open-nomad/scheduler/stack.go
Tim Gross d9d4da1e9f
scheduler: seed random shuffle nodes with eval ID (#12008)
Processing an evaluation is nearly a pure function over the state
snapshot, but we randomly shuffle the nodes. This means that
developers can't take a given state snapshot and pass an evaluation
through it and be guaranteed the same plan results.

But the evaluation ID is already random, so if we use this as the seed
for shuffling the nodes we can greatly reduce the sources of
non-determinism. Unfortunately golang map iteration uses a global
source of randomness and not a goroutine-local one, but arguably
if the scheduler behavior is impacted by this, that's a bug in the
iteration.
2022-02-08 12:16:33 -05:00

440 lines
14 KiB
Go

package scheduler
import (
"math"
"time"
"github.com/hashicorp/nomad/nomad/structs"
)
const (
// skipScoreThreshold is a threshold used in the limit iterator to skip nodes
// that have a score lower than this. -1 is the lowest possible score for a
// node with penalties (based on job anti affinity and node rescheduling penalties
skipScoreThreshold = 0.0
// maxSkip limits the number of nodes that can be skipped in the limit iterator
maxSkip = 3
)
// Stack is a chained collection of iterators. The stack is used to
// make placement decisions. Different schedulers may customize the
// stack they use to vary the way placements are made.
type Stack interface {
// SetNodes is used to set the base set of potential nodes
SetNodes([]*structs.Node)
// SetTaskGroup is used to set the job for selection
SetJob(job *structs.Job)
// Select is used to select a node for the task group
Select(tg *structs.TaskGroup, options *SelectOptions) *RankedNode
}
type SelectOptions struct {
PenaltyNodeIDs map[string]struct{}
PreferredNodes []*structs.Node
Preempt bool
AllocName string
}
// GenericStack is the Stack used for the Generic scheduler. It is
// designed to make better placement decisions at the cost of performance.
type GenericStack struct {
batch bool
ctx Context
source *StaticIterator
wrappedChecks *FeasibilityWrapper
quota FeasibleIterator
jobVersion *uint64
jobConstraint *ConstraintChecker
taskGroupDrivers *DriverChecker
taskGroupConstraint *ConstraintChecker
taskGroupDevices *DeviceChecker
taskGroupHostVolumes *HostVolumeChecker
taskGroupCSIVolumes *CSIVolumeChecker
taskGroupNetwork *NetworkChecker
distinctHostsConstraint *DistinctHostsIterator
distinctPropertyConstraint *DistinctPropertyIterator
binPack *BinPackIterator
jobAntiAff *JobAntiAffinityIterator
nodeReschedulingPenalty *NodeReschedulingPenaltyIterator
limit *LimitIterator
maxScore *MaxScoreIterator
nodeAffinity *NodeAffinityIterator
spread *SpreadIterator
scoreNorm *ScoreNormalizationIterator
}
func (s *GenericStack) SetNodes(baseNodes []*structs.Node) {
// Shuffle base nodes
idx, _ := s.ctx.State().LatestIndex()
shuffleNodes(s.ctx.Plan(), idx, baseNodes)
// Update the set of base nodes
s.source.SetNodes(baseNodes)
// Apply a limit function. This is to avoid scanning *every* possible node.
// For batch jobs we only need to evaluate 2 options and depend on the
// power of two choices. For services jobs we need to visit "enough".
// Using a log of the total number of nodes is a good restriction, with
// at least 2 as the floor
limit := 2
if n := len(baseNodes); !s.batch && n > 0 {
logLimit := int(math.Ceil(math.Log2(float64(n))))
if logLimit > limit {
limit = logLimit
}
}
s.limit.SetLimit(limit)
}
func (s *GenericStack) SetJob(job *structs.Job) {
if s.jobVersion != nil && *s.jobVersion == job.Version {
return
}
jobVer := job.Version
s.jobVersion = &jobVer
s.jobConstraint.SetConstraints(job.Constraints)
s.distinctHostsConstraint.SetJob(job)
s.distinctPropertyConstraint.SetJob(job)
s.binPack.SetJob(job)
s.jobAntiAff.SetJob(job)
s.nodeAffinity.SetJob(job)
s.spread.SetJob(job)
s.ctx.Eligibility().SetJob(job)
s.taskGroupCSIVolumes.SetNamespace(job.Namespace)
s.taskGroupCSIVolumes.SetJobID(job.ID)
if contextual, ok := s.quota.(ContextualIterator); ok {
contextual.SetJob(job)
}
}
func (s *GenericStack) Select(tg *structs.TaskGroup, options *SelectOptions) *RankedNode {
// This block handles trying to select from preferred nodes if options specify them
// It also sets back the set of nodes to the original nodes
if options != nil && len(options.PreferredNodes) > 0 {
originalNodes := s.source.nodes
s.source.SetNodes(options.PreferredNodes)
optionsNew := *options
optionsNew.PreferredNodes = nil
if option := s.Select(tg, &optionsNew); option != nil {
s.source.SetNodes(originalNodes)
return option
}
s.source.SetNodes(originalNodes)
return s.Select(tg, &optionsNew)
}
// Reset the max selector and context
s.maxScore.Reset()
s.ctx.Reset()
start := time.Now()
// Get the task groups constraints.
tgConstr := taskGroupConstraints(tg)
// Update the parameters of iterators
s.taskGroupDrivers.SetDrivers(tgConstr.drivers)
s.taskGroupConstraint.SetConstraints(tgConstr.constraints)
s.taskGroupDevices.SetTaskGroup(tg)
s.taskGroupHostVolumes.SetVolumes(tg.Volumes)
s.taskGroupCSIVolumes.SetVolumes(options.AllocName, tg.Volumes)
if len(tg.Networks) > 0 {
s.taskGroupNetwork.SetNetwork(tg.Networks[0])
}
s.distinctHostsConstraint.SetTaskGroup(tg)
s.distinctPropertyConstraint.SetTaskGroup(tg)
s.wrappedChecks.SetTaskGroup(tg.Name)
s.binPack.SetTaskGroup(tg)
if options != nil {
s.binPack.evict = options.Preempt
}
s.jobAntiAff.SetTaskGroup(tg)
if options != nil {
s.nodeReschedulingPenalty.SetPenaltyNodes(options.PenaltyNodeIDs)
}
s.nodeAffinity.SetTaskGroup(tg)
s.spread.SetTaskGroup(tg)
if s.nodeAffinity.hasAffinities() || s.spread.hasSpreads() {
// scoring spread across all nodes has quadratic behavior, so
// we need to consider a subset of nodes to keep evaluaton times
// reasonable but enough to ensure spread is correct. this
// value was empirically determined.
s.limit.SetLimit(tg.Count)
if tg.Count < 100 {
s.limit.SetLimit(100)
}
}
if contextual, ok := s.quota.(ContextualIterator); ok {
contextual.SetTaskGroup(tg)
}
// Find the node with the max score
option := s.maxScore.Next()
// Store the compute time
s.ctx.Metrics().AllocationTime = time.Since(start)
return option
}
// SystemStack is the Stack used for the System scheduler. It is designed to
// attempt to make placements on all nodes.
type SystemStack struct {
ctx Context
source *StaticIterator
wrappedChecks *FeasibilityWrapper
quota FeasibleIterator
jobConstraint *ConstraintChecker
taskGroupDrivers *DriverChecker
taskGroupConstraint *ConstraintChecker
taskGroupDevices *DeviceChecker
taskGroupHostVolumes *HostVolumeChecker
taskGroupCSIVolumes *CSIVolumeChecker
taskGroupNetwork *NetworkChecker
distinctPropertyConstraint *DistinctPropertyIterator
binPack *BinPackIterator
scoreNorm *ScoreNormalizationIterator
}
// NewSystemStack constructs a stack used for selecting system and sysbatch
// job placements.
//
// sysbatch is used to determine which scheduler config option is used to
// control the use of preemption.
func NewSystemStack(sysbatch bool, ctx Context) *SystemStack {
// Create a new stack
s := &SystemStack{ctx: ctx}
// Create the source iterator. We visit nodes in a linear order because we
// have to evaluate on all nodes.
s.source = NewStaticIterator(ctx, nil)
// Attach the job constraints. The job is filled in later.
s.jobConstraint = NewConstraintChecker(ctx, nil)
// Filter on task group drivers first as they are faster
s.taskGroupDrivers = NewDriverChecker(ctx, nil)
// Filter on task group constraints second
s.taskGroupConstraint = NewConstraintChecker(ctx, nil)
// Filter on task group host volumes
s.taskGroupHostVolumes = NewHostVolumeChecker(ctx)
// Filter on available, healthy CSI plugins
s.taskGroupCSIVolumes = NewCSIVolumeChecker(ctx)
// Filter on task group devices
s.taskGroupDevices = NewDeviceChecker(ctx)
// Filter on available client networks
s.taskGroupNetwork = NewNetworkChecker(ctx)
// Create the feasibility wrapper which wraps all feasibility checks in
// which feasibility checking can be skipped if the computed node class has
// previously been marked as eligible or ineligible. Generally this will be
// checks that only needs to examine the single node to determine feasibility.
jobs := []FeasibilityChecker{s.jobConstraint}
tgs := []FeasibilityChecker{
s.taskGroupDrivers,
s.taskGroupConstraint,
s.taskGroupHostVolumes,
s.taskGroupDevices,
s.taskGroupNetwork,
}
avail := []FeasibilityChecker{s.taskGroupCSIVolumes}
s.wrappedChecks = NewFeasibilityWrapper(ctx, s.source, jobs, tgs, avail)
// Filter on distinct property constraints.
s.distinctPropertyConstraint = NewDistinctPropertyIterator(ctx, s.wrappedChecks)
// Create the quota iterator to determine if placements would result in
// the quota attached to the namespace of the job to go over.
// Note: the quota iterator must be the last feasibility iterator before
// we upgrade to ranking, or our quota usage will include ineligible
// nodes!
s.quota = NewQuotaIterator(ctx, s.distinctPropertyConstraint)
// Upgrade from feasible to rank iterator
rankSource := NewFeasibleRankIterator(ctx, s.quota)
// Apply the bin packing, this depends on the resources needed
// by a particular task group. Enable eviction as system jobs are high
// priority.
_, schedConfig, _ := s.ctx.State().SchedulerConfig()
enablePreemption := true
if schedConfig != nil {
if sysbatch {
enablePreemption = schedConfig.PreemptionConfig.SysBatchSchedulerEnabled
} else {
enablePreemption = schedConfig.PreemptionConfig.SystemSchedulerEnabled
}
}
// Create binpack iterator
s.binPack = NewBinPackIterator(ctx, rankSource, enablePreemption, 0, schedConfig)
// Apply score normalization
s.scoreNorm = NewScoreNormalizationIterator(ctx, s.binPack)
return s
}
func (s *SystemStack) SetNodes(baseNodes []*structs.Node) {
// Update the set of base nodes
s.source.SetNodes(baseNodes)
}
func (s *SystemStack) SetJob(job *structs.Job) {
s.jobConstraint.SetConstraints(job.Constraints)
s.distinctPropertyConstraint.SetJob(job)
s.binPack.SetJob(job)
s.ctx.Eligibility().SetJob(job)
if contextual, ok := s.quota.(ContextualIterator); ok {
contextual.SetJob(job)
}
}
func (s *SystemStack) Select(tg *structs.TaskGroup, options *SelectOptions) *RankedNode {
// Reset the binpack selector and context
s.scoreNorm.Reset()
s.ctx.Reset()
start := time.Now()
// Get the task groups constraints.
tgConstr := taskGroupConstraints(tg)
// Update the parameters of iterators
s.taskGroupDrivers.SetDrivers(tgConstr.drivers)
s.taskGroupConstraint.SetConstraints(tgConstr.constraints)
s.taskGroupDevices.SetTaskGroup(tg)
s.taskGroupHostVolumes.SetVolumes(tg.Volumes)
s.taskGroupCSIVolumes.SetVolumes(options.AllocName, tg.Volumes)
if len(tg.Networks) > 0 {
s.taskGroupNetwork.SetNetwork(tg.Networks[0])
}
s.wrappedChecks.SetTaskGroup(tg.Name)
s.distinctPropertyConstraint.SetTaskGroup(tg)
s.binPack.SetTaskGroup(tg)
if contextual, ok := s.quota.(ContextualIterator); ok {
contextual.SetTaskGroup(tg)
}
// Get the next option that satisfies the constraints.
option := s.scoreNorm.Next()
// Store the compute time
s.ctx.Metrics().AllocationTime = time.Since(start)
return option
}
// NewGenericStack constructs a stack used for selecting service placements
func NewGenericStack(batch bool, ctx Context) *GenericStack {
// Create a new stack
s := &GenericStack{
batch: batch,
ctx: ctx,
}
// Create the source iterator. We randomize the order we visit nodes
// to reduce collisions between schedulers and to do a basic load
// balancing across eligible nodes.
s.source = NewRandomIterator(ctx, nil)
// Attach the job constraints. The job is filled in later.
s.jobConstraint = NewConstraintChecker(ctx, nil)
// Filter on task group drivers first as they are faster
s.taskGroupDrivers = NewDriverChecker(ctx, nil)
// Filter on task group constraints second
s.taskGroupConstraint = NewConstraintChecker(ctx, nil)
// Filter on task group devices
s.taskGroupDevices = NewDeviceChecker(ctx)
// Filter on task group host volumes
s.taskGroupHostVolumes = NewHostVolumeChecker(ctx)
// Filter on available, healthy CSI plugins
s.taskGroupCSIVolumes = NewCSIVolumeChecker(ctx)
// Filter on available client networks
s.taskGroupNetwork = NewNetworkChecker(ctx)
// Create the feasibility wrapper which wraps all feasibility checks in
// which feasibility checking can be skipped if the computed node class has
// previously been marked as eligible or ineligible. Generally this will be
// checks that only needs to examine the single node to determine feasibility.
jobs := []FeasibilityChecker{s.jobConstraint}
tgs := []FeasibilityChecker{
s.taskGroupDrivers,
s.taskGroupConstraint,
s.taskGroupHostVolumes,
s.taskGroupDevices,
s.taskGroupNetwork,
}
avail := []FeasibilityChecker{s.taskGroupCSIVolumes}
s.wrappedChecks = NewFeasibilityWrapper(ctx, s.source, jobs, tgs, avail)
// Filter on distinct host constraints.
s.distinctHostsConstraint = NewDistinctHostsIterator(ctx, s.wrappedChecks)
// Filter on distinct property constraints.
s.distinctPropertyConstraint = NewDistinctPropertyIterator(ctx, s.distinctHostsConstraint)
// Create the quota iterator to determine if placements would result in
// the quota attached to the namespace of the job to go over.
// Note: the quota iterator must be the last feasibility iterator before
// we upgrade to ranking, or our quota usage will include ineligible
// nodes!
s.quota = NewQuotaIterator(ctx, s.distinctPropertyConstraint)
// Upgrade from feasible to rank iterator
rankSource := NewFeasibleRankIterator(ctx, s.quota)
// Apply the bin packing, this depends on the resources needed
// by a particular task group.
_, schedConfig, _ := ctx.State().SchedulerConfig()
s.binPack = NewBinPackIterator(ctx, rankSource, false, 0, schedConfig)
// Apply the job anti-affinity iterator. This is to avoid placing
// multiple allocations on the same node for this job.
s.jobAntiAff = NewJobAntiAffinityIterator(ctx, s.binPack, "")
// Apply node rescheduling penalty. This tries to avoid placing on a
// node where the allocation failed previously
s.nodeReschedulingPenalty = NewNodeReschedulingPenaltyIterator(ctx, s.jobAntiAff)
// Apply scores based on affinity stanza
s.nodeAffinity = NewNodeAffinityIterator(ctx, s.nodeReschedulingPenalty)
// Apply scores based on spread stanza
s.spread = NewSpreadIterator(ctx, s.nodeAffinity)
// Add the preemption options scoring iterator
preemptionScorer := NewPreemptionScoringIterator(ctx, s.spread)
// Normalizes scores by averaging them across various scorers
s.scoreNorm = NewScoreNormalizationIterator(ctx, preemptionScorer)
// Apply a limit function. This is to avoid scanning *every* possible node.
s.limit = NewLimitIterator(ctx, s.scoreNorm, 2, skipScoreThreshold, maxSkip)
// Select the node with the maximum score for placement
s.maxScore = NewMaxScoreIterator(ctx, s.limit)
return s
}