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Merge branch 'kmeansTaskClassification' of https://bitbucket.org/sunybingcloud/electron into kmeansTaskClassification

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This commit is contained in:
Pradyumna Kaushik 2017-08-22 13:03:17 -04:00
commit e1350838f3
1 changed files with 4 additions and 2 deletions
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6
def/taskUtils.go
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@ -41,7 +41,9 @@ func ClassifyTasks(tasks []Task, numberOfClusters int) []TaskCluster {
func (tc TasksToClassify) classify(numberOfClusters int, taskObservation func(task Task) []float64) []TaskCluster {
clusters := make(map[int][]Task)
observations := getObservations(tc, taskObservation)
// TODO: Make the number rounds configurable based on the size of the workload
// TODO: Make the max number of rounds configurable based on the size of the workload
// The max number of rounds (currently defaulted to 100) is the number of iterations performed to obtain
// distinct clusters. When the data size becomes very large, we would need more iterations for clustering.
if trained, centroids := gokmeans.Train(observations, numberOfClusters, 100); trained {
for i := 0; i < len(observations); i++ {
observation := observations[i]
@ -66,7 +68,7 @@ func getObservations(tasks []Task, taskObservation func(task Task) []float64) []
}
// Size tasks based on the power consumption
// TODO: Size the cluster in a better way just taking an aggregate of the watts resource requirement.
// TODO: Size the cluster in a better way other than just taking an aggregate of the watts resource requirement.
func clusterSize(tasks []Task, taskObservation func(task Task) []float64) float64 {
size := 0.0
for _, task := range tasks {