1
0
mirror of https://github.com/newnius/YAO-scheduler.git synced 2025-12-16 00:26:43 +00:00
This commit is contained in:
2020-05-26 20:46:11 +08:00
parent f7149310e8
commit ec30e79c81
5 changed files with 298 additions and 218 deletions

199
src/ga.go
View File

@@ -3,14 +3,10 @@ package main
import (
"math/rand"
"github.com/MaxHalford/eaopt"
"time"
"math"
"math"
log "github.com/sirupsen/logrus"
)
var nodesMap map[string]NodeStatus
var tasksMap map[string]Task
// A resource allocation
type Allocation struct {
TasksOnNode map[string][]Task // tasks on nodes[id]
@@ -18,6 +14,7 @@ type Allocation struct {
NodeIDs []string
Flags map[string]bool
Evaluator Evaluator
Tasks []Task
}
func randomFit(allocation Allocation, task Task) (string, bool) {
@@ -27,10 +24,10 @@ func randomFit(allocation Allocation, task Task) (string, bool) {
numberGPU := 0
for _, gpu := range allocation.Nodes[nodeID].Status {
if gpu.MemoryAllocated == 0 {
numberGPU += 0
numberGPU += 1
}
}
if _, ok := allocation.Nodes[nodeID]; ok && len(allocation.TasksOnNode[nodeID]) < numberGPU {
if task.NumberGPU <= numberGPU {
flag = true
break
}
@@ -42,13 +39,16 @@ func firstFit(allocation Allocation, task Task) (string, bool) {
flag := false
nodeID := ""
for _, nodeID = range allocation.NodeIDs {
if _, ok := allocation.Nodes[nodeID]; !ok {
continue
}
numberGPU := 0
for _, gpu := range allocation.Nodes[nodeID].Status {
if gpu.MemoryAllocated == 0 {
numberGPU += 0
numberGPU += 1
}
}
if _, ok := allocation.Nodes[nodeID]; ok && len(allocation.TasksOnNode[nodeID]) < numberGPU {
if task.NumberGPU <= numberGPU {
flag = true
break
}
@@ -57,6 +57,8 @@ func firstFit(allocation Allocation, task Task) (string, bool) {
}
func fastBestFit(nodes []NodeStatus, tasks []Task) Allocation {
//log.Info(nodes)
//log.Info(tasks)
eva := Evaluator{}
eva.init(nodes, tasks)
@@ -64,90 +66,76 @@ func fastBestFit(nodes []NodeStatus, tasks []Task) Allocation {
allocation.TasksOnNode = map[string][]Task{}
for _, task := range tasks {
minCost := math.MaxFloat64
nodeID := ""
for _, node := range nodes {
var best *NodeStatus
for i, node := range nodes {
if _, ok := allocation.TasksOnNode[node.ClientID]; !ok {
allocation.TasksOnNode[node.ClientID] = []Task{}
}
numberGPU := 0
for _, gpu := range allocation.Nodes[nodeID].Status {
for _, gpu := range node.Status {
if gpu.MemoryAllocated == 0 {
numberGPU += 0
numberGPU += 1
}
}
if len(allocation.TasksOnNode[node.ClientID]) >= numberGPU {
if task.NumberGPU > numberGPU {
continue
}
eva.add(node, task)
cost := eva.calculate()
eva.remove(node, task)
if cost < minCost || nodeID == "" {
//log.Info(node, cost)
if cost < minCost || best == nil {
minCost = cost
nodeID = node.ClientID
best = &nodes[i]
}
//fmt.Println(cost)
}
if nodeID == "" {
log.Info(task, " choose ", best.ClientID)
if best == nil {
allocation.Flags["valid"] = false
break
} else {
//fmt.Println(task, nodeID, allocation.TasksOnNode, minCost)
allocation.TasksOnNode[nodeID] = append(allocation.TasksOnNode[nodeID], task)
eva.add(nodesMap[nodeID], task)
}
}
log.Println(eva.calculate())
return allocation
}
func bestFit(allocation Allocation, task Task) (string, bool) {
flag := false
nodeID := ""
minCost := math.MaxFloat64
for _, id := range allocation.NodeIDs {
numberGPU := 0
for _, gpu := range allocation.Nodes[id].Status {
if gpu.MemoryAllocated == 0 {
numberGPU += 0
}
}
if _, ok := allocation.Nodes[id]; ok && len(allocation.TasksOnNode[id]) < numberGPU {
/* add */
allocation.TasksOnNode[id] = append(allocation.TasksOnNode[id], task)
/* evaluate */
cost := evaluate(allocation)
/* revert */
idx := -1
for i, task2 := range allocation.TasksOnNode[id] {
if task2.Name == task.Name {
idx = i
allocation.TasksOnNode[best.ClientID] = append(allocation.TasksOnNode[best.ClientID], task)
eva.add(*best, task)
for i := range best.Status {
//allocate more than 1
if best.Status[i].MemoryAllocated == 0 {
best.Status[i].MemoryAllocated += task.MemoryGPU
break
}
}
copy(allocation.TasksOnNode[id][idx:], allocation.TasksOnNode[id][idx+1:])
allocation.TasksOnNode[id] = allocation.TasksOnNode[id][:len(allocation.TasksOnNode[id])-1]
if cost < minCost || !flag {
nodeID = id
minCost = cost
}
flag = true
}
}
return nodeID, flag
//log.Info(allocation.TasksOnNode)
log.Println("BestFit Cost:", eva.calculate())
return allocation
}
/* Evaluate the allocation */
func (X Allocation) Evaluate() (float64, error) {
//log.Info(X)
if !X.Flags["valid"] {
//fmt.Println("Invalid allocation")
return math.MaxFloat64, nil
}
costNetwork := evaluate(X)
//costNetwork := evaluate(X)
cost := costNetwork
var nodes []NodeStatus
for _, node := range X.Nodes {
nodes = append(nodes, node)
}
eva := Evaluator{}
eva.init(nodes, X.Tasks)
for node, tasks := range X.TasksOnNode {
for _, task := range tasks {
eva.add(X.Nodes[node], task)
}
}
cost := eva.calculate()
//log.Info(cost)
//fmt.Println(taskToNode, cost, len(X.Nodes))
return float64(cost), nil
}
@@ -189,6 +177,12 @@ func (X Allocation) Mutate(rng *rand.Rand) {
for _, task := range tasks {
if nodeID, ok := firstFit(X, task); ok {
X.TasksOnNode[nodeID] = append(X.TasksOnNode[nodeID], task)
for i := range X.Nodes[nodeID].Status {
if X.Nodes[nodeID].Status[i].MemoryAllocated == 0 {
X.Nodes[nodeID].Status[i].MemoryAllocated += task.MemoryGPU
break
}
}
} else {
X.Flags["valid"] = false
}
@@ -196,6 +190,7 @@ func (X Allocation) Mutate(rng *rand.Rand) {
}
//fmt.Println("After", X)
return
/* move tasks */
if !X.Flags["valid"] {
//fmt.Println("Invalid allocation")
@@ -230,7 +225,6 @@ func (X Allocation) Crossover(Y eaopt.Genome, rng *rand.Rand) {
if !Y.(Allocation).Flags["valid"] || !X.Flags["valid"] {
return
}
//fmt.Println("Crossover")
taskToNode := map[string]string{}
for nodeID, tasks := range X.TasksOnNode {
for _, task := range tasks {
@@ -265,6 +259,12 @@ func (X Allocation) Crossover(Y eaopt.Genome, rng *rand.Rand) {
//fmt.Println(X.TasksOnNode)
copy(X.TasksOnNode[nodeID2][idx:], X.TasksOnNode[nodeID2][idx+1:])
X.TasksOnNode[nodeID2] = X.TasksOnNode[nodeID2][:len(X.TasksOnNode[nodeID2])-1]
for i := range X.Nodes[nodeID].Status {
if X.Nodes[nodeID].Status[i].MemoryAllocated == 0 {
X.Nodes[nodeID].Status[i].MemoryAllocated -= task.MemoryGPU
break
}
}
//fmt.Println(X.TasksOnNode)
}
/* reschedule tasks on tgt node */
@@ -291,6 +291,12 @@ func (X Allocation) Crossover(Y eaopt.Genome, rng *rand.Rand) {
for _, task := range tasks {
if nodeID, ok := firstFit(X, task); ok {
X.TasksOnNode[nodeID] = append(X.TasksOnNode[nodeID], task)
for i := range X.Nodes[nodeID].Status {
if X.Nodes[nodeID].Status[i].MemoryAllocated == 0 {
X.Nodes[nodeID].Status[i].MemoryAllocated += task.MemoryGPU
break
}
}
} else {
X.Flags["valid"] = false
}
@@ -319,74 +325,3 @@ func (X Allocation) Clone() eaopt.Genome {
}
return Y
}
func VectorFactory(rng *rand.Rand) eaopt.Genome {
allocation := Allocation{TasksOnNode: map[string][]Task{}, Nodes: map[string]NodeStatus{}, Flags: map[string]bool{"valid": true}}
var nodes []NodeStatus
var tasks []Task
for _, node := range nodesMap {
nodes = append(nodes, node)
}
for _, task := range tasksMap {
tasks = append(tasks, task)
}
/* shuffle */
for n := len(nodes); n > 0; n-- {
randIndex := rng.Intn(n)
nodes[n-1], nodes[randIndex] = nodes[randIndex], nodes[n-1]
}
for n := len(tasks); n > 0; n-- {
randIndex := rng.Intn(n)
tasks[n-1], tasks[randIndex] = tasks[randIndex], tasks[n-1]
}
/* pick nodes */
for _, node := range nodesMap {
allocation.Nodes[node.ClientID] = node
allocation.NodeIDs = append(allocation.NodeIDs, node.ClientID)
}
t := rng.Int() % 10
if t == -1 {
/* best-fit */
ts := time.Now()
/*
for _, task := range tasks {
if nodeID, ok := bestFit(allocation, task); ok {
allocation.TasksOnNode[nodeID] = append(allocation.TasksOnNode[nodeID], task)
} else {
allocation.Flags["valid"] = false
}
}
*/
allocation.TasksOnNode = fastBestFit(nodes, tasks).TasksOnNode
log.Println(time.Since(ts))
//fmt.Println("Best Fit")
} else if t%2 == 0 {
/* first-fit */
for _, task := range tasks {
if nodeID, ok := randomFit(allocation, task); ok {
allocation.TasksOnNode[nodeID] = append(allocation.TasksOnNode[nodeID], task)
} else {
allocation.Flags["valid"] = false
}
}
} else {
/* random-fit */
for _, task := range tasks {
if nodeID, ok := randomFit(allocation, task); ok {
allocation.TasksOnNode[nodeID] = append(allocation.TasksOnNode[nodeID], task)
} else {
allocation.Flags["valid"] = false
}
}
}
//fmt.Println(evaluatue(allocation))
//fmt.Println(allocation)
return allocation
}