Both user-provided and built-in metrics collectors can optionally use `Hyperic Sigar <http://www.hyperic.com/products/sigar>`_
for a wider and more accurate range of metrics compared to what can be retrieved from ordinary JMX MBeans.
Sigar is using a native o/s library, and requires library provisioning, i.e.
deployment, extraction and loading of the o/s native library into JVM at runtime.
User can provision Sigar classes and native library in one of the following ways:
#. Use `Kamon sigar-loader <https://github.com/kamon-io/sigar-loader>`_ as a project dependency for the user project.
Metrics extension will extract and load sigar library on demand with help of Kamon sigar provisioner.
#. Use `Kamon sigar-loader <https://github.com/kamon-io/sigar-loader>`_ as java agent: ``java -javaagent:/path/to/sigar-loader.jar``.
Kamon sigar loader agent will extract and load sigar library during JVM start.
#. Place ``sigar.jar`` on the ``classpath`` and Sigar native library for the o/s on the ``java.library.path``.
User is required to manage both project dependency and library deployment manually.
To enable usage of Sigar you can add the following dependency to the user project
::
<dependency>
<groupId>io.kamon</groupId>
<artifactId>sigar-loader</artifactId>
<version>@sigarLoaderVersion@</version>
</dependency>
You can download Kamon sigar-loader from `Maven Central <http://search.maven.org/#search%7Cga%7C1%7Csigar-loader>`_
Adaptive Load Balancing
-----------------------
The ``AdaptiveLoadBalancingPool`` / ``AdaptiveLoadBalancingGroup`` performs load balancing of messages to cluster nodes based on the cluster metrics data.
It uses random selection of routees with probabilities derived from the remaining capacity of the corresponding node.
It can be configured to use a specific MetricsSelector to produce the probabilities, a.k.a. weights:
*``heap`` / ``HeapMetricsSelector`` - Used and max JVM heap memory. Weights based on remaining heap capacity; (max - used) / max
*``load`` / ``SystemLoadAverageMetricsSelector`` - System load average for the past 1 minute, corresponding value can be found in ``top`` of Linux systems. The system is possibly nearing a bottleneck if the system load average is nearing number of cpus/cores. Weights based on remaining load capacity; 1 - (load / processors)
*``cpu`` / ``CpuMetricsSelector`` - CPU utilization in percentage, sum of User + Sys + Nice + Wait. Weights based on remaining cpu capacity; 1 - utilization
*``mix`` / ``MixMetricsSelector`` - Combines heap, cpu and load. Weights based on mean of remaining capacity of the combined selectors.
* Any custom implementation of ``akka.cluster.metrics.MetricsSelector``
The collected metrics values are smoothed with `exponential weighted moving average <http://en.wikipedia.org/wiki/Moving_average#Exponential_moving_average>`_. In the :ref:`cluster_configuration_java` you can adjust how quickly past data is decayed compared to new data.
Let's take a look at this router in action. What can be more demanding than calculating factorials?
The backend worker that performs the factorial calculation:
