Grid Load Balancing Scheduling Algorithm Based on Statistics

ThinkingThe 9th International Conference for Young Computer Scientists

Bin Lu, Hongbin Zhang

Present by Chen, Ting-Wei

2

Table of content

• Introduction

• Hierarchical Grid system model

• Grid resource allocation and task scheduling

• Simulative experiments

• Conclusions

3

Introduction

• Propose a new task scheduling and resource allocation algorithm– Load tracking module– Job distributing module– Load monitoring module

• Simulate the algorithm

4

Introduction (cont.)

• Based on the hierarchical Grid structure model– Distribution of Grid resources – The strategy of task scheduling– Following statistical thinking

5

Hierarchical Grid system model

6

Grid resource allocation and task scheduling

Load (LLoad (Lxx))Length of CPU Length of CPU Ready queueReady queue

CPU UtilizationCPU Utilization

Light LoadLight Load LLxx < Llow Short Low

Heavy LoadHeavy Load LLxx >Lhigh Long High

Moderate Moderate LoadLoad

Llow < LLxx < Lhigh Normal Normal

• Definition of the Load

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Grid resource allocation and task scheduling (cont.)

• Algorithm analysis and implementation– Three components

• Load tracing module

• Job distributing module

• Load monitor module

8

Grid resource allocation and task scheduling (cont.)

– Three tables are installed on the database server in GRM and DRM

• The first: Every sub-DRM load information

• The second: The number of tasks being operated

• The third: The throughput and the average delayed response

9

Grid resource allocation and task scheduling (cont.)

• Load tracing module– Get the load information of DRM– Monitor periodically the implementation state o

f operations– Report it to the upper level for the overall mana

gement and scheduling

10

Grid resource allocation and task scheduling (cont.)

• Job distributing module– According to load balancing– GRM chooses a suitable DRM to carry out job

distributing– Consider

• CPU utilization • Memory usage

– The weight of load indicators can be determined statically or dynamically

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Grid resource allocation and task scheduling (cont.)

– Load computing formula of the CN

– The average of the sub-DRM’s load or CN’s load

– Variance of load

2

1

n

i j ijj

L a L

1

1 m

DK ii

L Lm

2( ) [( ) ]DK i DKVar L E L L

12

Grid resource allocation and task scheduling (cont.)

– Job algorithmInitialize;For (;;){do

If (There was information form the DRMs or CNs){

Updating the load information of DRM or CNs according to the formula (1)-(3);Calculating the rate of throughput;Calculating the average response delay;If (There was information of completed assignment){

Deducting one assignment of corresponding DRM;

}}

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Grid resource allocation and task scheduling (cont.)

If (Portal gives the assignment to GRM){

If (The node having the load is not unique){ ;Making the node whose variance is as the scheduling nodes, if still not unique, selecting the first node;}Else{Making the node whose load value is be the scheduling node;}Giving it the assignment for scheduling, and updating the number of assignment of DRM;}

}

min min kk

L L

minmax max

DKDK

L LVar Var

maxVar

minL

minL

14

Grid resource allocation and task scheduling (cont.)

• Load monitor module– Surveillance

• The current load of all the DRMs

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Grid resource allocation and task scheduling (cont.)

For (;;){

If (sum < ){

The opening of stand-by resources manager;}Reading the load vector of domain resources manager;Reading the upper and lower threshold provided in profile;If (Every load exceeds the threshold ){

Opening the stand-by resources manager;}If (Every load is below the threshold ){

Removing stand-by resources manager;}

mb

highL

lowL

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Grid resource allocation and task scheduling (cont.)

Else{

Get the last updated time of the nodeIf (The current time – last updated time) > (timeout)Means that the main resource manager failed and it takes proper measures;

}}

17

Simulative experiments

• Use Gridsim to simulate

• Compare performance of the load balancing scheduling method– Min-Min algorithm– Ant algorithm– This algorithm (This paper proposed)

18

Simulative experiments (cont.)

• Configuration of experiment environment

GRM

DRM1 DRM2 DRM3

DRM11 DRM12 DRM13 CN-17台CN-21台

CN-18台 CN-25台 CN-28台

USER

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Simulative experiments (cont.)

• Comparison of experiment results

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Conclusions

• Statistical load balancing scheduling scheme

• Future work– Granular computing idea to improve this

algorithm– More the flexibility and efficiency of this

algorithm

Thank you for your attentionThank you for your attention