Added a task utility to be able to cluster tasks into N clusters based on their watts resource requirements. Electron now compatible with Go1.8 and no longer with 1.7. Added TODOs.

README.md

@@ -18,6 +18,9 @@ To Do:
  * Make def.Task an interface for further modularization and flexibility.
  * Convert def#WattsToConsider(...) to be a receiver of def.Task and change the name of it to Watts(...).
  * Have a generic sorter for task resources instead of having one for each kind of resource.
+ *  **Critical** -- Add software requirements to the README.md (Mesos version, RAPL version, PCP version, Go version...)
+ * Retrofit to use Go 1.8 sorting techniques. Use def/taskUtils.go for reference.
+ * Retrofit TopHeavy and BottomHeavy schedulers to use the clustering utility for tasks.
 
 **Requires [Performance Co-Pilot](http://pcp.io/) tool pmdumptext to be installed on the
 machine on which electron is launched for logging to work and PCP collector agents installed

def/taskUtils.go

@@ -5,6 +5,34 @@ import (
 	"sort"
 )
 
+// Information about a cluster of tasks
+type TaskCluster struct {
+	ClusterIndex int
+	Tasks        []Task
+	SizeScore    int // How many other clusters is this cluster bigger than
+}
+
+// Classification of Tasks using KMeans clustering using the watts consumption observations
+type TasksToClassify []Task
+
+func (tc TasksToClassify) ClassifyTasks(numberOfClusters int) []TaskCluster {
+	clusters := make(map[int][]Task)
+	observations := getObservations(tc)
+	// TODO: Make the number of rounds configurable based on the size of the workload
+	if trained, centroids := gokmeans.Train(observations, numberOfClusters, 100); trained {
+		for i := 0; i < len(observations); i++ {
+			observation := observations[i]
+			classIndex := gokmeans.Nearest(observation, centroids)
+			if _, ok := clusters[classIndex]; ok {
+				clusters[classIndex] = append(clusters[classIndex], tc[i])
+			} else {
+				clusters[classIndex] = []Task{tc[i]}
+			}
+		}
+	}
+	return labelAndOrder(clusters, numberOfClusters)
+}
+
 // The watts consumption observations are taken into consideration.
 func getObservations(tasks []Task) []gokmeans.Node {
 	observations := []gokmeans.Node{}
@@ -25,6 +53,8 @@ func getObservations(tasks []Task) []gokmeans.Node {
 	return observations
 }
 
+// Size tasks based on the power consumption
+// TODO: Size the cluster in a better way just taking an aggregate of the watts resource requirement.
 func clusterSize(tasks []Task) float64 {
 	size := 0.0
 	for _, task := range tasks {
@@ -39,76 +69,38 @@ func clusterSize(tasks []Task) float64 {
 	return size
 }
 
-// information about a cluster of tasks
-type TaskCluster struct {
-	clusterIndex int
-	tasks        []Task
-	sizeScore    int // how many other clusters is this one bigger than (in the current workload)
-}
+// Order clusters in increasing order of task heaviness
+func labelAndOrder(clusters map[int][]Task, numberOfClusters int) []TaskCluster {
+	// Determine the position of the cluster in the ordered list of clusters
+	sizedClusters := []TaskCluster{}
 
-// Sorting TaskClusters based on sizeScore
-type TaskClusterSorter []TaskCluster
-
-func (slice TaskClusterSorter) Len() int {
-	return len(slice)
-}
-
-func (slice TaskClusterSorter) Less(i, j int) bool {
-	return slice[i].sizeScore <= slice[j].sizeScore
-}
-
-func (slice TaskClusterSorter) Swap(i, j int) {
-	slice[i], slice[j] = slice[j], slice[i]
-}
-
-// order clusters in increasing order of task heaviness
-// TODO: Make this look into task.ClassToWatts, if present.
-func order(clusters map[int][]Task, numberOfClusters int) []TaskCluster {
-	// determine the position of the cluster in the ordered list of clusters
-	clusterSizeScores := []TaskCluster{}
+	// Initializing
 	for i := 0; i < numberOfClusters; i++ {
-		// sizing the current cluster
+		sizedClusters = append(sizedClusters, TaskCluster{
+			ClusterIndex: i,
+			Tasks:        clusters[i],
+			SizeScore:    0,
+		})
+	}
+
+	for i := 0; i < numberOfClusters-1; i++ {
+		// Sizing the current cluster
 		sizeI := clusterSize(clusters[i])
 
-		// comparing with the other clusters
-		for j := 0; j != i; j++ {
-			if sizeI >= clusterSize(clusters[j]) {
-				if len(clusterSizeScores) >= i {
-					clusterSizeScores[i].sizeScore++
-				} else {
-					clusterSizeScores[i] = TaskCluster{
-						clusterIndex: i,
-						tasks:        clusters[i],
-						sizeScore:    1,
-					}
-				}
+		// Comparing with the other clusters
+		for j := i + 1; j < numberOfClusters; j++ {
+			sizeJ := clusterSize(clusters[j])
+			if sizeI > sizeJ {
+				sizedClusters[i].SizeScore++
+			} else {
+				sizedClusters[j].SizeScore++
 			}
 		}
 	}
 
 	// Sorting the clusters based on sizeScore
-	sort.Sort(TaskClusterSorter(clusterSizeScores))
-	return clusterSizeScores
-}
-
-// Classification of Tasks using KMeans clustering using the watts consumption observations
-type TasksToClassify []Task
-
-func (tc TasksToClassify) ClassifyTasks(numberOfClusters int) []TaskCluster {
-	clusters := make(map[int][]Task)
-	observations := getObservations(tc)
-	// TODO: Make the number of rounds configurable based on the size of the workload
-	if trained, centroids := gokmeans.Train(observations, numberOfClusters, 50); trained {
-		for i := 0; i < len(observations); i++ {
-			observation := observations[i]
-			classIndex := gokmeans.Nearest(observation, centroids)
-			if _, ok := clusters[classIndex]; ok {
-				clusters[classIndex] = append(clusters[classIndex], tc[i])
-			} else {
-				clusters[classIndex] = []Task{tc[i]}
-			}
-		}
-	}
-
-	return order(clusters, numberOfClusters)
+	sort.SliceStable(sizedClusters, func(i, j int) bool {
+		return sizedClusters[i].SizeScore <= sizedClusters[j].SizeScore
+	})
+	return sizedClusters
 }