Re: [PATCH V3 00/11] block-throttle: add .high limit
From: Paolo Valente
Date: Wed Oct 05 2016 - 08:37:42 EST
> Il giorno 04 ott 2016, alle ore 22:27, Tejun Heo <tj@xxxxxxxxxx> ha scritto:
> Hello, Paolo.
> On Tue, Oct 04, 2016 at 09:29:48PM +0200, Paolo Valente wrote:
>>> Hmm... I think we already discussed this but here's a really simple
>>> case. There are three unknown workloads A, B and C and we want to
>>> give A certain best-effort guarantees (let's say around 80% of the
>>> underlying device) whether A is sharing the device with B or C.
>> That's the same example that you proposed me in our previous
>> discussion. For this example I showed you, with many boring numbers,
>> that with BFQ you get the most accurate distribution of the resource.
> Yes, it is about the same example and what I understood was that
> "accurate distribution of the resources" holds as long as the
> randomness is incidental (ie. due to layout on the filesystem and so
> on) with the slice expiration mechanism offsetting the actually random
For completeness, this property holds whatever the workload is,
especially even if it changes.
>> If you have enough stamina, I can repeat them again. To save your
> I'll go back to the thread and re-read them.
Maybe we can make this less boring, see the end of this email.
>> patience, here is a very brief summary. In a concrete use case, the
>> unknown workloads turn into something like this: there will be a first
>> time interval during which A happens to be, say, sequential, B happens
>> to be, say, random and C happens to be, say, quasi-sequential. Then
>> there will be a next time interval during which their characteristics
>> change, and so on. It is easy (but boring, I acknowledge it) to show
>> that, for each of these time intervals BFQ provides the best possible
>> service in terms of fairness, bandwidth distribution, stability and so
>> on. Why? Because of the elastic bandwidth-time scheduling of BFQ
>> that we already discussed, and because BFQ is naturally accurate in
>> redistributing aggregate throughput proportionally, when needed.
> Yeah, that's what I remember and for workload above certain level of
> randomness its time consumption is mapped to bw, right?
>>> I get that bfq can be a good compromise on most desktop workloads and
>>> behave reasonably well for some server workloads with the slice
>>> expiration mechanism but it really isn't an IO resource partitioning
>> Right. My argument is that BFQ enables you to give to each client the
>> bandwidth and low-latency guarantees you want. And this IMO is way
>> better than partitioning a resource and then getting unavoidable
>> unfairness and high latency.
> But that statement only holds while bw is the main thing to guarantee,
> no? The level of isolation that we're looking for here is fairly
> strict adherence to sub/few-milliseconds in terms of high percentile
> scheduling latency while within the configured bw/iops limits, not
> "overall this device is being used pretty well".
Guaranteeing such a short-term latency, while guaranteeing not just bw
limits, but also proportional share distribution of the bw, is the
reason why we have devised BFQ years ago.
Anyway, to avoid going on with trying speculations and arguments, let
me retry with a practical proposal. BFQ is out there, free. Let's
just test, measure and check whether we have already a solution to
the problems you/we are still trying to solve in Linux.
In this respect, for your generic, unpredictable scenario to make
sense, there must exist at least one real system that meets the
requirements of such a scenario. Or, if such a real system does not
yet exist, it must be possible to emulate it. If it is impossible to
achieve this last goal either, then I miss the usefulness
of looking for solutions for such a scenario.
That said, let's define the instance(s) of the scenario that you find
most representative, and let's test BFQ on it/them. Numbers will give
us the answers. For example, what about all or part of the following
. one cyclically doing random I/O for some second and then sequential I/O
for the next seconds
. one doing, say, quasi-sequential I/O in ON/OFF cycles
. one starting an application cyclically
. one playing back or streaming a movie
For each group, we could then measure the time needed to complete each
phase of I/O in each cycle, plus the responsiveness in the group
starting an application, plus the frame drop in the group streaming
the movie. In addition, we can measure the bandwidth/iops enjoyed by
each group, plus, of course, the aggregate throughput of the whole
system. In particular we could compare results with throttling, BFQ,
Then we could write resulting numbers on the stone, and stick to them
until something proves them wrong.
What do you (or others) think about it?
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