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@@ -207,11 +207,11 @@ Chapter 1. Admin Guide
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4.1. Feedback Algorithm (FEEDBACK)
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- When running openser on different machines, one has to adjust the drop
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+ When running Kamailio on different machines, one has to adjust the drop
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rates for the static algorithms to maintain a sub 100% load average or
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packets start getting dropped in the network stack. While this is not
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in itself difficult, it isn't neither accurate nor trivial: another
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- server taking a notable fraction of the cpu time will require re-tuning
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+ server taking a notable fraction of the CPU time will require re-tuning
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the parameters.
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While tuning the drop rates from the outside based on a certain factor
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@@ -226,7 +226,7 @@ Chapter 1. Admin Guide
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adjusted dynamically based on the load factor so that the load factor
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always drifts towards the specified limit (or setpoint, in PID terms).
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- As reading the cpu load average is relatively expensive (opening
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+ As reading the CPU load average is relatively expensive (opening
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/proc/stat, parsing it, etc), this only happens once every
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timer_interval seconds and consequently the FEEDBACK value is only at
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these intervals recomputed. This in turn makes it difficult for the
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@@ -421,7 +421,7 @@ modparam("ratelimit", "pipe", "4:NETWORK:10000")
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8.1. rl.stats
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- Lists the parameters and variabiles in the ratelimit module.
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+ Lists the parameters and variables in the ratelimit module.
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Name: rl.stats
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@@ -507,7 +507,7 @@ modparam("ratelimit", "pipe", "4:NETWORK:10000")
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8.8. rl.push_load
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- Force the value of the load parameter. This methos is usefull for
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+ Force the value of the load parameter. This method is useful for
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testing the Feedback algorithm.
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Name: rl.push_load
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