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mgr/balancer: crush-compat: phase out osd_weights

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Phase out the osd_weight values as we optimize the crush compat
weight set.  Allow a small regression in overall score if it means
we are taking a step to remove the osd_weight value.

Fix the 'osd reweightn' invocation.

Signed-off-by: Sage Weil <sage@redhat.com>
This commit is contained in:
Sage Weil 2017-09-27 17:05:05 -04:00
parent c2bba5820a
commit 861c37fd99
1 changed files with 23 additions and 4 deletions
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27
src/pybind/mgr/balancer/module.py
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@ -659,12 +659,14 @@ class Module(MgrModule):
# go
best_ws = copy.deepcopy(orig_ws)
best_ow = copy.deepcopy(orig_osd_weight)
cur_pe = pe
left = max_iterations
while left > 0:
# adjust
self.log.debug('best_ws %s' % best_ws)
next_ws = copy.deepcopy(best_ws)
next_ow = copy.deepcopy(best_ow)
random.shuffle(roots)
for root in roots:
pools = cur_pe.root_pools[root]
@ -690,15 +692,22 @@ class Module(MgrModule):
break
self.log.debug('osd.%d deviation %f', osd, deviation)
weight = best_ws[osd]
ow = orig_osd_weight[osd]
if actual[osd] > 0:
calc_weight = target[osd] / actual[osd] * weight
calc_weight = target[osd] / actual[osd] * weight * ow
else:
# not enough to go on here... keep orig weight
calc_weight = weight
calc_weight = weight / orig_osd_weight[osd]
new_weight = weight * (1.0 - step) + calc_weight * step
self.log.debug('Reweight osd.%d %f -> %f', osd, weight,
new_weight)
next_ws[osd] = new_weight
if ow < 1.0:
new_ow = min(1.0, max(step + (1.0 - step) * ow,
ow + .002))
self.log.debug('Reweight osd.%d reweight %f -> %f',
osd, ow, new_ow)
next_ow[osd] = new_ow
# normalize weights under this root
root_weight = crush.get_item_weight(pe.root_ids[root])
@ -737,13 +746,23 @@ class Module(MgrModule):
else:
cur_pe = next_pe
best_ws = next_ws
best_ow = next_ow
if cur_pe.score == 0:
break
left -= 1
if cur_pe.score < pe.score:
# allow a small regression if we are phasing out osd weights
fudge = 0
if next_ow != orig_osd_weight:
fudge = .001
if cur_pe.score < pe.score + fudge:
self.log.info('Success, score %f -> %f', pe.score, cur_pe.score)
plan.compat_ws = best_ws
for osd, w in best_ow.iteritems():
if w != orig_osd_weight[osd]:
self.log.debug('osd.%d reweight %f', osd, w)
plan.osd_weights[osd] = w
return True
else:
self.log.info('Failed to find further optimization, score %f',
@ -837,7 +856,7 @@ class Module(MgrModule):
# new_weight
reweightn = {}
for osd, weight in plan.osd_weights.iteritems():
reweightn[int(osd)] = float(weight) / float(0x10000)
reweightn[str(osd)] = str(int(weight * float(0x10000)))
if len(reweightn):
self.log.info('ceph osd reweightn %s', reweightn)
result = CommandResult('foo')