55 lines
1.5 KiB
Go
55 lines
1.5 KiB
Go
/*
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* Copyright 2020 The Dragonfly Authors
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package evaluator
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import (
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"d7y.io/dragonfly/v2/scheduler/resource"
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)
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const (
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// DefaultAlgorithm is a rule-based scheduling algorithm.
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DefaultAlgorithm = "default"
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// MLAlgorithm is a machine learning scheduling algorithm.
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MLAlgorithm = "ml"
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// PluginAlgorithm is a scheduling algorithm based on plugin extension.
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PluginAlgorithm = "plugin"
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)
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type Evaluator interface {
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// Evaluate todo Normalization.
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Evaluate(parent *resource.Peer, child *resource.Peer, taskPieceCount int32) float64
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// IsBadNode determine if peer is a failed node.
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IsBadNode(peer *resource.Peer) bool
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}
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func New(algorithm string, pluginDir string) Evaluator {
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switch algorithm {
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case PluginAlgorithm:
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if plugin, err := LoadPlugin(pluginDir); err == nil {
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return plugin
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}
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// TODO Implement MLAlgorithm.
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case MLAlgorithm, DefaultAlgorithm:
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return NewEvaluatorBase()
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}
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return NewEvaluatorBase()
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}
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