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authorPaolo Valente <paolo.valente@linaro.org>2017-04-12 18:23:10 +0200
committerJens Axboe <axboe@fb.com>2017-04-19 08:30:26 -0600
commitab0e43e9cea047873599bc8041cd6278781fd4e0 (patch)
treefc7eb653bc41f6c04a7a09fd516f1ee81b12a0d4 /include/media
parent54b604567fbfa1a35a44c2ac4a35c959d277adc2 (diff)
downloadlinux-ab0e43e9cea047873599bc8041cd6278781fd4e0.tar.bz2
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>
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