[RFC] IO scheduler based IO controller V9

Jerome Marchand jmarchan at redhat.com
Fri Sep 11 06:16:23 PDT 2009


Vivek Goyal wrote:
> On Thu, Sep 10, 2009 at 04:52:27PM -0400, Vivek Goyal wrote:
>> On Thu, Sep 10, 2009 at 05:18:25PM +0200, Jerome Marchand wrote:
>>> Vivek Goyal wrote:
>>>> Hi All,
>>>>
>>>> Here is the V9 of the IO controller patches generated on top of 2.6.31-rc7.
>>>  
>>> Hi Vivek,
>>>
>>> I've run some postgresql benchmarks for io-controller. Tests have been
>>> made with 2.6.31-rc6 kernel, without io-controller patches (when
>>> relevant) and with io-controller v8 and v9 patches.
>>> I set up two instances of the TPC-H database, each running in their
>>> own io-cgroup. I ran two clients to these databases and tested on each
>>> that simple request:
>>> $ select count(*) from LINEITEM;
>>> where LINEITEM is the biggest table of TPC-H (6001215 entries,
>>> 720MB). That request generates a steady stream of IOs.
>>>
>>> Time is measure by psql (\timing switched on). Each test is run twice
>>> or more if there is any significant difference between the first two
>>> runs. Before each run, the cache is flush:
>>> $ echo 3 > /proc/sys/vm/drop_caches
>>>
>>>
>>> Results with 2 groups of same io policy (BE) and same io weight (1000):
>>>
>>> 	w/o io-scheduler	io-scheduler v8		io-scheduler v9
>>> 	first	second		first	second		first	second
>>> 	DB	DB		DB	DB		DB	DB
>>>
>>> CFQ	48.4s	48.4s		48.2s	48.2s		48.1s	48.5s
>>> Noop	138.0s	138.0s		48.3s	48.4s		48.5s	48.8s
>>> AS	46.3s	47.0s		48.5s	48.7s		48.3s	48.5s
>>> Deadl.	137.1s	137.1s		48.2s	48.3s		48.3s	48.5s
>>>
>>> As you can see, there is no significant difference for CFQ
>>> scheduler.
>> Thanks Jerome.  
>>
>>> There is big improvement for noop and deadline schedulers
>>> (why is that happening?).
>> I think because now related IO is in a single queue and it gets to run
>> for 100ms or so (like CFQ). So previously, IO from both the instances
>> will go into a single queue which should lead to more seeks as requests
>> from two groups will kind of get interleaved.
>>
>> With io controller, both groups have separate queues so requests from
>> both the data based instances will not get interleaved (This almost
>> becomes like CFQ where ther are separate queues for each io context
>> and for sequential reader, one io context gets to run nicely for certain
>> ms based on its priority).
>>
>>> The performance with anticipatory scheduler
>>> is a bit lower (~4%).
>>>
> 
> Hi Jerome, 
> 
> Can you also run the AS test with io controller patches and both the
> database in root group (basically don't put them in to separate group). I 
> suspect that this regression might come from that fact that we now have
> to switch between queues and in AS we wait for request to finish from
> previous queue before next queue is scheduled in and probably that is
> slowing down things a bit.., just a wild guess..
> 

Hi Vivek,

I guess that's not the reason. I got 46.6s for both DB in root group with
io-controller v9 patches. I also rerun the test with DB in different groups
and found about the same result as above (48.3s and 48.6s).

Jerome



> Thanks
> Vivek
> 
>> I will run some tests with AS and see if I can reproduce this lower
>> performance and attribute it to a particular piece of code.
>>
>>> Results with 2 groups of same io policy (BE), different io weights and
>>> CFQ scheduler:
>>> 			io-scheduler v8		io-scheduler v9
>>> weights = 1000, 500	35.6s	46.7s		35.6s	46.7s
>>> weigths = 1000, 250	29.2s	45.8s		29.2s	45.6s
>>>
>>> The result in term of fairness is close to what we can expect from the
>>> ideal theoric case: with io weights of 1000 and 500 (1000 and 250),
>>> the first request get 2/3 (4/5) of io time as long as it runs and thus
>>> finish in about 3/4 (5/8) of total time. 
>>>
>> Jerome, after 36.6 seconds, disk will be fully given to second group.
>> Hence these times might not reflect the accurate measure of who got how
>> much of disk time.
>>
>> Can you just capture the output of "io.disk_time" file in both the cgroups
>> at the time of completion of task in higher weight group. Alternatively,
>> you can just run this a script in a loop which prints the output of
>>  "cat io.disk_time | grep major:minor" every  2 seconds. That way we can
>> see how disk times are being distributed between groups.
>>
>>> Results  with 2 groups of different io policies, same io weight and
>>> CFQ scheduler:
>>> 			io-scheduler v8		io-scheduler v9
>>> policy = RT, BE		22.5s	45.3s		22.4s	45.0s
>>> policy = BE, IDLE	22.6s	44.8s		22.4s	45.0s
>>>
>>> Here again, the result in term of fairness is very close from what we
>>> expect.
>> Same as above in this case too.
>>
>> These seem to be good test for fairness measurement in case of streaming 
>> readers. I think one more interesting test case will be do how are the 
>> random read latencies in case of multiple streaming readers present.
>>
>> So if we can launch 4-5 dd processes in one group and then issue some
>> random small queueries on postgresql in second group, I am keen to see
>> how quickly the query can be completed with and without io controller.
>> Would be interesting to see at results for all 4 io schedulers.
>>
>> Thanks
>> Vivek



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