block: introduce the BFQ-v7r3 I/O sched for 3.4

Add the BFQ-v7r3 I/O scheduler to 3.4.
The general structure is borrowed from CFQ, as much of the code for
handling I/O contexts. Over time, several useful features have been
ported from CFQ as well (details in the changelog in README.BFQ). A
(bfq_)queue is associated to each task doing I/O on a device, and each
time a scheduling decision has to be made a queue is selected and served
until it expires.

    - Slices are given in the service domain: tasks are assigned
      budgets, measured in number of sectors. Once got the disk, a task
      must however consume its assigned budget within a configurable
      maximum time (by default, the maximum possible value of the
      budgets is automatically computed to comply with this timeout).
      This allows the desired latency vs "throughput boosting" tradeoff
      to be set.

    - Budgets are scheduled according to a variant of WF2Q+, implemented
      using an augmented rb-tree to take eligibility into account while
      preserving an O(log N) overall complexity.

    - A low-latency tunable is provided; if enabled, both interactive
      and soft real-time applications are guaranteed a very low latency.

    - Latency guarantees are preserved also in the presence of NCQ.

    - Also with flash-based devices, a high throughput is achieved
      while still preserving latency guarantees.

    - BFQ features Early Queue Merge (EQM), a sort of fusion of the
      cooperating-queue-merging and the preemption mechanisms present
      in CFQ. EQM is in fact a unified mechanism that tries to get a
      sequential read pattern, and hence a high throughput, with any
      set of processes performing interleaved I/O over a contiguous
      sequence of sectors.

    - BFQ supports full hierarchical scheduling, exporting a cgroups
      interface.  Since each node has a full scheduler, each group can
      be assigned its own weight.

    - If the cgroups interface is not used, only I/O priorities can be
      assigned to processes, with ioprio values mapped to weights
      with the relation weight = IOPRIO_BE_NR - ioprio.

    - ioprio classes are served in strict priority order, i.e., lower
      priority queues are not served as long as there are higher
      priority queues.  Among queues in the same class the bandwidth is
      distributed in proportion to the weight of each queue. A very
      thin extra bandwidth is however guaranteed to the Idle class, to
      prevent it from starving.

Signed-off-by: Paolo Valente <paolo.valente@unimore.it>
Signed-off-by: Arianna Avanzini <avanzini.arianna@gmail.com>

block: cgroups, kconfig, build bits for BFQ-v7r3-3.4

Update Kconfig.iosched and do the related Makefile changes to include
kernel configuration options for BFQ. Also add the bfqio controller
to the cgroups subsystem.

Signed-off-by: Paolo Valente <paolo.valente@unimore.it>
Signed-off-by: Arianna Avanzini <avanzini.arianna@gmail.com>

block, bfq: add Early Queue Merge (EQM) to BFQ-v7r3 for 3.4.0

A set of processes may happen  to  perform interleaved reads, i.e., requests
whose union would give rise to a  sequential read  pattern.  There are two
typical  cases: in the first  case,   processes  read  fixed-size chunks of
data at a fixed distance from each other, while in the second case processes
may read variable-size chunks at  variable distances. The latter case occurs
for  example with  KVM, which  splits the  I/O generated  by the  guest into
multiple chunks,  and lets these chunks  be served by a  pool of cooperating
processes,  iteratively  assigning  the  next  chunk of  I/O  to  the first
available  process. CFQ  uses actual  queue merging  for the  first type of
rocesses, whereas it  uses preemption to get a sequential  read pattern out
of the read requests  performed by the second type of  processes. In the end
it uses  two different  mechanisms to  achieve the  same goal: boosting the
throughput with interleaved I/O.

This patch introduces  Early Queue Merge (EQM), a unified mechanism to get a
sequential  read pattern  with both  types of  processes. The  main idea is
checking newly arrived requests against the next request of the active queue
both in case of actual request insert and in case of request merge. By doing
so, both the types of processes can be handled by just merging their queues.
EQM is  then simpler and  more compact than the  pair of mechanisms used in
CFQ.

Finally, EQM  also preserves the  typical low-latency properties of BFQ, by
properly restoring the weight-raising state of  a queue when it gets back to
a non-merged state.

Signed-off-by: Mauro Andreolini <mauro.andreolini@unimore.it>
Signed-off-by: Arianna Avanzini <avanzini.arianna@gmail.com>
Reviewed-by: Paolo Valente <paolo.valente@unimore.it>
block: introduce SIO I/O scheduler
block: introduce FIOPS I/O scheduler

FIOPS (Fair IOPS) ioscheduler is IOPS based ioscheduler, so only targets
for drive without I/O seek. It's quite similar like CFQ, but the dispatch
decision is made according to IOPS instead of slice.

The algorithm is simple. Drive has a service tree, and each task lives in
the tree. The key into the tree is called vios (virtual I/O). Every request
has vios, which is calculated according to its ioprio, request size and so
on. Task's vios is the sum of vios of all requests it dispatches. FIOPS
always selects task with minimum vios in the service tree and let the task
dispatch request. The dispatched request's vios is then added to the task's
vios and the task is repositioned in the sevice tree.

Unlike CFQ, FIOPS doesn't have separate sync/async queues, because with I/O
less writeback, usually a task can only dispatch either sync or async requests.
Bias read or write request can still be done with read/write scale.

One issue is if workload iodepth is lower than drive queue_depth, IOPS
share of a task might not be strictly according to its priority, request
Bias read or write request can still be done with read/write scale.

One issue is if workload iodepth is lower than drive queue_depth, IOPS
share of a task might not be strictly according to its priority, request
size and so on. In this case, the drive is in idle actually. Solving the
problem need make drive idle, so impact performance. I believe CFQ isn't
completely fair between tasks in such case too.

Signed-off-by: Shaohua Li <shaohua.li@intel.com>

Signed-off-by: Paul Reioux <reioux@gmail.com>

block: fiops read/write request scale

read/write speed of Flash based storage usually is different. For example,
in my SSD maxium thoughput of read is about 3 times faster than that of
write. Add a scale to differenate read and write. Also add a tunable, so
user can assign different scale for read and write.

By default, the scale is 1:1, which means the scale is a noop.

Signed-off-by: Shaohua Li <shaohua.li@intel.com>

block: fiops sync/async scale

CFQ gives 2.5 times more share to sync workload. This matches CFQ.

Note this is different with the read/write scale. We have 3 types of
requests:
1. read
2. sync write
3. write
CFQ doesn't differentitate type 1 and 2, but request cost of 1 and 2
are usually different for flash based storage. So we have both sync/async
and read/write scale here.

Signed-off-by: Shaohua Li <shaohua.li@intel.com>

block: fiops add ioprio support

Add CFQ-like ioprio support. Priority A will get 20% more share than priority
A+1, which matches CFQ.

Signed-off-by: Shaohua Li <shaohua.li@intel.com>

block: fiops preserve vios key for deep queue depth workload

If the task has running request, even it's added into service tree newly,
we preserve its vios key, so it will not lost its share. This should work
for task driving big queue depth. For single depth task, there is no approach
to preserve its vios key.

Signed-off-by: Shaohua Li <shaohua.li@intel.com>

block: fiops bias sync workload

If there are async requests running, delay async workload. Otherwise
async workload (usually very deep iodepth) will use all queue iodepth
and later sync requests will get long delayed. The idea is from CFQ.

Signed-off-by: Shaohua Li <shaohua.li@intel.com>