update

src/ga_test.go

@@ -0,0 +1,104 @@
+package main
+
+import (
+	"strconv"
+	"math/rand"
+	"time"
+	"log"
+	"github.com/MaxHalford/eaopt"
+	"math"
+	"testing"
+)
+
+func TestGA(t *testing.T) {
+	numTask := 20
+
+	nodesMap = map[string]NodeStatus{}
+	tasksMap = map[string]Task{}
+
+	for i := 0; i < numTask*3; i++ {
+		node := NodeStatus{ClientID: strconv.Itoa(i), Rack: strconv.Itoa(i % 40), Domain: strconv.Itoa(i % 4)}
+		node.NumCPU = 24
+		node.MemTotal = 188
+		node.TotalBW = 100
+		cnt := rand.Intn(3) + 1
+		for i := 0; i < cnt; i++ {
+			node.Status = append(node.Status, GPUStatus{MemoryTotal: 11439, MemoryAllocated: 0, UUID: node.ClientID + strconv.Itoa(i)})
+		}
+		nodesMap[strconv.Itoa(i)] = node
+	}
+	for i := 0; i < numTask; i++ {
+		isPS := false
+		if i >= 3 {
+			isPS = true
+		}
+		task := Task{Name: strconv.Itoa(i), IsPS: isPS}
+		task.Memory = 4
+		task.NumberCPU = 2
+		task.NumberGPU = 1
+		tasksMap[strconv.Itoa(i)] = task
+	}
+
+	var nodes []NodeStatus
+	var tasks []Task
+
+	for _, node := range nodesMap {
+		nodes = append(nodes, node)
+	}
+	for _, task := range tasksMap {
+		tasks = append(tasks, task)
+	}
+	s := time.Now()
+	allocation := fastBestFit(nodes, tasks)
+	log.Println(time.Since(s))
+
+	// Instantiate a GA with a GAConfig
+	var ga, err = eaopt.NewDefaultGAConfig().NewGA()
+	if err != nil {
+		log.Println(err)
+		return
+	}
+
+	// Set the number of generations to run for
+	ga.NGenerations = math.MaxInt32
+	ga.NPops = 1
+	ga.PopSize = 30 + uint(numTask/2)
+
+	// Add a custom print function to track progress
+	ga.Callback = func(ga *eaopt.GA) {
+		log.Printf("Best fitness at generation %d: %f\n", ga.Generations, ga.HallOfFame[0].Fitness)
+	}
+
+	bestFitness := math.MaxFloat64
+	count := 0
+
+	ts := time.Now()
+
+	ga.EarlyStop = func(ga *eaopt.GA) bool {
+		gap := math.Abs(ga.HallOfFame[0].Fitness - bestFitness)
+		if gap <= 0.000001 || ga.HallOfFame[0].Fitness >= bestFitness {
+			if count >= 30 || time.Since(ts) > time.Second*30 {
+				log.Println("Early Stop")
+				return true
+			} else {
+				count++
+			}
+		} else {
+			bestFitness = ga.HallOfFame[0].Fitness
+			count = 1
+		}
+		return false
+	}
+
+	// Find the minimum
+	err = ga.Minimize(VectorFactory)
+	log.Println(time.Since(ts))
+	log.Println(ga.HallOfFame[0].Genome.(Allocation).TasksOnNode)
+	//fmt.Println(ga.HallOfFame[0].Genome.(Allocation).Nodes)
+	if err != nil {
+		log.Println(err)
+		return
+	}
+
+	log.Println(allocation)
+}