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Improvements based on feedback, see #2251

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Patrik Nordwall 2012-09-21 16:23:55 +02:00
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23
akka-docs/cluster/cluster-usage.rst
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@ -219,19 +219,30 @@ The nodes in the cluster monitor each other by sending heartbeats to detect if a
unreachable from the rest of the cluster. The heartbeat arrival times is interpreted
by an implementation of
`The Phi Accrual Failure Detector <http://ddg.jaist.ac.jp/pub/HDY+04.pdf>`_.
It calculates a *phi* value representing the likelihood that the node is down.
The suspicion level of failure is given by a value called *phi*.
The basic idea of the phi failure detector is to express the value of *phi* on a scale that
is dynamically adjusted to reflect current network conditions.
The value of *phi* is calculated as::
phi = -log10(1 - F(timeSinceLastHeartbeat)
where F is the cumulative distribution function of a normal distribution with mean
and standard deviation estimated from historical heartbeat inter-arrival times.
In the :ref:`cluster_configuration` you can adjust the ``akka.cluster.failure-detector.threshold``
to define when a *phi* value is to be considered as a failure.
A low ``threshold`` is prone to generate many wrong suspicions but ensures
to define when a *phi* value is considered to be a failure.
A low ``threshold`` is prone to generate many false positives but ensures
a quick detection in the event of a real crash. Conversely, a high ``threshold``
generates fewer mistakes but needs more time to detect actual crashes. The
default ``threshold`` is 8 and is appropriate for most situations. However in
cloud environments, such as Amazon EC2, the value could be increased to 12 in
order to account for network issues that sometimes occur on such platforms.
The following chart illustrates how *phi* increase with increasing time since previous
heartbeat.
The following chart illustrates how *phi* increase with increasing time since the
previous heartbeat.
.. image:: images/phi1.png
@ -239,7 +250,7 @@ Phi is calculated from the mean and standard deviation of historical
inter arrival times. The previous chart is an example for standard deviation
of 200 ms. If the heartbeats arrive with less deviation the curve becomes steeper,
i.e. it's possible to determine failure more quickly. The curve looks like this for
standard deviation of 100 ms.
a standard deviation of 100 ms.
.. image:: images/phi2.png