361 lines
12 KiB
Go
361 lines
12 KiB
Go
/*
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Cluster wide dynamic capping
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Step1. Compute the running average of watts of tasks in window.
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Step2. Compute what percentage of total power of each node, is the running average.
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Step3. Compute the median of the percetages and this is the percentage that the cluster needs to be capped at.
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1. First fit scheduling -- Perform the above steps for each task that needs to be scheduled.
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This is not a scheduler but a scheduling scheme that schedulers can use.
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*/
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package schedulers
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import (
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"bitbucket.org/sunybingcloud/electron/constants"
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"bitbucket.org/sunybingcloud/electron/def"
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"container/list"
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"errors"
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"github.com/montanaflynn/stats"
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"log"
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"sort"
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)
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// Structure containing utility data structures used to compute cluster-wide dynamic cap.
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type clusterwideCapper struct {
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// window of tasks.
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window_of_tasks list.List
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// The current sum of requested powers of the tasks in the window.
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current_sum float64
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// The current number of tasks in the window.
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number_of_tasks_in_window int
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}
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// Defining constructor for clusterwideCapper. Please don't call this directly and instead use getClusterwideCapperInstance().
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func newClusterwideCapper() *clusterwideCapper {
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return &clusterwideCapper{current_sum: 0.0, number_of_tasks_in_window: 0}
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}
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// Singleton instance of clusterwideCapper
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var singleton_capper *clusterwideCapper
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// Retrieve the singleton instance of clusterwideCapper.
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func getClusterwideCapperInstance() *clusterwideCapper {
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if singleton_capper == nil {
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singleton_capper = newClusterwideCapper()
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} else {
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// Do nothing
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}
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return singleton_capper
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}
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// Clear and initialize all the members of clusterwideCapper.
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func (capper clusterwideCapper) clear() {
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capper.window_of_tasks.Init()
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capper.current_sum = 0
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capper.number_of_tasks_in_window = 0
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}
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// Compute the average of watts of all the tasks in the window.
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func (capper clusterwideCapper) average() float64 {
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return capper.current_sum / float64(capper.window_of_tasks.Len())
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}
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/*
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Compute the running average.
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Using clusterwideCapper#window_of_tasks to store the tasks.
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Task at position 0 (oldest task) is removed when the window is full and new task arrives.
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*/
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func (capper clusterwideCapper) running_average_of_watts(tsk *def.Task) float64 {
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var average float64
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if capper.number_of_tasks_in_window < constants.Window_size {
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capper.window_of_tasks.PushBack(tsk)
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capper.number_of_tasks_in_window++
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capper.current_sum += float64(tsk.Watts) * constants.Cap_margin
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} else {
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task_to_remove_element := capper.window_of_tasks.Front()
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if task_to_remove, ok := task_to_remove_element.Value.(*def.Task); ok {
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capper.current_sum -= float64(task_to_remove.Watts) * constants.Cap_margin
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capper.window_of_tasks.Remove(task_to_remove_element)
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}
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capper.window_of_tasks.PushBack(tsk)
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capper.current_sum += float64(tsk.Watts) * constants.Cap_margin
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}
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average = capper.average()
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return average
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}
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/*
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Calculating cap value.
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1. Sorting the values of running_average_to_total_power_percentage in ascending order.
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2. Computing the median of above sorted values.
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3. The median is now the cap.
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*/
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func (capper clusterwideCapper) get_cap(running_average_to_total_power_percentage map[string]float64) float64 {
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var values []float64
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// Validation
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if running_average_to_total_power_percentage == nil {
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return 100.0
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}
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for _, apower := range running_average_to_total_power_percentage {
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values = append(values, apower)
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}
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// sorting the values in ascending order.
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sort.Float64s(values)
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// Calculating the median
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if median, err := stats.Median(values); err == nil {
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return median
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}
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// should never reach here. If here, then just setting the cap value to be 100
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return 100.0
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}
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/*
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Recapping the entire cluster. Also, removing the finished task from the list of running tasks.
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We would, at this point, have a better knowledge about the state of the cluster.
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1. Calculate the total allocated watts per node in the cluster.
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2. Compute the ratio of the total watts usage per node to the total power for that node.
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This would give us the load on that node.
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3. Now, compute the average load across all the nodes in the cluster.
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This would be the cap value.
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*/
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func (capper clusterwideCapper) cleverRecap(total_power map[string]float64,
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task_monitor map[string][]def.Task, finished_taskId string) (float64, error) {
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// Validation
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if total_power == nil || task_monitor == nil {
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return 100.0, errors.New("Invalid argument: total_power, task_monitor")
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}
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// watts usage on each node in the cluster.
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watts_usages := make(map[string][]float64)
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host_of_finished_task := ""
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index_of_finished_task := -1
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for _, host := range constants.Hosts {
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watts_usages[host] = []float64{0.0}
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}
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for host, tasks := range task_monitor {
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for i, task := range tasks {
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if task.TaskID == finished_taskId {
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host_of_finished_task = host
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index_of_finished_task = i
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// Not considering this task
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continue
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}
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watts_usages[host] = append(watts_usages[host], float64(task.Watts) * constants.Cap_margin)
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}
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}
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// Updating task monitor
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if host_of_finished_task != "" && index_of_finished_task != -1 {
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log.Printf("Removing task with task [%s] from the list of running tasks\n",
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task_monitor[host_of_finished_task][index_of_finished_task].TaskID)
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task_monitor[host_of_finished_task] = append(task_monitor[host_of_finished_task][:index_of_finished_task],
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task_monitor[host_of_finished_task][index_of_finished_task+1:]...)
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}
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// load on each node in the cluster.
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loads := []float64{}
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for host, usages := range watts_usages {
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total_usage := 0.0
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for _, usage := range usages {
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total_usage += usage
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}
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loads = append(loads, total_usage / total_power[host])
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}
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// Now need to compute the average load.
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total_load := 0.0
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for _, load := range loads {
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total_load += load
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}
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average_load := total_load / float64(len(loads)) // this would be the cap value.
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return average_load, nil
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}
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/*
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Recapping the entire cluster.
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1. Remove the task that finished from the list of running tasks.
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2. Compute the average allocated power of each of the tasks that are currently running.
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3. For each host, determine the ratio of the average to the total power.
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4. Determine the median of the ratios and this would be the new cluster wide cap.
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This needs to be called whenever a task finishes execution.
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*/
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func (capper clusterwideCapper) recap(total_power map[string]float64,
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task_monitor map[string][]def.Task, finished_taskId string) (float64, error) {
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// Validation
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if total_power == nil || task_monitor == nil {
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return 100.0, errors.New("Invalid argument: total_power, task_monitor")
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}
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total_allocated_power := 0.0
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total_running_tasks := 0
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host_of_finished_task := ""
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index_of_finished_task := -1
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for host, tasks := range task_monitor {
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for i, task := range tasks {
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if task.TaskID == finished_taskId {
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host_of_finished_task = host
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index_of_finished_task = i
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// Not considering this task for the computation of total_allocated_power and total_running_tasks
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continue
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}
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total_allocated_power += (float64(task.Watts) * constants.Cap_margin)
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total_running_tasks++
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}
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}
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// Updating task monitor
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if host_of_finished_task != "" && index_of_finished_task != -1 {
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log.Printf("Removing task with task [%s] from the list of running tasks\n",
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task_monitor[host_of_finished_task][index_of_finished_task].TaskID)
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task_monitor[host_of_finished_task] = append(task_monitor[host_of_finished_task][:index_of_finished_task],
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task_monitor[host_of_finished_task][index_of_finished_task+1:]...)
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}
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// For the last task, total_allocated_power and total_running_tasks would be 0
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if total_allocated_power == 0 && total_running_tasks == 0 {
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return 100, errors.New("No task running on the cluster.")
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}
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average := total_allocated_power / float64(total_running_tasks)
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ratios := []float64{}
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for _, tpower := range total_power {
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ratios = append(ratios, (average/tpower)*100)
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}
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sort.Float64s(ratios)
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median, err := stats.Median(ratios)
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if err == nil {
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return median, nil
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} else {
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return 100, err
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}
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}
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/* Quick sort algorithm to sort tasks, in place, in ascending order of power.*/
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func (capper clusterwideCapper) quick_sort(low int, high int, tasks_to_sort []*def.Task) {
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i := low
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j := high
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// calculating the pivot
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pivot_index := low + (high-low)/2
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pivot := tasks_to_sort[pivot_index]
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for i <= j {
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for tasks_to_sort[i].Watts < pivot.Watts {
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i++
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}
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for tasks_to_sort[j].Watts > pivot.Watts {
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j--
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}
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if i <= j {
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temp := tasks_to_sort[i]
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tasks_to_sort[i] = tasks_to_sort[j]
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tasks_to_sort[j] = temp
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i++
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j--
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}
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}
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if low < j {
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capper.quick_sort(low, j, tasks_to_sort)
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}
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if i < high {
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capper.quick_sort(i, high, tasks_to_sort)
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}
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}
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// Sorting tasks in ascending order of requested watts.
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func (capper clusterwideCapper) sort_tasks(tasks_to_sort []*def.Task) {
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capper.quick_sort(0, len(tasks_to_sort)-1, tasks_to_sort)
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}
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/*
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Remove entry for finished task.
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This function is called when a task completes.
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This completed task needs to be removed from the window of tasks (if it is still present)
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so that it doesn't contribute to the computation of the cap value.
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*/
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func (capper clusterwideCapper) taskFinished(taskID string) {
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// If the window is empty the just return. This condition should technically return false.
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if capper.window_of_tasks.Len() == 0 {
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return
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}
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// Checking whether the task with the given taskID is currently present in the window of tasks.
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var task_element_to_remove *list.Element
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for task_element := capper.window_of_tasks.Front(); task_element != nil; task_element = task_element.Next() {
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if tsk, ok := task_element.Value.(*def.Task); ok {
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if tsk.TaskID == taskID {
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task_element_to_remove = task_element
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}
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}
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}
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// we need to remove the task from the window.
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if task_to_remove, ok := task_element_to_remove.Value.(*def.Task); ok {
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capper.window_of_tasks.Remove(task_element_to_remove)
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capper.number_of_tasks_in_window -= 1
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capper.current_sum -= float64(task_to_remove.Watts) * constants.Cap_margin
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}
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}
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// Ranked based scheduling.
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func (capper clusterwideCapper) rankedDetermineCap(available_power map[string]float64,
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tasks_to_schedule []*def.Task) ([]*def.Task, map[int]float64, error) {
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// Validation
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if available_power == nil || len(tasks_to_schedule) == 0 {
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return nil, nil, errors.New("Invalid argument: available_power, tasks_to_schedule")
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} else {
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// Need to sort the tasks in ascending order of requested power.
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capper.sort_tasks(tasks_to_schedule)
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// Now, for each task in the sorted set of tasks, we need to use the Fcfs_determine_cap logic.
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cluster_wide_cap_values := make(map[int]float64)
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index := 0
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for _, tsk := range tasks_to_schedule {
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/*
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Note that even though Fcfs_determine_cap is called, we have sorted the tasks aprior and thus, the tasks are scheduled in the sorted fashion.
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Calling Fcfs_determine_cap(...) just to avoid redundant code.
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*/
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if cap, err := capper.fcfsDetermineCap(available_power, tsk); err == nil {
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cluster_wide_cap_values[index] = cap
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} else {
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return nil, nil, err
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}
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index++
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}
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// Now returning the sorted set of tasks and the cluster wide cap values for each task that is launched.
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return tasks_to_schedule, cluster_wide_cap_values, nil
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}
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}
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// First come first serve scheduling.
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func (capper clusterwideCapper) fcfsDetermineCap(total_power map[string]float64,
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new_task *def.Task) (float64, error) {
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// Validation
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if total_power == nil {
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return 100, errors.New("Invalid argument: total_power")
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} else {
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// Need to calculate the running average
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running_average := capper.running_average_of_watts(new_task)
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// For each node, calculate the percentage of the running average to the total power.
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running_average_to_total_power_percentage := make(map[string]float64)
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for host, tpower := range total_power {
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if tpower >= running_average {
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running_average_to_total_power_percentage[host] = (running_average / tpower) * 100
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} else {
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// We don't consider this host for the computation of the cluster wide cap.
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}
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}
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// Determine the cluster wide cap value.
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cap_value := capper.get_cap(running_average_to_total_power_percentage)
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// Need to cap the cluster to this value.
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return cap_value, nil
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}
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}
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// Stringer for an instance of clusterwideCapper
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func (capper clusterwideCapper) string() string {
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return "Cluster Capper -- Proactively cap the entire cluster."
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}
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