On 10/1/24 19:27, Aaron Conole wrote:
> Ilya Maximets <[email protected]> writes:
> 
>> ovn-kubernetes users observe uneven distribution of traffic among
>> service backends.  It gets noticeably worse when 9+ backends are
>> configured.  For example:
>>
>>     Server        connections
>>     server-6nbg7     634
>>     server-72br8     326  <-----
>>     server-7b8rm     605
>>     server-9tnz7     655
>>     server-c84hw     596
>>     server-mjskl     622
>>     server-ptkcc     336  <-----
>>     server-rl7pk     592
>>     server-xd2cb     634
>>
>> Here we can see that out of 5000 connections total, servers '72br8'
>> and 'ptkcc' received about 2 times less connections than any other
>> server.  With 10 backends, 4 servers will receive 2x less, etc.
>> The situation is common, because kubernetes users frequently scale
>> their applications with something like kubectl scale --replicas=10.
>>
>> Services are implemented as OVN load-balancers, which are implemented
>> as OpenFlow select groups with a dp_hash selection method.  Balancing
>> is implemented by allocating a hash space (number of hashes is a
>> power of 2) and distributing hashes between the group buckets using
>> Webster's method taking into account bucket weights.
>>
>> This works well enough when buckets have different weights, because
>> OVS needs to allocate larger hash space in order to accommodate the
>> weight difference.  However, in case of OVN load-balancers, all the
>> buckets (backends) have the same weight.  This leads to allocation
>> of a smaller hash space just enough to fit all the backends (closest
>> power of 2).  For example, if we have 10 backends (buckets), then
>> 16 hashes will be allocated.  After hashes are distributed, we end
>> up with 6 buckets having 2 hashes each and 4 buckets having 1 hash.
>> So, each of the 6 buckets will receive on average 2x more traffic
>> than each of the remaining 4.
>>
>> The problem can be mitigated by expanding the hash space and this is
>> already done for cases with small number of buckets by limiting the
>> hash space to have at least 16 hashes.  However, it is just not large
>> enough for 9 or 10 buckets to provide a good distribution.  At the
>> same time, blindly increasing the limit is also not a good solution
>> as we do not want too large of a hash space for small number of
>> buckets, simply because each hash is a separate datapath flow and
>> having too many of them may cause performance issues.
>>
>> Approach taken in this change is to ensure that the hash space is
>> at least 4 times larger than the number of buckets, but not larger
>> than the maximum allowed (256).  This provides a better distribution
>> while not unnecessarily exploding number of datapath flows for
>> services with not that many backends.
>>
>> Here is some data to demonstrate why the 4 was chosen as a coefficient:
>>
>>   coeff.  :   1         2         3         4         5        100
>>   -------------------------------------------------------------------
>>   AvgDiff : 43.1 %    27.1 %    18.3 %    15.1 %    13.6 %    10.9 %
>>   MaxDiff : 50.0 %    33.3 %    25.0 %    20.0 %    20.0 %    20.0 %
>>   AvgDev  : 24.9 %    13.4 %     8.5 %     6.9 %     6.1 %     4.8 %
>>   MaxDev  : 35.4 %    20.4 %    14.4 %    11.2 %    11.2 %    11.2 %
>>   --------+----------------------------------------------------------
>>     16    :   1         1         1         1         1         -
>>     32    :  17         9         6         5         4         -
>>     64    :  33        17        11         9         7         -
>>    128    :  65        33        22        17        13         1
>>    256    : 129        65        43        33        26         2
>>   --------+----------------------------------------------------------
>>            current                       proposed
>>
>> Table shows average and maximum load difference (Diff) between backends
>> across groups with 1 to 64 equally weighted backends.  And it shows
>> average and maximum standard deviation (Dev) of load distribution for
>> the same.  For example, with a coefficient 2, the maximum difference
>> between two backends will be 33% and the maximum standard deviation
>> will be 20.4%.  With the current logic (coefficient of 1) we have
>> maximum difference as high as 50%, as shown with the example at the
>> beginning, with the standard deviation of 35.4%.
>>
>> The bottom half of the table shows from how many backends we start to
>> use a particular number of buckets.  For example, with a coeff. 3
>> we will have 16 hashes for 1 to 5 buckets, 32 hashes for 6-10, 64
>> buckets for 11-21 and so on.
>>
>> According to the table, the number 4 is about where we achieve a good
>> enough standard deviation for the load (11.2%) while still not creating
>> too many hashes for cases with low number of backends.  The standard
>> deviation also doesn't go down that much with higher coefficient.
>>
>> The data is aggregated for up to 64 backends because with higher
>> numbers we're getting close to the cap of 256 hashes, so deviation
>> increases.  However, the load difference with so many backends should
>> have lower impact on a cluster in general.  The change improves the
>> cases with 64+ backends, but to get very good numbers we'll need to
>> consider moving the upper limit for the hash space, which is a separate
>> change to think about.
>>
>> Added test checks that standard deviation of the load distribution
>> between buckets is below 12.5% for all cases up to 64 buckets.  It's
>> just a pretty number that I've chosen that should be IMO good enough.
>>
>> Note: when number of buckets is a power of 2, then we have an ideal
>> distribution with zero deviation, because hashes can be evenly mapped
>> to buckets.
>>
>> Note 2: The change doesn't affect distribution for 4 or less backends,
>> since there are still minimum 16 hashes for those groups.
>>
>> Signed-off-by: Ilya Maximets <[email protected]>
>> ---
>>
>> Not sure if this should be considered as a bug fix and be backported.
>> Thoughts on this are welcome.
> 
> I think it should be okay to backport.  It isn't a new feature.
> 
> Acked-by: Aaron Conole <[email protected]>

Thanks, Aaron and Eelco!

After some considerations, I think this should indeed be treated as a bug
fix, because OpenFlow users expect OVS to honor weights of the buckets
and in this case the load of equally weighted buckets is significantly
different up to 2x.

Applied and backported down to 3.1.  3.1 because the issue was originally
reported for 3.1, the fix is simple, and we do provide occasional releases
for the last 4 branches, according to our release policies.  At the same
time the issue doesn't seem critical to backport to the old-LTS 2.17.

Best regards, Ilya Maximets.
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