104 Commits

Author	SHA1	Message	Date
Paolo Valente	ab0e43e9ce	block, bfq: modify the peak-rate estimator ... Unless the maximum budget B_max that BFQ can assign to a queue is set explicitly by the user, BFQ automatically updates B_max. In particular, BFQ dynamically sets B_max to the number of sectors that can be read, at the current estimated peak rate, during the maximum time, T_max, allowed before a budget timeout occurs. In formulas, if we denote as R_est the estimated peak rate, then B_max = T_max ∗ R_est. Hence, the higher R_est is with respect to the actual device peak rate, the higher the probability that processes incur budget timeouts unjustly is. Besides, a too high value of B_max unnecessarily increases the deviation from an ideal, smooth service. Unfortunately, it is not trivial to estimate the peak rate correctly: because of the presence of sw and hw queues between the scheduler and the device components that finally serve I/O requests, it is hard to say exactly when a given dispatched request is served inside the device, and for how long. As a consequence, it is hard to know precisely at what rate a given set of requests is actually served by the device. On the opposite end, the dispatch time of any request is trivially available, and, from this piece of information, the "dispatch rate" of requests can be immediately computed. So, the idea in the next function is to use what is known, namely request dispatch times (plus, when useful, request completion times), to estimate what is unknown, namely in-device request service rate. The main issue is that, because of the above facts, the rate at which a certain set of requests is dispatched over a certain time interval can vary greatly with respect to the rate at which the same requests are then served. But, since the size of any intermediate queue is limited, and the service scheme is lossless (no request is silently dropped), the following obvious convergence property holds: the number of requests dispatched MUST become closer and closer to the number of requests completed as the observation interval grows. This is the key property used in this new version of the peak-rate estimator. Signed-off-by: Paolo Valente <paolo.valente@linaro.org> Signed-off-by: Arianna Avanzini <avanzini.arianna@gmail.com> Signed-off-by: Jens Axboe <axboe@fb.com>	2017-04-19 08:30:26 -06:00
Paolo Valente	54b604567f	block, bfq: improve throughput boosting ... The feedback-loop algorithm used by BFQ to compute queue (process) budgets is basically a set of three update rules, one for each of the main reasons why a queue may be expired. If many processes suddenly switch from sporadic I/O to greedy and sequential I/O, then these rules are quite slow to assign large budgets to these processes, and hence to achieve a high throughput. On the opposite side, BFQ assigns the maximum possible budget B_max to a just-created queue. This allows a high throughput to be achieved immediately if the associated process is I/O-bound and performs sequential I/O from the beginning. But it also increases the worst-case latency experienced by the first requests issued by the process, because the larger the budget of a queue waiting for service is, the later the queue will be served by B-WF2Q+ (Subsec 3.3 in [1]). This is detrimental for an interactive or soft real-time application. To tackle these throughput and latency problems, on one hand this patch changes the initial budget value to B_max/2. On the other hand, it re-tunes the three rules, adopting a more aggressive, multiplicative increase/linear decrease scheme. This scheme trades latency for throughput more than before, and tends to assign large budgets quickly to processes that are or become I/O-bound. For two of the expiration reasons, the new version of the rules also contains some more little improvements, briefly described below. *No more backlog.* In this case, the budget was larger than the number of sectors actually read/written by the process before it stopped doing I/O. Hence, to reduce latency for the possible future I/O requests of the process, the old rule simply set the next budget to the number of sectors actually consumed by the process. However, if there are still outstanding requests, then the process may have not yet issued its next request just because it is still waiting for the completion of some of the still outstanding ones. If this sub-case holds true, then the new rule, instead of decreasing the budget, doubles it, proactively, in the hope that: 1) a larger budget will fit the actual needs of the process, and 2) the process is sequential and hence a higher throughput will be achieved by serving the process longer after granting it access to the device. *Budget timeout*. The original rule set the new budget to the maximum value B_max, to maximize throughput and let all processes experiencing budget timeouts receive the same share of the device time. In our experiments we verified that this sudden jump to B_max did not provide sensible benefits; rather it increased the latency of processes performing sporadic and short I/O. The new rule only doubles the budget. [1] 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 Signed-off-by: Paolo Valente <paolo.valente@linaro.org> Signed-off-by: Arianna Avanzini <avanzini.arianna@gmail.com> Signed-off-by: Jens Axboe <axboe@fb.com>	2017-04-19 08:30:26 -06:00
Arianna Avanzini	e21b7a0b98	block, bfq: add full hierarchical scheduling and cgroups support ... Add complete support for full hierarchical scheduling, with a cgroups interface. Full hierarchical scheduling is implemented through the 'entity' abstraction: both bfq_queues, i.e., the internal BFQ queues associated with processes, and groups are represented in general by entities. Given the bfq_queues associated with the processes belonging to a given group, the entities representing these queues are sons of the entity representing the group. At higher levels, if a group, say G, contains other groups, then the entity representing G is the parent entity of the entities representing the groups in G. Hierarchical scheduling is performed as follows: if the timestamps of a leaf entity (i.e., of a bfq_queue) change, and such a change lets the entity become the next-to-serve entity for its parent entity, then the timestamps of the parent entity are recomputed as a function of the budget of its new next-to-serve leaf entity. If the parent entity belongs, in its turn, to a group, and its new timestamps let it become the next-to-serve for its parent entity, then the timestamps of the latter parent entity are recomputed as well, and so on. When a new bfq_queue must be set in service, the reverse path is followed: the next-to-serve highest-level entity is chosen, then its next-to-serve child entity, and so on, until the next-to-serve leaf entity is reached, and the bfq_queue that this entity represents is set in service. Writeback is accounted for on a per-group basis, i.e., for each group, the async I/O requests of the processes of the group are enqueued in a distinct bfq_queue, and the entity associated with this queue is a child of the entity associated with the group. Weights can be assigned explicitly to groups and processes through the cgroups interface, differently from what happens, for single processes, if the cgroups interface is not used (as explained in the description of the previous patch). In particular, since each node has a full scheduler, each group can be assigned its own weight. Signed-off-by: Fabio Checconi <fchecconi@gmail.com> Signed-off-by: Paolo Valente <paolo.valente@linaro.org> Signed-off-by: Arianna Avanzini <avanzini.arianna@gmail.com> Signed-off-by: Jens Axboe <axboe@fb.com>	2017-04-19 08:30:26 -06:00
Paolo Valente	aee69d78de	block, bfq: introduce the BFQ-v0 I/O scheduler as an extra scheduler ... We tag as v0 the version of BFQ containing only BFQ's engine plus hierarchical support. BFQ's engine is introduced by this commit, while hierarchical support is added by next commit. We use the v0 tag to distinguish this minimal version of BFQ from the versions containing also the features and the improvements added by next commits. BFQ-v0 coincides with the version of BFQ submitted a few years ago [1], apart from the introduction of preemption, described below. 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, 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 [2] for 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, after the following commit) 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. - 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. However, for a cleaner logical breakdown, the code that enables and completes hierarchical support is provided in the next commit, which focuses exactly on this feature. - 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]). - Weights can be assigned to processes only indirectly, through I/O priorities, and according to the relation: weight = 10 * (IOPRIO_BE_NR - ioprio). The next patch provides, instead, a cgroups interface through which weights can be assigned explicitly. - 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. - If the strict_guarantees 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 queue receives its allotted share of the bandwidth (see Documentation/block/bfq-iosched.txt for more details). Setting strict_guarantees may evidently affect throughput. [1] https://lkml.org/lkml/2008/4/1/234 https://lkml.org/lkml/2008/11/11/148 [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 Signed-off-by: Fabio Checconi <fchecconi@gmail.com> Signed-off-by: Paolo Valente <paolo.valente@linaro.org> Signed-off-by: Arianna Avanzini <avanzini.arianna@gmail.com> Signed-off-by: Jens Axboe <axboe@fb.com>	2017-04-19 08:29:02 -06:00