remove async replication that is AF specific (#402)

Author: nerpaula

site/content/3.12/deploy/architecture/replication.md

@@ -50,65 +50,4 @@ In addition to the replication factor, there is a **writeConcern** that
 specifies the minimum number of in-sync followers required for write operations.
 If you specify the `writeConcern` parameter with a value greater than `1`, the
 collection's leader shards are locked down for writing as soon as too few
-followers are available.
-
-## Asynchronous replication
-
-When using asynchronous replication, _Followers_ connect to a _Leader_ and apply
-all the events from the Leader log in the same order locally. As a result, the
-_Followers_ end up with the same state of data as the _Leader_.
-
-_Followers_ are only eventually consistent with the _Leader_.
-
-Transactions are honored in replication, i.e. transactional write operations 
-become visible on _Followers_ atomically.
-
-All write operations are logged to the Leader's _write-ahead log_. Therefore,
-asynchronous replication in ArangoDB cannot be used for write-scaling. The main
-purposes of this type of replication are to provide read-scalability and
-hot standby servers.
-
-It is possible to connect multiple _Follower_ to the same _Leader_. _Followers_
-should be used as read-only instances, and no user-initiated write operations 
-should be carried out on them. Otherwise, data conflicts may occur that cannot
-be solved automatically, and this makes the replication stop.
-
-In an asynchronous replication scenario, Followers _pull_ changes
-from the _Leader_. _Followers_ need to know to which _Leader_ they should 
-connect to, but a _Leader_ is not aware of the _Followers_ that replicate from it. 
-When the network connection between the _Leader_ and a _Follower_ goes down, write 
-operations on the Leader can continue normally. When the network is up again, _Followers_ 
-can reconnect to the _Leader_ and transfer the remaining changes. This 
-happens automatically, provided _Followers_ are configured appropriately.
-
-### Replication lag
-
-As described above, write operations are applied first in the _Leader_, and then applied 
-in the _Followers_. 
-
-For example, let's assume a write operation is executed in the _Leader_ 
-at point in time _t0_. To make a _Follower_ apply the same operation, it must first 
-fetch the write operation's data from Leader's write-ahead log, then parse it and 
-apply it locally. This happens at some point in time after _t0_, let's say _t1_. 
-
-The difference between _t1_ and _t0_ is called the _replication lag_, and it is unavoidable 
-in asynchronous replication. The amount of replication _lag_ depends on many factors, a 
-few of which are:
-
-- the network capacity between the _Followers_ and the _Leader_
-- the load of the _Leader_ and the _Followers_
-- the frequency in which _Followers_ poll the _Leader_ for updates
-
-Between _t0_ and _t1_, the state of data on the _Leader_ is newer than the state of data
-on the _Followers_. At point in time _t1_, the state of data on the _Leader_ and _Followers_
-is consistent again (provided no new data modifications happened on the _Leader_ in
-between). Thus, the replication leads to an _eventually consistent_ state of data.
-
-### Replication overhead
-
-As the _Leader_ servers are logging any write operation in the _write-ahead-log_
-anyway, replication doesn't cause any extra overhead on the _Leader_. However, it
-causes some overhead for the _Leader_ to serve incoming read
-requests of the _Followers_. However, returning the requested data is a trivial
-task for the _Leader_ and should not result in a notable performance
-degradation in production.
+followers are available.