- 19 Apr, 2017 25 commits
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Bart Van Assche authored
Export this function such that it becomes available to block drivers. Signed-off-by:
Bart Van Assche <bart.vanassche@sandisk.com> Reviewed-by:
Christoph Hellwig <hch@lst.de> Cc: Matias Bjørling <m@bjorling.me> Cc: Adam Manzanares <adam.manzanares@wdc.com> Signed-off-by:
Jens Axboe <axboe@fb.com>
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Arnd Bergmann authored
The driver uses both u64 and sector_t to refer to offsets, and assigns between the two. This causes one harmless warning when sector_t is 32-bit: drivers/lightnvm/pblk-rb.c: In function 'pblk_rb_write_entry_gc': include/linux/lightnvm.h:215:20: error: large integer implicitly truncated to unsigned type [-Werror=overflow] drivers/lightnvm/pblk-rb.c:324:22: note: in expansion of macro 'ADDR_EMPTY' As the driver is already doing this inconsistently, changing the type won't make it worse and is an easy way to avoid the warning. Fixes: a4bd217b ("lightnvm: physical block device (pblk) target") Signed-off-by:
Arnd Bergmann <arnd@arndb.de> Signed-off-by:
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Christoph Hellwig authored
blk_insert_flush should be using __blk_end_request to start with. Signed-off-by:
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Christoph Hellwig authored
This function is not used anywhere in the kernel. Signed-off-by:
Johannes Thumshirn <jthumshirn@suse.de> Signed-off-by:
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Christoph Hellwig authored
Both functions are entirely unused. Signed-off-by:
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Christoph Hellwig authored
This was just a proof of concept user for the SCSI OSD library, and never had any real users. Signed-off-by:
Boaz Harrosh <ooo@electrozaur.com> Signed-off-by:
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Jan Kara authored
When CFQ is used as an elevator, it disables writeback throttling because they don't play well together. Later when a different elevator is chosen for the device, writeback throttling doesn't get enabled again as it should. Make sure CFQ enables writeback throttling (if it should be enabled by default) when we switch from it to another IO scheduler. Signed-off-by:
Jan Kara <jack@suse.cz> Signed-off-by:
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Paolo Valente authored
The BFQ I/O scheduler features an optimal fair-queuing (proportional-share) scheduling algorithm, enriched with several mechanisms to boost throughput and reduce latency for interactive and real-time applications. This makes BFQ a large and complex piece of code. This commit addresses this issue by splitting BFQ into three main, independent components, and by moving each component into a separate source file: 1. Main algorithm: handles the interaction with the kernel, and decides which requests to dispatch; it uses the following two further components to achieve its goals. 2. Scheduling engine (Hierarchical B-WF2Q+ scheduling algorithm): computes the schedule, using weights and budgets provided by the above component. 3. cgroups support: handles group operations (creation, destruction, move, ...). Signed-off-by:
Paolo Valente <paolo.valente@linaro.org> Signed-off-by:
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Paolo Valente authored
When a bfq queue is set in service and when it is merged, a reference to the I/O context associated with the queue is taken. This reference is then released when the queue is deselected from service or split. More precisely, the release of the reference is postponed to when the scheduler lock is released, to avoid nesting between the scheduler and the I/O-context lock. In fact, such nesting would lead to deadlocks, because of other code paths that take the same locks in the opposite order. This postponing of I/O-context releases does complicate code. This commit addresses these issue by modifying involved operations in such a way to not need to get the above I/O-context references any more. Then it also removes any get and release of these references. Signed-off-by:
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Arianna Avanzini authored
Many popular I/O-intensive services or applications spawn or reactivate many parallel threads/processes during short time intervals. Examples are systemd during boot or git grep. These services or applications benefit mostly from a high throughput: the quicker the I/O generated by their processes is cumulatively served, the sooner the target job of these services or applications gets completed. As a consequence, it is almost always counterproductive to weight-raise any of the queues associated to the processes of these services or applications: in most cases it would just lower the throughput, mainly because weight-raising also implies device idling. To address this issue, an I/O scheduler needs, first, to detect which queues are associated with these services or applications. In this respect, we have that, from the I/O-scheduler standpoint, these services or applications cause bursts of activations, i.e., activations of different queues occurring shortly after each other. However, a shorter burst of activations may be caused also by the start of an application that does not consist in a lot of parallel I/O-bound threads (see the comments on the function bfq_handle_burst for details). In view of these facts, this commit introduces: 1) an heuristic to detect (only) bursts of queue activations caused by services or applications consisting in many parallel I/O-bound threads; 2) the prevention of device idling and weight-raising for the queues belonging to these bursts. Signed-off-by:
Arianna Avanzini <avanzini.arianna@gmail.com> Signed-off-by:
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Paolo Valente authored
This patch is basically the counterpart, for NCQ-capable rotational devices, of the previous patch. Exactly as the previous patch does on flash-based devices and for any workload, this patch disables device idling on rotational devices, but only for random I/O. In fact, only with these queues disabling idling boosts the throughput on NCQ-capable rotational devices. To not break service guarantees, idling is disabled for NCQ-enabled rotational devices only when the same symmetry conditions considered in the previous patches hold. Signed-off-by:
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Paolo Valente authored
This patch boosts the throughput on NCQ-capable flash-based devices, while still preserving latency guarantees for interactive and soft real-time applications. The throughput is boosted by just not idling the device when the in-service queue remains empty, even if the queue is sync and has a non-null idle window. This helps to keep the drive's internal queue full, which is necessary to achieve maximum performance. This solution to boost the throughput is a port of commits a68bbdd and f7d7b7a7 for CFQ. As already highlighted in a previous patch, allowing the device to prefetch and internally reorder requests trivially causes loss of control on the request service order, and hence on service guarantees. Fortunately, as discussed in detail in the comments on the function bfq_bfqq_may_idle(), if every process has to receive the same fraction of the throughput, then the service order enforced by the internal scheduler of a flash-based device is relatively close to that enforced by BFQ. In particular, it is close enough to let service guarantees be substantially preserved. Things change in an asymmetric scenario, i.e., if not every process has to receive the same fraction of the throughput. In this case, to guarantee the desired throughput distribution, the device must be prevented from prefetching requests. This is exactly what this patch does in asymmetric scenarios. Signed-off-by:
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Arianna Avanzini authored
A seeky queue (i..e, a queue containing random requests) is assigned a very small device-idling slice, for throughput issues. Unfortunately, given the process associated with a seeky queue, this behavior causes the following problem: if the process, say P, performs sync I/O and has a higher weight than some other processes doing I/O and associated with non-seeky queues, then BFQ may fail to guarantee to P its reserved share of the throughput. The reason is that idling is key for providing service guarantees to processes doing sync I/O [1]. This commit addresses this issue by allowing the device-idling slice to be reduced for a seeky queue only if the scenario happens to be symmetric, i.e., if all the queues are to receive the same share of the throughput. [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.pdfSigned-off-by:Riccardo Pizzetti <riccardo.pizzetti@gmail.com> Signed-off-by:
Samuele Zecchini <samuele.zecchini92@gmail.com> Signed-off-by:
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Arianna Avanzini authored
A set of processes may happen to perform interleaved reads, i.e., read requests whose union would give rise to a sequential read pattern. There are two typical cases: first, processes reading fixed-size chunks of data at a fixed distance from each other; second, processes reading variable-size chunks at variable distances. The latter case occurs for example with QEMU, which splits the I/O generated by a guest into multiple chunks, and lets these chunks be served by a pool of I/O threads, iteratively assigning the next chunk of I/O to the first available thread. CFQ denotes as 'cooperating' a set of processes that are doing interleaved I/O, and when it detects cooperating processes, it merges their queues to obtain a sequential I/O pattern from the union of their I/O requests, and hence boost the throughput. Unfortunately, in the following frequent case, the mechanism implemented in CFQ for detecting cooperating processes and merging their queues is not responsive enough to handle also the fluctuating I/O pattern of the second type of processes. Suppose that one process of the second type issues a request close to the next request to serve of another process of the same type. At that time the two processes would be considered as cooperating. But, if the request issued by the first process is to be merged with some other already-queued request, then, from the moment at which this request arrives, to the moment when CFQ controls whether the two processes are cooperating, the two processes are likely to be already doing I/O in distant zones of the disk surface or device memory. CFQ uses however preemption to get a sequential read pattern out of the read requests performed by the second type of processes too. As a consequence, CFQ 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 to immediately check whether a newly-arrived request lets some pair of processes become cooperating, both in the case of actual request insertion and, to be responsive with the second type of processes, in the case of request merge. Both types of processes are then handled by just merging their queues. Signed-off-by:
Mauro Andreolini <mauro.andreolini@unimore.it> Signed-off-by:
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Paolo Valente authored
This patch introduces an heuristic that reduces latency when the I/O-request pool is saturated. This goal is achieved by disabling device idling, for non-weight-raised queues, when there are weight- raised queues with pending or in-flight requests. In fact, as explained in more detail in the comment on the function bfq_bfqq_may_idle(), this reduces the rate at which processes associated with non-weight-raised queues grab requests from the pool, thereby increasing the probability that processes associated with weight-raised queues get a request immediately (or at least soon) when they need one. Along the same line, if there are weight-raised queues, then this patch halves the service rate of async (write) requests for non-weight-raised queues. Signed-off-by:
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Paolo Valente authored
I/O schedulers typically allow NCQ-capable drives to prefetch I/O requests, as NCQ boosts the throughput exactly by prefetching and internally reordering requests. Unfortunately, as discussed in detail and shown experimentally in [1], this may cause fairness and latency guarantees to be violated. The main problem is that the internal scheduler of an NCQ-capable drive may postpone the service of some unlucky (prefetched) requests as long as it deems serving other requests more appropriate to boost the throughput. This patch addresses this issue by not disabling device idling for weight-raised queues, even if the device supports NCQ. This allows BFQ to start serving a new queue, and therefore allows the drive to prefetch new requests, only after the idling timeout expires. At that time, all the outstanding requests of the expired queue have been most certainly served. [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 authored
To guarantee a low latency also to the I/O requests issued by soft real-time applications, this patch introduces a further heuristic, which weight-raises (in the sense explained in the previous patch) also the queues associated to applications deemed as soft real-time. To be deemed as soft real-time, an application must meet two requirements. First, the application must not require an average bandwidth higher than the approximate bandwidth required to playback or record a compressed high-definition video. Second, the request pattern of the application must be isochronous, i.e., after issuing a request or a batch of requests, the application must stop issuing new requests until all its pending requests have been completed. After that, the application may issue a new batch, and so on. As for the second requirement, it is critical to require also that, after all the pending requests of the application have been completed, an adequate minimum amount of time elapses before the application starts issuing new requests. This prevents also greedy (i.e., I/O-bound) applications from being incorrectly deemed, occasionally, as soft real-time. In fact, if *any amount of time* is fine, then even a greedy application may, paradoxically, meet both the above requirements, if: (1) the application performs random I/O and/or the device is slow, and (2) the CPU load is high. The reason is the following. First, if condition (1) is true, then, during the service of the application, the throughput may be low enough to let the application meet the bandwidth requirement. Second, if condition (2) is true as well, then the application may occasionally behave in an apparently isochronous way, because it may simply stop issuing requests while the CPUs are busy serving other processes. To address this issue, the heuristic leverages the simple fact that greedy applications issue *all* their requests as quickly as they can, whereas soft real-time applications spend some time processing data after each batch of requests is completed. In particular, the heuristic works as follows. First, according to the above isochrony requirement, the heuristic checks whether an application may be soft real-time, thereby giving to the application the opportunity to be deemed as such, only when both the following two conditions happen to hold: 1) the queue associated with the application has expired and is empty, 2) there is no outstanding request of the application. Suppose that both conditions hold at time, say, t_c and that the application issues its next request at time, say, t_i. At time t_c the heuristic computes the next time instant, called soft_rt_next_start in the code, such that, only if t_i >= soft_rt_next_start, then both the next conditions will hold when the application issues its next request: 1) the application will meet the above bandwidth requirement, 2) a given minimum time interval, say Delta, will have elapsed from time t_c (so as to filter out greedy application). The current value of Delta is a little bit higher than the value that we have found, experimentally, to be adequate on a real, general-purpose machine. In particular we had to increase Delta to make the filter quite precise also in slower, embedded systems, and in KVM/QEMU virtual machines (details in the comments on the code). If the application actually issues its next request after time soft_rt_next_start, then its associated queue will be weight-raised for a relatively short time interval. If, during this time interval, the application proves again to meet the bandwidth and isochrony requirements, then the end of the weight-raising period for the queue is moved forward, and so on. Note that an application whose associated queue never happens to be empty when it expires will never have the opportunity to be deemed as soft real-time. Signed-off-by:
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Paolo Valente authored
This patch introduces a simple heuristic to load applications quickly, and to perform the I/O requested by interactive applications just as quickly. To this purpose, both a newly-created queue and a queue associated with an interactive application (we explain in a moment how BFQ decides whether the associated application is interactive), receive the following two special treatments: 1) The weight of the queue is raised. 2) The queue unconditionally enjoys device idling when it empties; in fact, if the requests of a queue are sync, then performing device idling for the queue is a necessary condition to guarantee that the queue receives a fraction of the throughput proportional to its weight (see [1] for details). For brevity, we call just weight-raising the combination of these two preferential treatments. For a newly-created queue, weight-raising starts immediately and lasts for a time interval that: 1) depends on the device speed and type (rotational or non-rotational), and 2) is equal to the time needed to load (start up) a large-size application on that device, with cold caches and with no additional workload. Finally, as for guaranteeing a fast execution to interactive, I/O-related tasks (such as opening a file), consider that any interactive application blocks and waits for user input both after starting up and after executing some task. After a while, the user may trigger new operations, after which the application stops again, and so on. Accordingly, the low-latency heuristic weight-raises again a queue in case it becomes backlogged after being idle for a sufficiently long (configurable) time. The weight-raising then lasts for the same time as for a just-created queue. According to our experiments, the combination of this low-latency heuristic and of the improvements described in the previous patch allows BFQ to guarantee a high application responsiveness. [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.pdfSigned-off-by: -
Paolo Valente authored
This patch deals with two sources of unfairness, which can also cause high latencies and throughput loss. The first source is related to write requests. Write requests tend to starve read requests, basically because, on one side, writes are slower than reads, whereas, on the other side, storage devices confuse schedulers by deceptively signaling the completion of write requests immediately after receiving them. This patch addresses this issue by just throttling writes. In particular, after a write request is dispatched for a queue, the budget of the queue is decremented by the number of sectors to write, multiplied by an (over)charge coefficient. The value of the coefficient is the result of our tuning with different devices. The second source of unfairness has to do with slowness detection: when the in-service queue is expired, BFQ also controls whether the queue has been "too slow", i.e., has consumed its last-assigned budget at such a low rate that it would have been impossible to consume all of this budget within the maximum time slice T_max (Subsec. 3.5 in [1]). In this case, the queue is always (over)charged the whole budget, to reduce its utilization of the device. Both this overcharge and the slowness-detection criterion may cause unfairness. First, always charging a full budget to a slow queue is too coarse. It is much more accurate, and this patch lets BFQ do so, to charge an amount of service 'equivalent' to the amount of time during which the queue has been in service. As explained in more detail in the comments on the code, this enables BFQ to provide time fairness among slow queues. Secondly, because of ZBR, a queue may be deemed as slow when its associated process is performing I/O on the slowest zones of a disk. However, unless the process is truly too slow, not reducing the disk utilization of the queue is more profitable in terms of disk throughput than the opposite. A similar problem is caused by logical block mapping on non-rotational devices. For this reason, this patch lets a queue be charged time, and not budget, only if the queue has consumed less than 2/3 of its assigned budget. As an additional, important benefit, this tolerance allows BFQ to preserve enough elasticity to still perform bandwidth, and not time, distribution with little unlucky or quasi-sequential processes. Finally, for the same reasons as above, this patch makes slowness detection itself much less harsh: a queue is deemed slow only if it has consumed its budget at less than half of the peak rate. [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 authored
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:
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Paolo Valente authored
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: -
Arianna Avanzini authored
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:
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Paolo Valente authored
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: -
Josef Bacik authored
NBD doesn't care about limiting the segment size, let the user push the largest bio's they want. This allows us to control the request size solely through max_sectors_kb. Signed-off-by:
Josef Bacik <jbacik@fb.com> Reviewed-by:
Ming Lei <ming.lei@redhat.com> Signed-off-by:
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Jens Axboe authored
Merge branch 'stable/for-jens-4.12' of git://git.kernel.org/pub/scm/linux/kernel/git/konrad/xen into for-4.12/block Konrad writes: It has one fix - to emit an uevent whenever the size of the guest disk image changes.
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- 18 Apr, 2017 1 commit
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Marc Olson authored
When a blkfront device is resized from dom0, emit a KOBJ_CHANGE uevent to notify the guest about the change. This allows for custom udev rules, such as automatically resizing a filesystem, when an event occurs. With this patch you get these udev KERNEL[577.206230] change /devices/vbd-51728/block/xvdb (block) UDEV [577.226218] change /devices/vbd-51728/block/xvdb (block) Signed-off-by:
Marc Olson <marcolso@amazon.com> Signed-off-by:
Konrad Rzeszutek Wilk <konrad.wilk@oracle.com>
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- 17 Apr, 2017 12 commits
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Josef Bacik authored
For ease of management it would be nice for users to specify that the device node for a nbd device is destroyed once it is disconnected and there are no more users. Add a client flag and enable this operation to happen. Signed-off-by:
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Josef Bacik authored
In order to support deleting the device on disconnect we need to refcount the actual nbd_device struct. So add the refcounting framework and change how we free the normal devices at rmmod time so we can catch reference leaks. Signed-off-by:
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Josef Bacik authored
Allow users to query the status of existing nbd devices. Right now this only returns whether or not the device is connected, but could be extended in the future to include more information. Signed-off-by:
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Josef Bacik authored
Sometimes we like to upgrade our server without making all of our clients freak out and reconnect. This patch provides a way to specify a dead connection timeout to allow us to pause all requests and wait for new connections to be opened. With this in place I can take down the nbd server for less than the dead connection timeout time and bring it back up and everything resumes gracefully. Signed-off-by:
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Josef Bacik authored
When running a disconnect torture test I noticed that sometimes we would crash with a negative ref count on our queue. This was because we were ending the same request twice. Turns out we were racing with NBD_CLEAR_SOCK clearing the requests as well as the teardown of the device clearing the requests. So instead make the ioctl only shutdown the sockets and make it so that we only ever run nbd_clear_que from the device teardown. Signed-off-by:
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Josef Bacik authored
Provide a mechanism to notify userspace that there's been a link problem on a NBD device. This will allow userspace to re-establish a connection and provide the new socket to the device without disrupting the device. Signed-off-by:
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Josef Bacik authored
We want to be able to reconnect dead connections to existing block devices, so add a reconfigure netlink command. We will also allow users to change their timeout on the fly, but everything else will require a disconnect and reconnect. You won't be able to add more connections either, simply replace dead connections with new more lively connections. Signed-off-by:
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Josef Bacik authored
The existing ioctl interface for configuring NBD devices is a bit cumbersome and hard to extend. The other problem is we leave a userspace app sitting in it's syscall until the device disconnects, which is less than ideal. This patch introduces a netlink interface for adding and disconnecting nbd devices. This has the benefits of being easily extendable without breaking older userspace applications, and allows us to configure a nbd device without leaving a userspace app sitting waiting for the device to disconnect. With this interface we also gain the ability to configure more devices than are preallocated at insmod time. We also have gained the ability to not specify a particular device and be provided one for us so that userspace doesn't need to find a free device to configure. Signed-off-by:
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Josef Bacik authored
In preparation for the upcoming netlink interface we need to not rely on already having the bdev for the NBD device we are doing operations on. Instead of passing the bdev around, just use it in places where we know we already have the bdev. Signed-off-by:
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Josef Bacik authored
In order to properly refcount the various aspects of a NBD device we need to separate out the configuration elements of the nbd device. The configuration of a NBD device has a different lifetime from the actual device, so it doesn't make sense to bundle these two concepts. Add a config_refs to keep track of the configuration structure, that way we can be sure that we never access it when we've torn down the device. Add a new nbd_config structure to hold all of the transient configuration information. Finally create this when we open the device so that it is in place when we start to configure the device. This has a nice side-effect of fixing a long standing problem where you could end up with a half-configured nbd device that needed to be "disconnected" in order to be usable again. Now once we close our device the configuration will be discarded. Signed-off-by:
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Josef Bacik authored
Currently if we have multiple connections and one of them goes down we will tear down the whole device. However there's no reason we need to do this as we could have other connections that are working fine. Deal with this by keeping track of the state of the different connections, and if we lose one we mark it as dead and send all IO destined for that socket to one of the other healthy sockets. Any outstanding requests that were on the dead socket will timeout and be re-submitted properly. Signed-off-by:
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Josef Bacik authored
When adding a new socket we look it up and then try to add it to our configuration. If any of those steps fail we need to make sure we put the socket so we don't leak them. Signed-off-by:
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- 16 Apr, 2017 2 commits
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Dan Carpenter authored
There were a bunch of places in pblk_lines_init() where we didn't set an error code. And in pblk_writer_init() we accidentally return 1 instead of a correct error code, which would result in a Oops later. Fixes: 11a5d6fdf919 ("lightnvm: physical block device (pblk) target") Signed-off-by:Dan Carpenter <dan.carpenter@oracle.com> Signed-off-by:
Matias Bjørling <matias@cnexlabs.com> Signed-off-by:
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Dan Carpenter authored
WARN_ON() takes a condition, not an error message. I slightly tweaked some conditions so hopefully it's more clear. Signed-off-by:
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