79 lines
3 KiB
Go
79 lines
3 KiB
Go
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// +build !pro,!ent
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package scheduler
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// NewGenericStack constructs a stack used for selecting service placements
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func NewGenericStack(batch bool, ctx Context) *GenericStack {
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// Create a new stack
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s := &GenericStack{
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batch: batch,
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ctx: ctx,
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}
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// Create the source iterator. We randomize the order we visit nodes
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// to reduce collisions between schedulers and to do a basic load
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// balancing across eligible nodes.
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s.source = NewRandomIterator(ctx, nil)
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// Create the quota iterator to determine if placements would result in the
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// quota attached to the namespace of the job to go over.
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s.quota = NewQuotaIterator(ctx, s.source)
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// Attach the job constraints. The job is filled in later.
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s.jobConstraint = NewConstraintChecker(ctx, nil)
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// Filter on task group drivers first as they are faster
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s.taskGroupDrivers = NewDriverChecker(ctx, nil)
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// Filter on task group constraints second
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s.taskGroupConstraint = NewConstraintChecker(ctx, nil)
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// Filter on task group devices
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s.taskGroupDevices = NewDeviceChecker(ctx)
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// Create the feasibility wrapper which wraps all feasibility checks in
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// which feasibility checking can be skipped if the computed node class has
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// previously been marked as eligible or ineligible. Generally this will be
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// checks that only needs to examine the single node to determine feasibility.
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jobs := []FeasibilityChecker{s.jobConstraint}
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tgs := []FeasibilityChecker{s.taskGroupDrivers, s.taskGroupConstraint, s.taskGroupDevices}
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s.wrappedChecks = NewFeasibilityWrapper(ctx, s.quota, jobs, tgs)
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// Filter on distinct host constraints.
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s.distinctHostsConstraint = NewDistinctHostsIterator(ctx, s.wrappedChecks)
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// Filter on distinct property constraints.
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s.distinctPropertyConstraint = NewDistinctPropertyIterator(ctx, s.distinctHostsConstraint)
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// Upgrade from feasible to rank iterator
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rankSource := NewFeasibleRankIterator(ctx, s.distinctPropertyConstraint)
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// Apply the bin packing, this depends on the resources needed
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// by a particular task group.
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s.binPack = NewBinPackIterator(ctx, rankSource, false, 0)
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// Apply the job anti-affinity iterator. This is to avoid placing
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// multiple allocations on the same node for this job.
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s.jobAntiAff = NewJobAntiAffinityIterator(ctx, s.binPack, "")
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// Apply node rescheduling penalty. This tries to avoid placing on a
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// node where the allocation failed previously
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s.nodeReschedulingPenalty = NewNodeReschedulingPenaltyIterator(ctx, s.jobAntiAff)
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// Apply scores based on affinity stanza
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s.nodeAffinity = NewNodeAffinityIterator(ctx, s.nodeReschedulingPenalty)
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// Apply scores based on spread stanza
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s.spread = NewSpreadIterator(ctx, s.nodeAffinity)
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// Normalizes scores by averaging them across various scorers
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s.scoreNorm = NewScoreNormalizationIterator(ctx, s.spread)
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// Apply a limit function. This is to avoid scanning *every* possible node.
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s.limit = NewLimitIterator(ctx, s.scoreNorm, 2, skipScoreThreshold, maxSkip)
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// Select the node with the maximum score for placement
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s.maxScore = NewMaxScoreIterator(ctx, s.limit)
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return s
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}
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