BFQ (Budget Fair Queueing)
  ==========================
  
  BFQ is a proportional-share I/O scheduler, with some extra
  low-latency capabilities. In addition to cgroups support (blkio or io
  controllers), BFQ's main features are:
  - BFQ guarantees a high system and application responsiveness, and a
    low latency for time-sensitive applications, such as audio or video
    players;
  - BFQ distributes bandwidth, and not just time, among processes or
    groups (switching back to time distribution when needed to keep
    throughput high).

  In its default configuration, BFQ privileges latency over
  throughput. So, when needed for achieving a lower latency, BFQ builds
  schedules that may lead to a lower throughput. If your main or only
  goal, for a given device, is to achieve the maximum-possible
  throughput at all times, then do switch off all low-latency heuristics

  for that device, by setting low_latency to 0. See Section 3 for
  details on how to configure BFQ for the desired tradeoff between
  latency and throughput, or on how to maximize throughput.

  On average CPUs, the current version of BFQ can handle devices
  performing at most ~30K IOPS; at most ~50 KIOPS on faster CPUs. As a
  reference, 30-50 KIOPS correspond to very high bandwidths with
  sequential I/O (e.g., 8-12 GB/s if I/O requests are 256 KB large), and

  to 120-200 MB/s with 4KB random I/O. BFQ is currently being tested on
  multi-queue devices too.

  
  The table of contents follow. Impatients can just jump to Section 3.
  
  CONTENTS
  
  1. When may BFQ be useful?
   1-1 Personal systems
   1-2 Server systems
  2. How does BFQ work?

  3. What are BFQ's tunables and how to properly configure BFQ?

  4. BFQ group scheduling
   4-1 Service guarantees provided
   4-2 Interface
  
  1. When may BFQ be useful?
  ==========================
  
  BFQ provides the following benefits on personal and server systems.
  
  1-1 Personal systems
  --------------------
  
  Low latency for interactive applications
  
  Regardless of the actual background workload, BFQ guarantees that, for
  interactive tasks, the storage device is virtually as responsive as if
  it was idle. For example, even if one or more of the following
  background workloads are being executed:
  - one or more large files are being read, written or copied,
  - a tree of source files is being compiled,
  - one or more virtual machines are performing I/O,
  - a software update is in progress,
  - indexing daemons are scanning filesystems and updating their
    databases,
  starting an application or loading a file from within an application
  takes about the same time as if the storage device was idle. As a
  comparison, with CFQ, NOOP or DEADLINE, and in the same conditions,
  applications experience high latencies, or even become unresponsive
  until the background workload terminates (also on SSDs).
  
  Low latency for soft real-time applications
  
  Also soft real-time applications, such as audio and video
  players/streamers, enjoy a low latency and a low drop rate, regardless
  of the background I/O workload. As a consequence, these applications
  do not suffer from almost any glitch due to the background workload.
  
  Higher speed for code-development tasks
  
  If some additional workload happens to be executed in parallel, then
  BFQ executes the I/O-related components of typical code-development
  tasks (compilation, checkout, merge, ...) much more quickly than CFQ,
  NOOP or DEADLINE.
  
  High throughput
  
  On hard disks, BFQ achieves up to 30% higher throughput than CFQ, and
  up to 150% higher throughput than DEADLINE and NOOP, with all the
  sequential workloads considered in our tests. With random workloads,
  and with all the workloads on flash-based devices, BFQ achieves,
  instead, about the same throughput as the other schedulers.
  
  Strong fairness, bandwidth and delay guarantees
  
  BFQ distributes the device throughput, and not just the device time,
  among I/O-bound applications in proportion their weights, with any
  workload and regardless of the device parameters. From these bandwidth
  guarantees, it is possible to compute tight per-I/O-request delay
  guarantees by a simple formula. If not configured for strict service
  guarantees, BFQ switches to time-based resource sharing (only) for
  applications that would otherwise cause a throughput loss.
  
  1-2 Server systems
  ------------------
  
  Most benefits for server systems follow from the same service
  properties as above. In particular, regardless of whether additional,
  possibly heavy workloads are being served, BFQ guarantees:
  
  . audio and video-streaming with zero or very low jitter and drop
    rate;
  
  . fast retrieval of WEB pages and embedded objects;
  
  . real-time recording of data in live-dumping applications (e.g.,
    packet logging);
  
  . responsiveness in local and remote access to a server.
  
  
  2. How does BFQ work?
  =====================
  
  BFQ is a proportional-share I/O scheduler, whose general structure,
  plus a lot of code, are borrowed from CFQ.
  
  - Each process doing I/O on a device is associated with a weight and a
    (bfq_)queue.
  
  - BFQ grants exclusive access to the device, for a while, to one queue
    (process) at a time, and implements this service model by
    associating every queue with a budget, measured in number of
    sectors.
  
    - After a queue is granted access to the device, the budget of the
      queue is decremented, on each request dispatch, by the size of the
      request.
  
    - The in-service queue is expired, i.e., its service is suspended,
      only if one of the following events occurs: 1) the queue finishes
      its budget, 2) the queue empties, 3) a "budget timeout" fires.
  
      - The budget timeout prevents processes doing random I/O from
        holding the device for too long and dramatically reducing
        throughput.
  
      - Actually, as in CFQ, a queue associated with a process issuing
        sync requests may not be expired immediately when it empties. In
        contrast, BFQ may idle the device for a short time interval,
        giving the process the chance to go on being served if it issues
        a new request in time. Device idling typically boosts the

        throughput on rotational devices and on non-queueing flash-based
        devices, if processes do synchronous and sequential I/O. In
        addition, under BFQ, device idling is also instrumental in
        guaranteeing the desired throughput fraction to processes
        issuing sync requests (see the description of the slice_idle
        tunable in this document, or [1, 2], for more details).

  
        - With respect to idling for service guarantees, if several
  	processes are competing for the device at the same time, but

  	all processes and groups have the same weight, then BFQ
  	guarantees the expected throughput distribution without ever
  	idling the device. Throughput is thus as high as possible in
  	this common scenario.

       - On flash-based storage with internal queueing of commands
         (typically NCQ), device idling happens to be always detrimental
         for throughput. So, with these devices, BFQ performs idling
         only when strictly needed for service guarantees, i.e., for
         guaranteeing low latency or fairness. In these cases, overall
         throughput may be sub-optimal. No solution currently exists to
         provide both strong service guarantees and optimal throughput
         on devices with internal queueing.

    - If low-latency mode is enabled (default configuration), BFQ
      executes some special heuristics to detect interactive and soft
      real-time applications (e.g., video or audio players/streamers),
      and to reduce their latency. The most important action taken to
      achieve this goal is to give to the queues associated with these
      applications more than their fair share of the device
      throughput. For brevity, we call just "weight-raising" the whole
      sets of actions taken by BFQ to privilege these queues. In
      particular, BFQ provides a milder form of weight-raising for
      interactive applications, and a stronger form for soft real-time
      applications.
  
    - BFQ automatically deactivates idling for queues born in a burst of
      queue creations. In fact, these queues are usually associated with
      the processes of applications and services that benefit mostly
      from a high throughput. Examples are systemd during boot, or git
      grep.
  
    - As CFQ, BFQ merges queues performing interleaved I/O, i.e.,
      performing random I/O that becomes mostly sequential if
      merged. Differently from CFQ, BFQ achieves this goal with a more
      reactive mechanism, called Early Queue Merge (EQM). EQM is so
      responsive in detecting interleaved I/O (cooperating processes),
      that it enables BFQ to achieve a high throughput, by queue
      merging, even for queues for which CFQ needs a different
      mechanism, preemption, to get a high throughput. As such EQM is a
      unified mechanism to achieve a high throughput with interleaved
      I/O.
  
    - Queues are scheduled according to a variant of WF2Q+, named
      B-WF2Q+, and implemented using an augmented rb-tree to preserve an
      O(log N) overall complexity.  See [2] for more details. B-WF2Q+ is

      also ready for hierarchical scheduling, details in Section 4.

  
    - B-WF2Q+ guarantees a tight deviation with respect to an ideal,
      perfectly fair, and smooth service. In particular, B-WF2Q+
      guarantees that each queue receives a fraction of the device
      throughput proportional to its weight, even if the throughput
      fluctuates, and regardless of: the device parameters, the current
      workload and the budgets assigned to the queue.
  
    - The last, budget-independence, property (although probably
      counterintuitive in the first place) is definitely beneficial, for
      the following reasons:
  
      - First, with any proportional-share scheduler, the maximum
        deviation with respect to an ideal service is proportional to
        the maximum budget (slice) assigned to queues. As a consequence,
        BFQ can keep this deviation tight not only because of the
        accurate service of B-WF2Q+, but also because BFQ *does not*
        need to assign a larger budget to a queue to let the queue
        receive a higher fraction of the device throughput.
  
      - Second, BFQ is free to choose, for every process (queue), the
        budget that best fits the needs of the process, or best
        leverages the I/O pattern of the process. In particular, BFQ
        updates queue budgets with a simple feedback-loop algorithm that
        allows a high throughput to be achieved, while still providing
        tight latency guarantees to time-sensitive applications. When
        the in-service queue expires, this algorithm computes the next
        budget of the queue so as to:
  
        - Let large budgets be eventually assigned to the queues
  	associated with I/O-bound applications performing sequential
  	I/O: in fact, the longer these applications are served once
  	got access to the device, the higher the throughput is.
  
        - Let small budgets be eventually assigned to the queues
  	associated with time-sensitive applications (which typically
  	perform sporadic and short I/O), because, the smaller the
  	budget assigned to a queue waiting for service is, the sooner
  	B-WF2Q+ will serve that queue (Subsec 3.3 in [2]).
  
  - If several processes are competing for the device at the same time,
    but all processes and groups have the same weight, then BFQ
    guarantees the expected throughput distribution without ever idling
    the device. It uses preemption instead. Throughput is then much
    higher in this common scenario.
  
  - 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.

  3. What are BFQ's tunables and how to properly configure BFQ?
  =============================================================

  Most BFQ tunables affect service guarantees (basically latency and
  fairness) and throughput. For full details on how to choose the
  desired tradeoff between service guarantees and throughput, see the
  parameters slice_idle, strict_guarantees and low_latency. For details
  on how to maximise throughput, see slice_idle, timeout_sync and
  max_budget. The other performance-related parameters have been
  inherited from, and have been preserved mostly for compatibility with
  CFQ. So far, no performance improvement has been reported after
  changing the latter parameters in BFQ.
  
  In particular, the tunables back_seek-max, back_seek_penalty,
  fifo_expire_async and fifo_expire_sync below are the same as in
  CFQ. Their description is just copied from that for CFQ. Some
  considerations in the description of slice_idle are copied from CFQ
  too.

  
  per-process ioprio and weight
  -----------------------------

  Unless the cgroups interface is used (see "4. BFQ group scheduling"),
  weights can be assigned to processes only indirectly, through I/O
  priorities, and according to the relation:
  weight = (IOPRIO_BE_NR - ioprio) * 10.
  
  Beware that, if low-latency is set, then BFQ automatically raises the
  weight of the queues associated with interactive and soft real-time
  applications. Unset this tunable if you need/want to control weights.

  
  slice_idle
  ----------
  
  This parameter specifies how long BFQ should idle for next I/O
  request, when certain sync BFQ queues become empty. By default
  slice_idle is a non-zero value. Idling has a double purpose: boosting
  throughput and making sure that the desired throughput distribution is
  respected (see the description of how BFQ works, and, if needed, the
  papers referred there).
  
  As for throughput, idling 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 and one
  should see an overall improved throughput on faster storage devices

  like multiple SATA/SAS disks in hardware RAID configuration, as well
  as flash-based storage with internal command queueing (and
  parallelism).

  
  So depending on storage and workload, it might be useful to set
  slice_idle=0.  In general 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), or with
  flash-based fast storage, setting slice_idle=0 might end up in better
  throughput and acceptable latencies.

  
  Idling is however necessary to have service guarantees enforced in
  case of differentiated weights or differentiated I/O-request lengths.
  To see why, suppose that a given BFQ queue A must get several I/O
  requests served for each request served for another queue B. Idling
  ensures that, if A makes a new I/O request slightly after becoming
  empty, then no request of B is dispatched in the middle, and thus A
  does not lose the possibility to get more than one request dispatched
  before the next request of B is dispatched. Note that idling
  guarantees the desired differentiated treatment of queues only in
  terms of I/O-request dispatches. To guarantee that the actual service
  order then corresponds to the dispatch order, the strict_guarantees
  tunable must be set too.
  
  There is an important flipside for idling: apart from the above cases
  where it is beneficial also for throughput, idling can severely impact
  throughput. One important case is random workload. Because of this
  issue, BFQ tends to avoid idling as much as possible, when it is not

  beneficial also for throughput (as detailed in Section 2). As a
  consequence of this behavior, and of further issues described for the
  strict_guarantees tunable, short-term service guarantees may be
  occasionally violated. And, in some cases, these guarantees may be
  more important than guaranteeing maximum throughput. For example, in
  video playing/streaming, a very low drop rate may be more important
  than maximum throughput. In these cases, consider setting the
  strict_guarantees parameter.

  
  strict_guarantees
  -----------------
  
  If this parameter is set (default: unset), then BFQ
  
  - always performs idling when the in-service queue becomes empty;
  
  - forces the device to serve one I/O request at a time, by dispatching a
    new request only if there is no outstanding request.
  
  In the presence of differentiated weights or I/O-request sizes, both
  the above conditions are needed to guarantee that every BFQ queue
  receives its allotted share of the bandwidth. The first condition is
  needed for the reasons explained in the description of the slice_idle
  tunable.  The second condition is needed because all modern storage
  devices reorder internally-queued requests, which may trivially break
  the service guarantees enforced by the I/O scheduler.
  
  Setting strict_guarantees may evidently affect throughput.
  
  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.
  
  low_latency
  -----------
  
  This parameter is used to enable/disable BFQ's low latency mode. By
  default, low latency mode is enabled. If enabled, interactive and soft
  real-time applications are privileged and experience a lower latency,
  as explained in more detail in the description of how BFQ works.

  DISABLE this mode if you need full control on bandwidth

  distribution. In fact, if it is enabled, then BFQ automatically
  increases the bandwidth share of privileged applications, as the main
  means to guarantee a lower latency to them.

  In addition, as already highlighted at the beginning of this document,
  DISABLE this mode if your only goal is to achieve a high throughput.
  In fact, privileging the I/O of some application over the rest may
  entail a lower throughput. To achieve the highest-possible throughput
  on a non-rotational device, setting slice_idle to 0 may be needed too
  (at the cost of giving up any strong guarantee on fairness and low
  latency).

  timeout_sync
  ------------
  
  Maximum amount of device time that can be given to a task (queue) once
  it has been selected for service. On devices with costly seeks,
  increasing this time usually increases maximum throughput. On the
  opposite end, increasing this time coarsens the granularity of the
  short-term bandwidth and latency guarantees, especially if the
  following parameter is set to zero.
  
  max_budget
  ----------
  
  Maximum amount of service, measured in sectors, that can be provided
  to a BFQ queue once it is set in service (of course within the limits
  of the above timeout). According to what said in the description of
  the algorithm, larger values increase the throughput in proportion to
  the percentage of sequential I/O requests issued. The price of larger
  values is that they coarsen the granularity of short-term bandwidth
  and latency guarantees.
  
  The default value is 0, which enables auto-tuning: BFQ sets max_budget
  to the maximum number of sectors that can be served during
  timeout_sync, according to the estimated peak rate.

  For specific devices, some users have occasionally reported to have
  reached a higher throughput by setting max_budget explicitly, i.e., by
  setting max_budget to a higher value than 0. In particular, they have
  set max_budget to higher values than those to which BFQ would have set
  it with auto-tuning. An alternative way to achieve this goal is to
  just increase the value of timeout_sync, leaving max_budget equal to 0.

  weights
  -------
  
  Read-only parameter, used to show the weights of the currently active
  BFQ queues.

  4. Group scheduling with BFQ
  ============================

  BFQ supports both cgroups-v1 and cgroups-v2 io controllers, namely
  blkio and io. In particular, BFQ supports weight-based proportional
  share. To activate cgroups support, set BFQ_GROUP_IOSCHED.

  
  4-1 Service guarantees provided
  -------------------------------
  
  With BFQ, proportional share means true proportional share of the
  device bandwidth, according to group weights. For example, a group
  with weight 200 gets twice the bandwidth, and not just twice the time,
  of a group with weight 100.
  
  BFQ supports hierarchies (group trees) of any depth. Bandwidth is
  distributed among groups and processes in the expected way: for each
  group, the children of the group share the whole bandwidth of the
  group in proportion to their weights. In particular, this implies
  that, for each leaf group, every process of the group receives the
  same share of the whole group bandwidth, unless the ioprio of the
  process is modified.
  
  The resource-sharing guarantee for a group may partially or totally
  switch from bandwidth to time, if providing bandwidth guarantees to
  the group lowers the throughput too much. This switch occurs on a
  per-process basis: if a process of a leaf group causes throughput loss
  if served in such a way to receive its share of the bandwidth, then
  BFQ switches back to just time-based proportional share for that
  process.
  
  4-2 Interface
  -------------
  
  To get proportional sharing of bandwidth with BFQ for a given device,
  BFQ must of course be the active scheduler for that device.
  
  Within each group directory, the names of the files associated with
  BFQ-specific cgroup parameters and stats begin with the "bfq."
  prefix. So, with cgroups-v1 or cgroups-v2, the full prefix for
  BFQ-specific files is "blkio.bfq." or "io.bfq." For example, the group
  parameter to set the weight of a group with BFQ is blkio.bfq.weight
  or io.bfq.weight.
  
  Parameters to set
  -----------------
  
  For each group, there is only the following parameter to set.
  
  weight (namely blkio.bfq.weight or io.bfq-weight): the weight of the
  group inside its parent. Available values: 1..10000 (default 100). The
  linear mapping between ioprio and weights, described at the beginning
  of the tunable section, is still valid, but all weights higher than
  IOPRIO_BE_NR*10 are mapped to ioprio 0.

  Recall that, if low-latency is set, then BFQ automatically raises the
  weight of the queues associated with interactive and soft real-time
  applications. Unset this tunable if you need/want to control weights.

  
  [1] P. Valente, A. Avanzini, "Evolution of the BFQ Storage I/O
      Scheduler", Proceedings of the First Workshop on Mobile System
      Technologies (MST-2015), May 2015.
      http://algogroup.unimore.it/people/paolo/disk_sched/mst-2015.pdf
  
  [2] P. Valente and M. Andreolini, "Improving Application
      Responsiveness with the BFQ Disk I/O Scheduler", Proceedings of
      the 5th Annual International Systems and Storage Conference
      (SYSTOR '12), June 2012.
      Slightly extended version:
      http://algogroup.unimore.it/people/paolo/disk_sched/bfq-v1-suite-
  							results.pdf