doc/design-thoughts/rate-shaping.txt

2010/01/24 - Design of multi-criteria request rate shaping.

We want to be able to rate-shape traffic on multiple cirteria. For instance, we
may want to support shaping of per-host header requests, as well as per source.

In order to achieve this, we will use checkpoints, one per criterion. Each of
these checkpoints will consist in a test, a rate counter and a queue.

A request reaches the checkpoint and checks the counter. If the counter is
below the limit, it is updated and the request continues. If the limit is
reached, the request attaches itself into the queue and sleeps. The sleep time
is computed from the queue status, and updates the queue status.

A task is dedicated to each queue. Its sole purpose is to be woken up when the
next task may wake up, to check the frequency counter, wake as many requests as
possible and update the counter. All the woken up requests are detached from
the queue. Maybe the task dedicated to the queue can be avoided and replaced
with all queued tasks's sleep counters, though this looks tricky. Or maybe it's
just the first request in the queue that should be responsible for waking up
other tasks, and not to forget to pass on this responsibility to next tasks if
it leaves the queue.

The woken up request then goes on evaluating other criteria and possibly sleeps
again on another one. In the end, the task will have waited the amount of time
required to pass all checkpoints, and all checkpoints will be able to maintain
a permanent load of exactly their limit if enough streams flow through them.

Since a request can only sleep in one queue at a time, it makes sense to use a
linked list element in each session to attach it to any queue. It could very
well be shared with the pendconn hooks which could then be part of the session.

This mechanism could be used to rate-shape sessions and requests per backend
and per server.

When rate-shaping on dynamic criteria, such as the source IP address, we have
to first extract the data pattern, then look it up in a table very similar to
the stickiness tables, but with a frequency counter. At the checkpoint, the
pattern is looked up, the entry created or refreshed, and the frequency counter
updated and checked. Then the request either goes on or sleeps as described
above, but if it sleeps, it's still in the checkpoint's queue, but with a date
computed from the criterion's status.

This means that we need 3 distinct features :

   - optional pattern extraction
   - per-pattern or per-queue frequency counter
   - time-ordered queue with a task

Based on past experiences with frequency counters, it does not appear very easy
to exactly compute sleep delays in advance for multiple requests. So most
likely we'll have to run per-criterion queues too, with only the head of the
queue holding a wake-up timeout.

This finally leads us to the following :

   - optional pattern extraction
   - per-pattern or per-queue frequency counter
   - per-frequency counter queue
   - head of the queue serves as a global queue timer.

This brings us to a very flexible architecture :
   - 1 list of rule-based checkpoints per frontend
   - 1 list of rule-based checkpoints per backend
   - 1 list of rule-based checkpoints per server

Each of these lists have a lot of rules conditioned by ACLs, just like the
use-backend rules, except that all rules are evaluated in turn.

Since we might sometimes just want to enable that without setting any limit and
just for enabling control in ACLs (or logging ?), we should probably try to
find a flexible way of declaring just a counter without a queue.

These checkpoints could be of two types :
  - rate-limit (described here)
  - concurrency-limit (very similar with the counter and no timer). This
    feature would require to keep track of all accounted criteria in a
    request so that they can be released upon request completion.

It should be possible to define a max of requests in the queue, above which a
503 is returned. The same applies for the max delay in the queue. We could have
it per-task (currently it's the connection timeout) and abort tasks with a 503
when the delay is exceeded.

Per-server connection concurrency could be converted to use this mechanism
which is very similar.

The construct should be flexible enough so that the counters may be checked
from ACLs. That would allow to reject connections or switch to an alternate
backend when some limits are reached.