Minki/linux
1
0
Fork 1
mirror of https://github.com/torvalds/linux.git synced 2024-10-30 16:51:45 +00:00
Code Issues Packages Projects Releases Wiki Activity

block, bfq: add description of weight-raising heuristics

Browse Source 
A description of how weight raising works is missing in BFQ
sources. In addition, the code for handling weight raising is
scattered across a few functions. This makes it rather hard to
understand the mechanism and its rationale. This commits adds such a
description at the beginning of the main source file.

Signed-off-by: Paolo Valente <paolo.valente@linaro.org>
Signed-off-by: Jens Axboe <axboe@kernel.dk>
This commit is contained in:
Paolo Valente 2018-05-31 16:45:05 +02:00 committed by Jens Axboe
parent ac857e0d54
commit 4029eef1be
1 changed files with 56 additions and 24 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

80
block/bfq-iosched.c
View File

@ -49,9 +49,39 @@
*
* In particular, to provide these low-latency guarantees, BFQ
* explicitly privileges the I/O of two classes of time-sensitive
* applications: interactive and soft real-time. This feature enables
* BFQ to provide applications in these classes with a very low
* latency. Finally, BFQ also features additional heuristics for
* applications: interactive and soft real-time. In more detail, BFQ
* behaves this way if the low_latency parameter is set (default
* configuration). This feature enables BFQ to provide applications in
* these classes with a very low latency.
*
* To implement this feature, BFQ constantly tries to detect whether
* the I/O requests in a bfq_queue come from an interactive or a soft
* real-time application. For brevity, in these cases, the queue is
* said to be interactive or soft real-time. In both cases, BFQ
* privileges the service of the queue, over that of non-interactive
* and non-soft-real-time queues. This privileging is performed,
* mainly, by raising the weight of the queue. So, for brevity, we
* call just weight-raising periods the time periods during which a
* queue is privileged, because deemed interactive or soft real-time.
*
* The detection of soft real-time queues/applications is described in
* detail in the comments on the function
* bfq_bfqq_softrt_next_start. On the other hand, the detection of an
* interactive queue works as follows: a queue is deemed interactive
* if it is constantly non empty only for a limited time interval,
* after which it does become empty. The queue may be deemed
* interactive again (for a limited time), if it restarts being
* constantly non empty, provided that this happens only after the
* queue has remained empty for a given minimum idle time.
*
* By default, BFQ computes automatically the above maximum time
* interval, i.e., the time interval after which a constantly
* non-empty queue stops being deemed interactive. Since a queue is
* weight-raised while it is deemed interactive, this maximum time
* interval happens to coincide with the (maximum) duration of the
* weight-raising for interactive queues.
*
* Finally, BFQ also features additional heuristics for
* preserving both a low latency and a high throughput on NCQ-capable,
* rotational or flash-based devices, and to get the job done quickly
* for applications consisting in many I/O-bound processes.
@ -61,14 +91,14 @@
* all low-latency heuristics for that device, by setting low_latency
* to 0.
*
* BFQ is described in [1], where also a reference to the initial, more
* theoretical paper on BFQ can be found. The interested reader can find
* in the latter paper full details on the main algorithm, as well as
* formulas of the guarantees and formal proofs of all the properties.
* With respect to the version of BFQ presented in these papers, this
* implementation adds a few more heuristics, such as the one that
* guarantees a low latency to soft real-time applications, and a
* hierarchical extension based on H-WF2Q+.
* BFQ is described in [1], where also a reference to the initial,
* more theoretical paper on BFQ can be found. The interested reader
* can find in the latter paper full details on the main algorithm, as
* well as formulas of the guarantees and formal proofs of all the
* properties. With respect to the version of BFQ presented in these
* papers, this implementation adds a few more heuristics, such as the
* ones that guarantee a low latency to interactive and soft real-time
* applications, and a hierarchical extension based on H-WF2Q+.
*
* B-WF2Q+ is based on WF2Q+, which is described in [2], together with
* H-WF2Q+, while the augmented tree used here to implement B-WF2Q+
@ -218,21 +248,23 @@ static struct kmem_cache *bfq_pool;
#define BFQ_RATE_SHIFT 16
/*
* By default, BFQ computes the duration of the weight raising for
* interactive applications automatically, using the following formula:
* duration = (R / r) * T, where r is the peak rate of the device, and
* R and T are two reference parameters.
* In particular, R is the peak rate of the reference device (see
* below), and T is a reference time: given the systems that are
* likely to be installed on the reference device according to its
* speed class, T is about the maximum time needed, under BFQ and
* When configured for computing the duration of the weight-raising
* for interactive queues automatically (see the comments at the
* beginning of this file), BFQ does it using the following formula:
* duration = (R / r) * T,
* where r is the peak rate of the device, and R
* and T are two reference parameters. In particular,
* R is the peak rate of the reference device (see below), and
* T is a reference time: given the systems that are likely
* to be installed on the reference device according to its speed
* class, T is about the maximum time needed, under BFQ and
* while reading two files in parallel, to load typical large
* applications on these systems (see the comments on
* max_service_from_wr below, for more details on how T is obtained).
* In practice, the slower/faster the device at hand is, the more/less
* it takes to load applications with respect to the reference device.
* Accordingly, the longer/shorter BFQ grants weight raising to
* interactive applications.
* max_service_from_wr below, for more details on how T is
* obtained). In practice, the slower/faster the device at hand is,
* the more/less it takes to load applications with respect to the
* reference device. Accordingly, the longer/shorter BFQ grants
* weight raising to interactive applications.
*
* BFQ uses four different reference pairs (R, T), depending on:
* . whether the device is rotational or non-rotational;