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CFQ (Complete Fairness Queueing)
===============================
The main aim of CFQ scheduler is to provide a fair allocation of the disk
I/O bandwidth for all the processes which requests an I/O operation.
CFQ maintains the per process queue for the processes which request I/O
operation(syncronous requests). In case of asynchronous requests, all the
requests from all the processes are batched together according to their
process's I/O priority.
CFQ ioscheduler tunables
========================
slice_idle
----------
This specifies how long CFQ should idle for next request on certain cfq queues
(for sequential workloads) and service trees (for random workloads) before
queue is expired and CFQ selects next queue to dispatch from.
By default slice_idle is a non-zero value. That means by default we idle on
queues/service trees. This can be very helpful on highly seeky media like
single spindle SATA/SAS disks where we can cut down on overall number of
seeks and see improved throughput.
Setting slice_idle to 0 will remove all the idling on queues/service tree
level and one should see an overall improved throughput on faster storage
devices like multiple SATA/SAS disks in hardware RAID configuration. The down
side is that isolation provided from WRITES also goes down and notion of
IO priority becomes weaker.
So depending on storage and workload, it might be useful to set slice_idle=0.
In general I think for SATA/SAS disks and software RAID of SATA/SAS disks
keeping slice_idle enabled should be useful. For any configurations where
there are multiple spindles behind single LUN (Host based hardware RAID
controller or for storage arrays), setting slice_idle=0 might end up in better
throughput and acceptable latencies.
back_seek_max
-------------
This specifies, given in Kbytes, the maximum "distance" for backward seeking.
The distance is the amount of space from the current head location to the
sectors that are backward in terms of distance.
This parameter allows the scheduler to anticipate requests in the "backward"
direction and consider them as being the "next" if they are within this
distance from the current head location.
back_seek_penalty
-----------------
This parameter is used to compute the cost of backward seeking. If the
backward distance of request is just 1/back_seek_penalty from a "front"
request, then the seeking cost of two requests is considered equivalent.
So scheduler will not bias toward one or the other request (otherwise scheduler
will bias toward front request). Default value of back_seek_penalty is 2.
fifo_expire_async
-----------------
This parameter is used to set the timeout of asynchronous requests. Default
value of this is 248ms.
fifo_expire_sync
----------------
This parameter is used to set the timeout of synchronous requests. Default
value of this is 124ms. In case to favor synchronous requests over asynchronous
one, this value should be decreased relative to fifo_expire_async.
slice_async
-----------
This parameter is same as of slice_sync but for asynchronous queue. The
default value is 40ms.
slice_async_rq
--------------
This parameter is used to limit the dispatching of asynchronous request to
device request queue in queue's slice time. The maximum number of request that
are allowed to be dispatched also depends upon the io priority. Default value
for this is 2.
slice_sync
----------
When a queue is selected for execution, the queues IO requests are only
executed for a certain amount of time(time_slice) before switching to another
queue. This parameter is used to calculate the time slice of synchronous
queue.
time_slice is computed using the below equation:-
time_slice = slice_sync + (slice_sync/5 * (4 - prio)). To increase the
time_slice of synchronous queue, increase the value of slice_sync. Default
value is 100ms.
quantum
-------
This specifies the number of request dispatched to the device queue. In a
queue's time slice, a request will not be dispatched if the number of request
in the device exceeds this parameter. This parameter is used for synchronous
request.
In case of storage with several disk, this setting can limit the parallel
processing of request. Therefore, increasing the value can imporve the
performace although this can cause the latency of some I/O to increase due
to more number of requests.
CFQ Group scheduling
====================
CFQ supports blkio cgroup and has "blkio." prefixed files in each
blkio cgroup directory. It is weight-based and there are four knobs
for configuration - weight[_device] and leaf_weight[_device].
Internal cgroup nodes (the ones with children) can also have tasks in
them, so the former two configure how much proportion the cgroup as a
whole is entitled to at its parent's level while the latter two
configure how much proportion the tasks in the cgroup have compared to
its direct children.
Another way to think about it is assuming that each internal node has
an implicit leaf child node which hosts all the tasks whose weight is
configured by leaf_weight[_device]. Let's assume a blkio hierarchy
composed of five cgroups - root, A, B, AA and AB - with the following
weights where the names represent the hierarchy.
weight leaf_weight
root : 125 125
A : 500 750
B : 250 500
AA : 500 500
AB : 1000 500
root never has a parent making its weight is meaningless. For backward
compatibility, weight is always kept in sync with leaf_weight. B, AA
and AB have no child and thus its tasks have no children cgroup to
compete with. They always get 100% of what the cgroup won at the
parent level. Considering only the weights which matter, the hierarchy
looks like the following.
root
/ | \
A B leaf
500 250 125
/ | \
AA AB leaf
500 1000 750
If all cgroups have active IOs and competing with each other, disk
time will be distributed like the following.
Distribution below root. The total active weight at this level is
A:500 + B:250 + C:125 = 875.
root-leaf : 125 / 875 =~ 14%
A : 500 / 875 =~ 57%
B(-leaf) : 250 / 875 =~ 28%
A has children and further distributes its 57% among the children and
the implicit leaf node. The total active weight at this level is
AA:500 + AB:1000 + A-leaf:750 = 2250.
A-leaf : ( 750 / 2250) * A =~ 19%
AA(-leaf) : ( 500 / 2250) * A =~ 12%
AB(-leaf) : (1000 / 2250) * A =~ 25%
CFQ IOPS Mode for group scheduling
===================================
Basic CFQ design is to provide priority based time slices. Higher priority
process gets bigger time slice and lower priority process gets smaller time
slice. Measuring time becomes harder if storage is fast and supports NCQ and
it would be better to dispatch multiple requests from multiple cfq queues in
request queue at a time. In such scenario, it is not possible to measure time
consumed by single queue accurately.
What is possible though is to measure number of requests dispatched from a
single queue and also allow dispatch from multiple cfq queue at the same time.
This effectively becomes the fairness in terms of IOPS (IO operations per
second).
If one sets slice_idle=0 and if storage supports NCQ, CFQ internally switches
to IOPS mode and starts providing fairness in terms of number of requests
dispatched. Note that this mode switching takes effect only for group
scheduling. For non-cgroup users nothing should change.
CFQ IO scheduler Idling Theory
===============================
Idling on a queue is primarily about waiting for the next request to come
on same queue after completion of a request. In this process CFQ will not
dispatch requests from other cfq queues even if requests are pending there.
The rationale behind idling is that it can cut down on number of seeks
on rotational media. For example, if a process is doing dependent
sequential reads (next read will come on only after completion of previous
one), then not dispatching request from other queue should help as we
did not move the disk head and kept on dispatching sequential IO from
one queue.
CFQ has following service trees and various queues are put on these trees.
sync-idle sync-noidle async
All cfq queues doing synchronous sequential IO go on to sync-idle tree.
On this tree we idle on each queue individually.
All synchronous non-sequential queues go on sync-noidle tree. Also any
request which are marked with REQ_NOIDLE go on this service tree. On this
tree we do not idle on individual queues instead idle on the whole group
of queues or the tree. So if there are 4 queues waiting for IO to dispatch
we will idle only once last queue has dispatched the IO and there is
no more IO on this service tree.
All async writes go on async service tree. There is no idling on async
queues.
CFQ has some optimizations for SSDs and if it detects a non-rotational
media which can support higher queue depth (multiple requests at in
flight at a time), then it cuts down on idling of individual queues and
all the queues move to sync-noidle tree and only tree idle remains. This
tree idling provides isolation with buffered write queues on async tree.
FAQ
===
Q1. Why to idle at all on queues marked with REQ_NOIDLE.
A1. We only do tree idle (all queues on sync-noidle tree) on queues marked
with REQ_NOIDLE. This helps in providing isolation with all the sync-idle
queues. Otherwise in presence of many sequential readers, other
synchronous IO might not get fair share of disk.
For example, if there are 10 sequential readers doing IO and they get
100ms each. If a REQ_NOIDLE request comes in, it will be scheduled
roughly after 1 second. If after completion of REQ_NOIDLE request we
do not idle, and after a couple of milli seconds a another REQ_NOIDLE
request comes in, again it will be scheduled after 1second. Repeat it
and notice how a workload can lose its disk share and suffer due to
multiple sequential readers.
fsync can generate dependent IO where bunch of data is written in the
context of fsync, and later some journaling data is written. Journaling
data comes in only after fsync has finished its IO (atleast for ext4
that seemed to be the case). Now if one decides not to idle on fsync
thread due to REQ_NOIDLE, then next journaling write will not get
scheduled for another second. A process doing small fsync, will suffer
badly in presence of multiple sequential readers.
Hence doing tree idling on threads using REQ_NOIDLE flag on requests
provides isolation from multiple sequential readers and at the same
time we do not idle on individual threads.
Q2. When to specify REQ_NOIDLE
A2. I would think whenever one is doing synchronous write and not expecting
more writes to be dispatched from same context soon, should be able
to specify REQ_NOIDLE on writes and that probably should work well for
most of the cases.
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