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Pradyumna Kaushik 2016-11-10 20:05:48 -05:00 • committed by Renan DelValle
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62
schedulers/proactive_dynamic_capping/README.md
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##Proactive Dynamic Capping
Perform Cluster wide dynamic capping.
Offer 2 methods:
1. First Come First Serve -- For each task that needs to be scheduled, in the order in which it arrives, compute the cluster wide cap.
2. Rank based cluster wide capping -- Sort a given set of tasks to be scheduled, in ascending order of requested watts, and then compute the cluster wide cap for each of the tasks in the ordered set.
main.go contains a set of test functions for the above algorithm.
###**.go Files**
*main.go*
```
Contains functions that simulate FCFS and Ranked based scheduling.
```
*task.go*
```
Contains the blue print for a task.
A task contains the following information,
1. Image -- The image tag of the benchmark.
2. Name -- The name of the benchmark.
3. Host -- The host on which the task is to be scheduled.
4. CMD -- Comamnd to execute the benchmark.
5. CPU -- CPU shares to be allocated to the task.
6. RAM -- Amount of RAM to be given to the task.
7. Watts -- Requested amount of power, in watts.
8. Inst -- Number of instances.
```
*constants.go*
```
Contains constants that are used by all the subroutines.
Defines the following constants,
1. Hosts -- The possible hosts on which tasks can be scheduled.
2. Cap margin -- Margin of the requested power to be given to the task.
3. Power threshold -- Lower bound of power threshold for a task.
4. Total power -- Total power (including the static power) per node.
5. Window size -- size of the window of tasks.
```
*utils.go*
```
Contains functions that are used by all other Go routines.
```
###Please run the following commands to install dependencies and run the test code.
```
go build
go run main.go
```
###Note
The github.com folder contains a library that is required to compute the median of a given set of values.
###Things to do
1. Need to improve the test cases in main.go.
2. Need to add more test cases to main.go.
3. Add better exception handling to capper.go.

99
schedulers/proactive_dynamic_capping/main.go
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package main
import (
"constants"
"fmt"
"math/rand"
"task"
"proactive_dynamic_capping"
)
func sample_available_power() map[string]float64{
return map[string]float64{
"stratos-001":100.0,
"stratos-002":150.0,
"stratos-003":80.0,
"stratos-004":90.0,
}
}
func get_random_power(min, max int) int {
return rand.Intn(max - min) + min
}
func cap_value_one_task_fcfs(capper *proactive_dynamic_capping.Capper) {
fmt.Println("==== FCFS, Number of tasks: 1 ====")
available_power := sample_available_power()
tsk := task.NewTask("gouravr/minife:v5", "minife:v5", "stratos-001",
"minife_command", 4.0, 10, 50, 1)
if cap_value, err := capper.Fcfs_determine_cap(available_power, tsk); err == nil {
fmt.Println("task = " + tsk.String())
fmt.Printf("cap value = %f\n", cap_value)
}
}
func cap_value_window_size_tasks_fcfs(capper *proactive_dynamic_capping.Capper) {
fmt.Println()
fmt.Println("==== FCFS, Number of tasks: 3 (window size) ====")
available_power := sample_available_power()
for i := 0; i < constants.Window_size; i++ {
tsk := task.NewTask("gouravr/minife:v5", "minife:v5", "stratos-001",
"minife_command", 4.0, 10, get_random_power(30, 150), 1)
fmt.Printf("task%d = %s\n", i, tsk.String())
if cap_value, err := capper.Fcfs_determine_cap(available_power, tsk); err == nil {
fmt.Printf("CAP: %f\n", cap_value)
}
}
}
func cap_value_more_than_window_size_tasks_fcfs(capper *proactive_dynamic_capping.Capper) {
fmt.Println()
fmt.Println("==== FCFS, Number of tasks: >3 (> window_size) ====")
available_power := sample_available_power()
for i := 0; i < constants.Window_size + 2; i++ {
tsk := task.NewTask("gouravr/minife:v5", "minife:v5", "stratos-001",
"minife_command", 4.0, 10, get_random_power(30, 150), 1)
fmt.Printf("task%d = %s\n", i, tsk.String())
if cap_value, err := capper.Fcfs_determine_cap(available_power, tsk); err == nil {
fmt.Printf("CAP: %f\n", cap_value)
}
}
}
func cap_values_for_ranked_tasks(capper *proactive_dynamic_capping.Capper) {
fmt.Println()
fmt.Println("==== Ranked, Number of tasks: 5 (window size + 2) ====")
available_power := sample_available_power()
var tasks_to_schedule []*task.Task
for i := 0; i < constants.Window_size + 2; i++ {
tasks_to_schedule = append(tasks_to_schedule,
task.NewTask("gouravr/minife:v5", "minife:v5", "stratos-001",
"minife_command", 4.0, 10, get_random_power(30, 150), 1))
}
// Printing the tasks that need to be scheduled.
index := 0
for _, tsk := range tasks_to_schedule {
fmt.Printf("task%d = %s\n", index, tsk.String())
index++
}
if sorted_tasks_to_be_scheduled, cwcv, err := capper.Ranked_determine_cap(available_power, tasks_to_schedule); err == nil {
fmt.Printf("The cap values are: ")
fmt.Println(cwcv)
fmt.Println("The order of tasks to be scheduled :-")
for _, tsk := range sorted_tasks_to_be_scheduled {
fmt.Println(tsk.String())
}
}
}
func main() {
capper := proactive_dynamic_capping.GetInstance()
cap_value_one_task_fcfs(capper)
capper.Clear()
cap_value_window_size_tasks_fcfs(capper)
capper.Clear()
cap_value_more_than_window_size_tasks_fcfs(capper)
capper.Clear()
cap_values_for_ranked_tasks(capper)
capper.Clear()
}

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schedulers/proactive_dynamic_capping/src/constants/constants.go
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/*
Constants that are used across scripts
1. The available hosts = stratos-00x (x varies from 1 to 8)
2. cap_margin = percentage of the requested power to allocate
3. power_threshold = overloading factor
4. total_power = total power per node
5. window_size = number of tasks to consider for computation of the dynamic cap.
*/
package constants
var Hosts = []string{"stratos-001", "stratos-002",
"stratos-003", "stratos-004",
"stratos-005", "stratos-006",
"stratos-007", "stratos-008"}
/*
Margin with respect to the required power for a job.
So, if power required = 10W, the node would be capped to 75%*10W.
This value can be changed upon convenience.
*/
var Cap_margin = 0.75
// Lower bound of the power threshold for a tasks
var Power_threshold = 0.6
// Total power per node
var Total_power = map[string]float64 {
"stratos-001": 100.0,
"stratos-002": 150.0,
"stratos-003": 80.0,
"stratos-004": 90.0,
"stratos-005": 200.0,
"stratos-006": 100.0,
"stratos-007": 175.0,
"stratos-008": 175.0,
}
// Window size for running average
var Window_size = 3

1
schedulers/proactive_dynamic_capping/src/github.com/montanaflynn/stats

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Subproject commit 60dcacf48f43d6dd654d0ed94120ff5806c5ca5c

235
schedulers/proactive_dynamic_capping/src/proactive_dynamic_capping/capper.go
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/*
Cluster wide dynamic capping
Step1. Compute running average of tasks in window.
Step2. Compute what percentage of available power of each node, is the running average.
Step3. Compute the median of the percentages and this is the percentage that the cluster needs to be cpaped at.
1. First Fit Scheduling -- Perform the above steps for each task that needs to be scheduled.
2. Rank based Scheduling -- Sort a set of tasks to be scheduled, in ascending order of power, and then perform the above steps for each of them in the sorted order.
*/
package proactive_dynamic_capping
import (
"constants"
"container/list"
"errors"
"github.com/montanaflynn/stats"
"task"
"sort"
"sync"
)
// Structure containing utility data structures used to compute cluster wide dyanmic cap.
type Capper struct {
// window of tasks.
window_of_tasks list.List
// The current sum of requested powers of the tasks in the window.
current_sum float64
// The current number of tasks in the window.
number_of_tasks_in_window int
}
// Defining constructor for Capper.
func NewCapper() *Capper {
return &Capper{current_sum: 0.0, number_of_tasks_in_window: 0}
}
// For locking on operations that may result in race conditions.
var mutex sync.Mutex
// Singleton instance of Capper
var singleton_capper *Capper
// Retrieve the singleton instance of Capper.
func GetInstance() *Capper {
if singleton_capper == nil {
mutex.Lock()
singleton_capper = NewCapper()
mutex.Unlock()
} else {
// Do nothing
}
return singleton_capper
}
// Clear and initialize all the members of Capper.
func (capper Capper) Clear() {
capper.window_of_tasks.Init()
capper.current_sum = 0
capper.number_of_tasks_in_window = 0
}
// Compute the average of watts of all the tasks in the window.
func (capper Capper) average() float64 {
return capper.current_sum / float64(capper.window_of_tasks.Len())
}
/*
Compute the running average
Using Capper#window_of_tasks to store the tasks in the window. Task at position 0 (oldest task) removed when window is full and new task arrives.
*/
func (capper Capper) running_average_of_watts(tsk *task.Task) float64 {
var average float64
if capper.number_of_tasks_in_window < constants.Window_size {
capper.window_of_tasks.PushBack(tsk)
capper.number_of_tasks_in_window++
capper.current_sum += float64(tsk.Watts)
} else {
task_to_remove_element := capper.window_of_tasks.Front()
if task_to_remove, ok := task_to_remove_element.Value.(*task.Task); ok {
capper.current_sum -= float64(task_to_remove.Watts)
capper.window_of_tasks.Remove(task_to_remove_element)
}
capper.window_of_tasks.PushBack(tsk)
capper.current_sum += float64(tsk.Watts)
}
average = capper.average()
return average
}
/*
Calculating cap value
1. Sorting the values of running_average_available_power_percentage in ascending order.
2. Computing the median of the above sorted values.
3. The median is now the cap value.
*/
func (capper Capper) get_cap(running_average_available_power_percentage map[string]float64) float64 {
var values []float64
// Validation
if running_average_available_power_percentage == nil {
return 100.0
}
for _, apower := range running_average_available_power_percentage {
values = append(values, apower)
}
// sorting the values in ascending order
sort.Float64s(values)
// Calculating the median
if median, err := stats.Median(values); err == nil {
return median
}
// should never reach here. If here, then just setting the cap value to be 100
return 100.0
}
// In place sorting of tasks to be scheduled based on the requested watts.
func qsort_tasks(low int, high int, tasks_to_sort []*task.Task) {
i := low
j := high
// calculating the pivot
pivot_index := low + (high - low)/2
pivot := tasks_to_sort[pivot_index]
for i <= j {
for tasks_to_sort[i].Watts < pivot.Watts {
i++
}
for tasks_to_sort[j].Watts > pivot.Watts {
j--
}
if i <= j {
temp := tasks_to_sort[i]
tasks_to_sort[i] = tasks_to_sort[j]
tasks_to_sort[j] = temp
i++
j--
}
}
if low < j {
qsort_tasks(low, j, tasks_to_sort)
}
if i < high {
qsort_tasks(i, high, tasks_to_sort)
}
}
// Sorting tasks in ascending order of requested watts.
func (capper Capper) sort_tasks(tasks_to_sort []*task.Task) {
qsort_tasks(0, len(tasks_to_sort)-1, tasks_to_sort)
}
/*
Remove entry for finished task.
Electron needs to call this whenever a task completes so that the finished task no longer contributes to the computation of the cluster wide cap.
*/
func (capper Capper) Task_finished(finished_task *task.Task) {
// If the window is empty then just return. Should not be entering this condition as it would mean that there is a bug.
if capper.window_of_tasks.Len() == 0 {
return
}
// Checking whether the finished task is currently present in the window of tasks.
var task_element_to_remove *list.Element
for task_element := capper.window_of_tasks.Front(); task_element != nil; task_element = task_element.Next() {
if tsk, ok := task_element.Value.(*task.Task); ok {
if task.Compare(tsk, finished_task) {
task_element_to_remove = task_element
}
}
}
// If finished task is there in the window of tasks, then we need to remove the task from the same and modify the members of Capper accordingly.
if task_to_remove, ok := task_element_to_remove.Value.(*task.Task); ok {
capper.window_of_tasks.Remove(task_element_to_remove)
capper.number_of_tasks_in_window -= 1
capper.current_sum -= float64(task_to_remove.Watts)
}
}
// Ranked based scheduling
func (capper Capper) Ranked_determine_cap(available_power map[string]float64, tasks_to_schedule []*task.Task) ([]*task.Task, map[int]float64, error) {
// Validation
if available_power == nil || len(tasks_to_schedule) == 0 {
return nil, nil, errors.New("No available power and no tasks to schedule.")
} else {
// Need to sort the tasks in ascending order of requested power
capper.sort_tasks(tasks_to_schedule)
// Now, for each task in the sorted set of tasks, we need to use the Fcfs_determine_cap logic.
cluster_wide_cap_values := make(map[int]float64)
index := 0
for _, tsk := range tasks_to_schedule {
/*
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.
Calling Fcfs_determine_cap(...) just to avoid redundant code.
*/
if cap, err := capper.Fcfs_determine_cap(available_power, tsk); err == nil {
cluster_wide_cap_values[index] = cap
} else {
return nil, nil, err
}
index++
}
// Now returning the sorted set of tasks and the cluster wide cap values for each task that is launched.
return tasks_to_schedule, cluster_wide_cap_values, nil
}
}
// First come first serve scheduling.
func (capper Capper) Fcfs_determine_cap(available_power map[string]float64, new_task *task.Task) (float64, error) {
// Validation
if available_power == nil {
// If no power available power, then capping the cluster at 100%. Electron might choose to queue the task.
return 100.0, errors.New("No available power.")
} else {
mutex.Lock()
// Need to calcualte the running average
running_average := capper.running_average_of_watts(new_task)
// What percent of available power for each node is the running average
running_average_available_power_percentage := make(map[string]float64)
for node, apower := range available_power {
if apower >= running_average {
running_average_available_power_percentage[node] = (running_average/apower) * 100
} else {
// We don't consider this node in the offers
}
}
// Determine the cluster wide cap value.
cap_value := capper.get_cap(running_average_available_power_percentage)
// Electron has to now cap the cluster to this value before launching the next task.
mutex.Unlock()
return cap_value, nil
}
}

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schedulers/proactive_dynamic_capping/src/task/task.go
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package task
import (
"constants"
"encoding/json"
"reflect"
"strconv"
"utilities"
)
/*
Blueprint for the task.
Members:
image: <image tag>
name: <benchmark name>
host: <host on which the task needs to be run>
cmd: <command to run the task>
cpu: <CPU requirement>
ram: <RAM requirement>
watts: <Power requirement>
inst: <Number of instances>
*/
type Task struct {
Image string
Name string
Host string
CMD string
CPU float64
RAM int
Watts int
Inst int
}
// Defining a constructor for Task
func NewTask(image string, name string, host string,
cmd string, cpu float64, ram int, watts int, inst int) *Task {
return &Task{Image: image, Name: name, Host: host, CPU: cpu,
RAM: ram, Watts: watts, Inst: inst}
}
// Update the host on which the task needs to be scheduled.
func (task Task) Update_host(new_host string) {
// Validation
if _, ok := constants.Total_power[new_host]; ok {
task.Host = new_host
}
}
// Stringify task instance
func (task Task) String() string {
task_map := make(map[string]string)
task_map["image"] = task.Image
task_map["name"] = task.Name
task_map["host"] = task.Host
task_map["cmd"] = task.CMD
task_map["cpu"] = utils.FloatToString(task.CPU)
task_map["ram"] = strconv.Itoa(task.RAM)
task_map["watts"] = strconv.Itoa(task.Watts)
task_map["inst"] = strconv.Itoa(task.Inst)
json_string, _ := json.Marshal(task_map)
return string(json_string)
}
// Compare one task to another. 2 tasks are the same if all the corresponding members are the same.
func Compare(task *Task, other_task *Task) bool {
// If comparing the same pointers (checking the addresses).
if task == other_task {
return true
}
// Checking member equality
return reflect.DeepEqual(*task, *other_task)
}

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schedulers/proactive_dynamic_capping/src/utilities/utils.go
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package utils
import "strconv"
// Convert float64 to string
func FloatToString(input float64) string {
// Precision is 2, Base is 64
return strconv.FormatFloat(input, 'f', 2, 64)
}