324 строки
14 KiB
Python
324 строки
14 KiB
Python
# Copyright (c) Microsoft Corporation
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#
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# All rights reserved.
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#
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# MIT License
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#
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# Permission is hereby granted, free of charge, to any person obtaining a
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# copy of this software and associated documentation files (the "Software"),
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# to deal in the Software without restriction, including without limitation
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# the rights to use, copy, modify, merge, publish, distribute, sublicense,
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# and/or sell copies of the Software, and to permit persons to whom the
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# Software is furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in
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# all copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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# DEALINGS IN THE SOFTWARE.
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# compat imports
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from __future__ import (
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absolute_import, division, print_function, unicode_literals
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)
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from builtins import ( # noqa
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bytes, dict, int, list, object, range, str, ascii, chr, hex, input,
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next, oct, open, pow, round, super, filter, map, zip)
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# stdlib imports
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import collections
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# non-stdlib imports
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# local imports
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from . import util
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# global defines
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_UNBOUND_MAX_NODES = 16777216
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AutoscaleMinMax = collections.namedtuple(
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'AutoscaleMinMax', [
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'max_tasks_per_node',
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'min_target_dedicated',
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'min_target_low_priority',
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'max_target_dedicated',
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'max_target_low_priority',
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]
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)
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def _formula_tasks(pool):
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# type: (settings.PoolSettings) -> str
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"""Generate an autoscale formula for tasks scenario
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:param settings.PoolSettings pool: pool settings
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:rtype: str
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:return: autoscale formula
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"""
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minmax = _get_minmax(pool)
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if pool.autoscale.scenario.name == 'active_tasks':
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task_type = 'Active'
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elif pool.autoscale.scenario.name == 'pending_tasks':
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task_type = 'Pending'
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else:
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raise ValueError('autoscale scenario name invalid: {}'.format(
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pool.autoscale.scenario.name))
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if pool.autoscale.scenario.bias_last_sample:
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req_vms = [
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'sli = TimeInterval_Second * {}'.format(
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pool.autoscale.scenario.sample_lookback_interval.
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total_seconds()),
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'samplepercent = ${}Tasks.GetSamplePercent(sli)'.format(task_type),
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'lastsample = val(${}Tasks.GetSample(1), 0)'.format(task_type),
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('samplevecavg = samplepercent < {} ? max(0, lastsample) : '
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'avg(${}Tasks.GetSample(sli))').format(
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pool.autoscale.scenario.required_sample_percentage,
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task_type),
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('{}TaskAvg = samplepercent < {} ? max(0, lastsample) : '
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'(lastsample < samplevecavg ? avg(lastsample, samplevecavg) : '
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'max(lastsample, samplevecavg))').format(
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task_type,
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pool.autoscale.scenario.required_sample_percentage,
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),
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'reqVMs = {}TaskAvg / maxTasksPerNode'.format(task_type),
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]
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if pool.autoscale.scenario.rebalance_preemption_percentage is not None:
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req_vms.extend([
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'preemptsamplepercent = '
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'$PreemptedNodeCount.GetSamplePercent(sli)',
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'lastpreemptsample = val($PreemptedNodeCount.GetSample(1), 0)',
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'preemptedavg = avg($PreemptedNodeCount.GetSample(sli))',
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('preemptcount = preemptsamplepercent < {} ? '
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'max(0, lastpreemptsample) : (lastpreemptsample > '
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'preemptedavg ? avg(lastpreemptsample, preemptedavg) : '
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'min(lastpreemptsample, preemptedavg))').format(
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pool.autoscale.scenario.required_sample_percentage),
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])
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else:
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req_vms = [
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'sli = TimeInterval_Second * {}'.format(
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pool.autoscale.scenario.sample_lookback_interval.
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total_seconds()),
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'{}TaskAvg = avg(${}Tasks.GetSample(sli, {}))'.format(
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task_type, task_type,
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pool.autoscale.scenario.required_sample_percentage),
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'reqVMs = {}TaskAvg / maxTasksPerNode'.format(task_type),
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'reqVMs = ({}TaskAvg > 0 && reqVMs < 1) ? 1 : reqVMs'.format(
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task_type),
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]
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if pool.autoscale.scenario.rebalance_preemption_percentage is not None:
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req_vms.extend([
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'preemptcount = avg($PreemptedNodeCount.GetSample('
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'sli, {}))'.format(
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pool.autoscale.scenario.required_sample_percentage),
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])
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if pool.autoscale.scenario.rebalance_preemption_percentage is not None:
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req_vms.extend([
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'currenttotal = $CurrentDedicatedNodes + '
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'$CurrentLowPriorityNodes',
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'preemptedpercent = currenttotal > 0 ? '
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'preemptcount / currenttotal : 0',
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'rebalance = preemptedpercent >= {}'.format(
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pool.autoscale.scenario.rebalance_preemption_percentage),
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])
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else:
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req_vms.extend([
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'preemptcount = 0',
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'rebalance = 0 == 1',
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])
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# compute additional required VMs (subtract out minimums)
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req_vms.append(
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'reqVMs = max(0, reqVMs - minTargetDedicated - minTargetLowPriority)'
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)
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req_vms = ';\n'.join(req_vms)
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if pool.autoscale.scenario.bias_node_type == 'auto':
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target_vms = [
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'divisor = (maxTargetDedicated == 0 || '
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'maxTargetLowPriority == 0) ? 1 : 2',
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'dedicatedVMs = reqVMs / divisor',
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'dedicatedVMs = min(maxTargetDedicated, '
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'(dedicatedVMs > 0 && dedicatedVMs < 1) ? 1 : dedicatedVMs)',
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'remainingVMs = max(0, reqVMs - dedicatedVMs)',
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'redistVMs = rebalance ? min(preemptcount, remainingVMs) : 0',
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'dedicatedVMs = min(maxTargetDedicated, '
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'dedicatedVMs + redistVMs + minTargetDedicated)',
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'remainingVMs = max(0, reqVMs - dedicatedVMs)',
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'lowPriVMs = min(maxTargetLowPriority, '
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'remainingVMs + minTargetLowPriority)',
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'$TargetDedicatedNodes = dedicatedVMs',
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'$TargetLowPriorityNodes = lowPriVMs',
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]
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elif pool.autoscale.scenario.bias_node_type == 'dedicated':
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target_vms = [
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'dedicatedVMs = min(maxTargetDedicated, reqVMs)',
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'remainingVMs = max(0, reqVMs - dedicatedVMs)',
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'$TargetDedicatedNodes = dedicatedVMs',
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'$TargetLowPriorityNodes = min(maxTargetLowPriority, '
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'remainingVMs + minTargetLowPriority)',
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]
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elif pool.autoscale.scenario.bias_node_type == 'low_priority':
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target_vms = [
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'lowPriVMs = min(maxTargetLowPriority, reqVMs)',
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'remainingVMs = max(0, reqVMs - lowPriVMs)',
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'redistVMs = rebalance ? min(preemptcount, remainingVMs) : 0',
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'lowPriVMs = max(minTargetLowPriority, '
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'reqVMs - redistVMs + minTargetLowPriority)',
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'remainingVMs = max(0, reqVMs - lowPriVMs)',
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'$TargetLowPriorityNodes = lowPriVMs',
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'$TargetDedicatedNodes = min(maxTargetDedicated, '
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'remainingVMs + minTargetDedicated)',
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]
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else:
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raise ValueError(
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'autoscale scenario bias node type invalid: {}'.format(
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pool.autoscale.scenario.bias_node_type))
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target_vms = ';\n'.join(target_vms)
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formula = [
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'maxTasksPerNode = {}'.format(minmax.max_tasks_per_node),
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'minTargetDedicated = {}'.format(minmax.min_target_dedicated),
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'minTargetLowPriority = {}'.format(minmax.min_target_low_priority),
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'maxTargetDedicated = {}'.format(minmax.max_target_dedicated),
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'maxTargetLowPriority = {}'.format(minmax.max_target_low_priority),
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req_vms,
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target_vms,
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'$NodeDeallocationOption = {}'.format(
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pool.autoscale.scenario.node_deallocation_option),
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]
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return ';\n'.join(formula) + ';'
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def _formula_day_of_week(pool):
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# type: (settings.PoolSettings) -> str
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"""Generate an autoscale formula for a day of the week scenario
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:param settings.PoolSettings pool: pool settings
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:rtype: str
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:return: autoscale formula
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"""
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minmax = _get_minmax(pool)
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if pool.autoscale.scenario.name == 'workday':
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target_vms = [
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'now = time()',
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'isWorkHours = now.hour >= 8 && now.hour < 18',
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'isWeekday = now.weekday >= 1 && now.weekday <= 5',
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'isPeakTime = isWeekday && isWorkHours',
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]
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elif (pool.autoscale.scenario.name ==
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'workday_with_offpeak_max_low_priority'):
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target_vms = [
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'now = time()',
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'isWorkHours = now.hour >= 8 && now.hour < 18',
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'isWeekday = now.weekday >= 1 && now.weekday <= 5',
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'isPeakTime = isWeekday && isWorkHours',
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'$TargetLowPriorityNodes = maxTargetLowPriority',
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]
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if pool.autoscale.scenario.bias_node_type == 'low_priority':
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target_vms.append('$TargetDedicatedNodes = minTargetDedicated')
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else:
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target_vms.append(
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'$TargetDedicatedNodes = isPeakTime ? '
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'maxTargetDedicated : minTargetDedicated')
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elif pool.autoscale.scenario.name == 'weekday':
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target_vms = [
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'now = time()',
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'isPeakTime = now.weekday >= 1 && now.weekday <= 5',
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]
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elif pool.autoscale.scenario.name == 'weekend':
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target_vms = [
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'now = time()',
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'isPeakTime = now.weekday >= 6 && now.weekday <= 7',
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]
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else:
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raise ValueError('autoscale scenario name invalid: {}'.format(
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pool.autoscale.scenario.name))
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if pool.autoscale.scenario.name != 'workday_with_offpeak_max_low_priority':
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if pool.autoscale.scenario.bias_node_type == 'auto':
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target_vms.append(
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'$TargetDedicatedNodes = isPeakTime ? '
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'maxTargetDedicated : minTargetDedicated')
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target_vms.append(
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'$TargetLowPriorityNodes = isPeakTime ? '
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'maxTargetLowPriority : minTargetLowPriority')
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elif pool.autoscale.scenario.bias_node_type == 'dedicated':
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target_vms.append(
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'$TargetDedicatedNodes = isPeakTime ? '
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'maxTargetDedicated : minTargetDedicated')
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target_vms.append('$TargetLowPriorityNodes = minTargetLowPriority')
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elif pool.autoscale.scenario.bias_node_type == 'low_priority':
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target_vms.append('$TargetDedicatedNodes = minTargetDedicated')
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target_vms.append(
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'$TargetLowPriorityNodes = isPeakTime ? '
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'maxTargetLowPriority : minTargetLowPriority')
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else:
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raise ValueError(
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'autoscale scenario bias node type invalid: {}'.format(
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pool.autoscale.scenario.bias_node_type))
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target_vms = ';\n'.join(target_vms)
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formula = [
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'maxTasksPerNode = {}'.format(minmax.max_tasks_per_node),
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'minTargetDedicated = {}'.format(minmax.min_target_dedicated),
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'minTargetLowPriority = {}'.format(minmax.min_target_low_priority),
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'maxTargetDedicated = {}'.format(minmax.max_target_dedicated),
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'maxTargetLowPriority = {}'.format(minmax.max_target_low_priority),
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target_vms,
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'$NodeDeallocationOption = {}'.format(
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pool.autoscale.scenario.node_deallocation_option),
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]
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return ';\n'.join(formula) + ';'
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def _get_minmax(pool):
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# type: (settings.PoolSettings) -> AutoscaleMinMax
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"""Get the min/max settings for autoscale spec
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:param settings.PoolSettings pool: pool settings
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:rtype: AutoscaleMinMax
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:return: autoscale min max object
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"""
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min_target_dedicated = pool.vm_count.dedicated
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min_target_low_priority = pool.vm_count.low_priority
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max_target_dedicated = pool.autoscale.scenario.maximum_vm_count.dedicated
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if max_target_dedicated < 0:
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max_target_dedicated = _UNBOUND_MAX_NODES
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max_target_low_priority = (
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pool.autoscale.scenario.maximum_vm_count.low_priority
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)
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if max_target_low_priority < 0:
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max_target_low_priority = _UNBOUND_MAX_NODES
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if min_target_dedicated > max_target_dedicated:
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raise ValueError(
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'min target dedicated {} > max target dedicated {}'.format(
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min_target_dedicated, max_target_dedicated))
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if min_target_low_priority > max_target_low_priority:
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raise ValueError(
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'min target low priority {} > max target low priority {}'.format(
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min_target_low_priority, max_target_low_priority))
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return AutoscaleMinMax(
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max_tasks_per_node=pool.max_tasks_per_node,
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min_target_dedicated=min_target_dedicated,
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min_target_low_priority=min_target_low_priority,
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max_target_dedicated=max_target_dedicated,
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max_target_low_priority=max_target_low_priority,
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)
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_AUTOSCALE_SCENARIOS = {
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'active_tasks': _formula_tasks,
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'pending_tasks': _formula_tasks,
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'workday': _formula_day_of_week,
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'workday_with_offpeak_max_low_priority': _formula_day_of_week,
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'weekday': _formula_day_of_week,
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'weekend': _formula_day_of_week,
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}
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def get_formula(pool):
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# type: (settings.PoolSettings) -> str
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"""Get or generate an autoscale formula according to settings
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:param settings.PoolSettings pool: pool settings
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:rtype: str
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:return: autoscale formula
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"""
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if util.is_not_empty(pool.autoscale.formula):
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return pool.autoscale.formula
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else:
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return _AUTOSCALE_SCENARIOS[pool.autoscale.scenario.name](pool)
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