 30s
         aggregation: no

How to add a new Probe

Developers are free to create their own Monitoring Probes and Metrics, by adhering to the properties defined in the Monitoring Probe API which provides a common API interface and abstractions hiding the complexity of the underlying Probe functionality. The following figure depicts the implementation of an ExampleProbe which includes the definition of two SimpleMetric’s, denoted as metric1 and metric2, which periodically report random values respectively. In turn, a CounterMetric and a TimerMetric are also defined. In this figure it is observed that for a user to develop a Monitoring Probe, he/she must only provide default values for the Probe periodicity and a name, a short description of the offered functionality, and a concrete implementation of the collect() method which, as denoted by the name, defines how metric values are updated.

from probes.Metric import SimpleMetric, CounterMetric, DiffMetric, TimerMetric
    from probes.Probe import Probe
    class ExampleProbe(Probe):
        def __init__(self, name="ExampleProbe", periodicity=5):
	    super(ExampleProbe, self).__init__(name, periodicity)

	    self.myMetric1 = SimpleMetric('myMetric1', '%', 'random double between 0 and 10', 0, 10)
	    self.myMetric2 = SimpleMetric('myMetric2', '#', 'random int between 0 and 1000', 0, 1000, higherIsBetter=False)
    	    self.myMetric3 = CounterMetric('myMetric3', '#', 'counter incrementing by 1 and resetting at 20', maxVal=20)
	    self.myMetric4 = DiffMetric('myMetric4', '#', 'scaled difference from previous val')
	    self.myMetric5 = TimerMetric('myMetric5', maxVal=10)

        def get_desc(self):
	    return "ExampleProbe collects some dummy metrics..."

        def collect(self):

	    d = random.uniform(0, 100)
	    i = random.randint(0, 1000)


Probes’ metrics may take other advanced forms, denoted as metric handlers. The user is also able to define the metric handlers they prefer for their custom probe.

SimpleMetricEmits a single value for a referenced metric where the value is given by an external process. Is considered the base upon which all other metric handlers are extended from.
CounterMetricEmits a counter-increased value for a reference metric based on either a pre-defined increment (e.g., +1) or a given increment.
TimerMetricEmits the time consumed for the completion of a referenced task (e.g., API call).
DiffMetricEmits the proportional difference of the current collected value from the previous value.


The whole monitoring agent runs in the container and the user needs only to run the docker build command.

docker build -t rainbow-monitoring:v0.01 .

The default configurations of the service are already injected and are placed at config.yaml file.

Users can override the configurations by injecting a new config file to the /code/configs.yaml folder of the container

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