Session 03_Paper 41

5/19/2018 Session 03_Paper 41

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Proceedings of International Conference on Computing Sciences

WILKES100 ICCS 2013

ISBN: 978-93-5107-172-3

An intelligent middleware for multi parametric load balancing in

grid environment using AHP

Faz Mohammad1,*

, Sunita Yadav2

1M.Tech Scholar, Department of Computer Science and Engineering, AKGEC, Ghaziabad 201009, U.P, India.2Professor, Department of Computer Science and Engineering, AKGEC, Ghaziabad 201009, U.P, India.

Abstract

In Grid environment, nodes have the property to join or leave the grid at any instance of time. Due to joining and leaving

property some nodes may get overloaded and other may getunderloaded and the overall performance of the grid will degrade.

To maintain the performance a load balancing strategy is required. In this paper we are considering multi-parameters for load

balancing. Analytical Hierarchy Process (AHP)is used for decision making. In this paper we proposed an intelligent

middleware for multi-parametric load balancing(IMMLB) in grid environment using AHP.

2013 Elsevier Science. All rights reserved.

Keywords: Grid Computing, Multi-Parameters, Load Balancing, AHP, Fuzzy AHP.

1. Introduction

Grid is an implementation model of distributed computing. Grid is used to share resources like data, storage,

computing power and instrumentetc. Grid enables the Virtual Organizations (VO) to access the coordinated set of

services provided by another organization or departments of the same organization. Grid computing is a class ofHigh Performance Computing (HPC). The main motive of HPC is to enhance the computing capacity and overall

performance of a system[4, 5]. A unique feature of grid is that a node/user connected with a grid, may leave or

join the grid at any instance of time. There is no central authority to authenticate, restrict the connecting nodes

and to distribute the resources among the nodes/users. According to scale and functional grid model is of various

type like cluster, enterprises, global, compute and data grid [5, 6].

In grid model when a node leaves the grid, the application running on it will be transfer to another node [1, 11]. A

load balancing strategy is required to maintain the load among all computing nodes. Load balancing strategy is of

two types, one is static and second is dynamic load balancing [10, 12]. Making a decision for Load balancing is

very important task because it may affect the overall performance. Therefore decision is made by considering the

parameters like network, application characteristics and computing node capacity [8, 14, 15]. Load balancingdecision may automated or manual.

A dynamic load balancing strategy is best suited in Grid environment due to its dynamic nature. The overall

performance may get affected by different parameter together [13]. If a single parameter is considered for

decision making, the performance may limit because other parameters also affect the performance. E.g. only

communication delay is considered for load balancing and applications are transfer to another node, but

processing power of that node may be very low. In that condition there will be a large queue on the node and the

overall performance may limit [14]. There are various types of Grid model like cluster grid, enterprises grid,global grid, data grid, compute grid. In enterprise grid multiple projects or departments of organization can share

resources within a campus or enterprises. There is no need to any security concern and management policy

because all nodes are from same enterprises [3, 7].

* Corresponding author.


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In this paper, we worked on Enterprise Grid Model. A dynamic environment has been created by using

GRIDSIM toolkit. With the help of GRIDSIM, four computing nodes N1, N2, N3 and N4 have generated. These

nodes may transfer their application from one node to another node. Multi-parameters namely

intercommunication delay, numbers of available resources and execution time has beenconsidered to select a bestnode for load transfer. We have generated an intelligent middleware IMMLB, which accepts the values of allthree parameters and works on dynamic load balancing strategy. IMMLB used AHP for making decision of best

node for load transfer. After transferring the load, overall system performance is analyzed by considering multi

and single parameter model.

GridSim

GridSim toolkit provides a facility to simulate grid environment. In grid environment different users,

heterogeneous resources, schedulers and brokers have been generated. It enables to create a dynamic environment

according to requirement and can apply modified algorithms. GridSim is very flexible tool and provides auction

model, data grid extension into GridSim, network extension into GridSim and can simulate an experiment with

multiple Virtual Organizations (VOs) [21, 22]. There are five layers in GridSim architecture namely grid

application, user-level middleware, core grid middleware, grid fabric software and grid fabric hardware [21]. Inour model, in place of user-level middleware, IMMLB works.

Parameters for Load Balancing

There are mainly three parameters and their sub-parameters which may participate in the decision process of load

balancing and hence may affect the performance of grid system. Network parameter has some sub-parameters

like Inter-communication delay, available bandwidth, communication link capacity and network latency. No. ofavailable processing unit, processing power and memory capacity are the characteristics of computing node.

Application can affect the performance due to its execution time, pre-emptive and non-pre-emptive

characteristics.

Automatic Decision Making: Analytical Hierarchy Process

AHP is a structured technique for dealing with complex decisions based on mathematics and psychology. AHP isdeveloped by Thomas L. Saaty in the 1970s and has been extensively studied and refined since then. The AHPprovides a comprehensive and rational framework for structuring a decision problem, for representing andquantifying its elements, for relating those elements to overall goals, and for evaluating alternative solutions [18].It is used around the world in a wide variety of decision situations, in fields such as government, business,industry, healthcare and education [17]. AHP develops priorities for different alternatives and according tocriteria judges the alternatives. Initially priorities are sets according to importance to achieve the goal, after that

priorities are derived for the performance of the alternatives on each criteria, these priorities are derived based onpair-wise assessments using judgments, or rations of measurements from a scales if one exists[16].

AHP has been used in many areas due to the ability to rank choices in the order of their effectiveness in meeting

conflicting objectives [19]. Researches has been successfully employed AHP in selection of one alternative from

many; resource allocation; forecasting; total quality management; business process re-engineering; quality

function deployment, and the balanced scorecard. AHP is a better method, where the parameters are categories

into sub parameters [9].

2. Proposed model

Intelligent middleware IMMLB is implemented on a central server located in an organization as shown in figure

1. All four nodes pass the values of all three parameter which we are considering for load balancing decision.

Middleware processed the preference value of parameters entered by user and generates the score for each node.The node with highest score will be best for load transferring. IMMLB is based on dynamic load balancing

strategy.IMMLB works in different steps as follows grid formation, parameters passing, intelligent middleware

processing and decision making.


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An intelligent middleware for multi parametric load balancing in grid environment using AHP

Fig 1. Working of IMMLB model

2.1. Grid Formation

An Enterprise Grid Model has been created by using GRIDSIM toolkit. With the help of GRIDSIM,four computing nodes N1, N2, N3 and N4 have generated. Each node has some resources and application running

on it.These nodes may transfer their application from one node to another node.Figure 2 shows the generation of

resource on node N1 having resource ID: 5 and No. of parallel elements: 13.

Fig 2. Grid Formation

3. Parameters Passing

In this paper, as the considered parameters are changed, load balancing decision is also changed accordingly.

According to the environment, preference of parameter may be changed. In IMMLB, we are considering threeparameters namely Inter-communication delay (ICD) from network parameters, No. of available resources(NOAR) from communication node characteristics and execution time (E.T.) from application characteristics.

Our model makes decision according to the value of parameters. User can change the preference of parameters as

the values of parameters changes. The values of parameters are passing to the middleware.

After the generation of gird, middleware accepts the value of parameters namely ICD, NOAR and E.T. GridSim

provides the value of all three parameters as follows:

Communication Delay

user.body(): Sending Message_0, at time = 2.000 sec

test.body(): receive Message_0, at time = 2.002 sec

user.body(): Receives Ack for Message_0

total comm. Delay between middleware and node = 2.002-2.000 = 2ms


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Execution Time

[TimeSlot={startTime=2.0, finishTime=2.14748364}]

total execution time = 0.147448364 sec

No. of Available ResourcesCreating a grid user entity with name = User_0, and id = 17

User_0:Creating 3 Gridlets

User_0:ReceivedResourceCharacteristics from Resource_1, with id = 9

GridletID-STATUS-ResourceID-Cost

0-SUCCES-9-27.8514588 ms

4. Intelligent Middleware Processing

Middleware accepts the values of parameter form GRIDSIM, and according to the preference it calculatesthe highest score. This calculation is done using AHP. Figure 3 shows the procedure of AHP on middleware.All nodes pass the value of E.T., NOAR and ICDto middleware. Middleware accepts the values of all three

parameters and compare these values. According to the environment and values of parameters, user enters

preference values to the middleware. After accepting the preference values, AHP processing starts. As shownin Figure 3, initially a weight is assigned to each criterion and then a best node according to each criterion isselected. Finally the node having the highest sore according to criterion and option will be selected as a bestnode for transferring the load.

Fig 3. Intelligent Middleware Processing

5. Decision Making

IMMLB makes a decision to select the best node for load transferring. Decision is made according to AHP

processing. AHP works in three steps as follows:

5.1. Develop the weight for parameters

To generate the weight for each criterion we have done a survey. The survey has done on forty two industrial

persons from different industries like Berizon Data service, CTS, Cybage S/W, Endeavour s/w Ltd, Ernst &

Young Ltd, Fuzitsu, IBM, Info Cept, M-Hance India Ltd, NCR Cooperation, Radius Infratel Ltd, Reppify, Syntel

Ltd, TCS, Tech at Hcentive, Vinsol and Wipro. In the survey industrial persons were asked to give preference

value to the three parameters. We have received pair-wise comparison values of parameters. Preference values

have scaled on three levels as shown in table 1.


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Table 1: Preference Values

S. No. Preference Preference Value

1 Less Preferable < 1

2 Equally Preferable = 1

3 Highly Preferable > 1

After scaling the values, a comparison matrix has calculated as shown in table 2.

Table2: Comparison Matrix

E.T. NOAR ICD

E.T 1.000 0.500 1.000

NOAR 2.000 1.000 2.000

ICD 2.000 0.700 1.000

As middleware get the pair wise comparison values, AHP starts processing on it and generates the priority vectorfor each criterion. There are three steps to calculate priority vectors, which are as follows:

a. Calculate 3rd

root of product.

b. Priority Vector = value of 3rd

root of product/ sum of 3rd

root of product.

c. Add the value of each column which is called Sum.

Priority vector for each criterion calculated by first step of AHP, according to pair wise comparison is shown in

table 3.

Table 3: Develop the Weight for Criteria.

6. Develop the rating for each node for each parameter

Using step two of AHP, intelligent middleware IMMLB calculates priority vector for each node on the basis of

each parameter. Rating of each node for Execution Time is shown in table 4, Rating of each node for NOAR is

shown in table 5 and Rating of each node for ICD is shown in table 6.

Table 4: Rating of Each Node for Execution Time

E.T NOAR ICD 3rd Root of product Priority Vector

E.T 1.000 0.500 1.000 0.794 0.227

NOAR 2.000 1.000 2.000 1.588 0.453

ICD 2.000 0.700 1.000 1.119 0.320

Sum 5.000 2.200 4.000 3.501 1.000


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N1 N2 N3 N43rd root of

productPriority Vector

N1 1.000 1.000 0.733 0.640 0.777 0.280

N2 1.000 1.000 0.734 0.874 0.862 0.310

N3 0.733 0.734 1.000 0.874 0.777 0.280

N4 0.641 0.642 0.874 1.000 0.360 0.130

Sum 3.374 3.376 3.341 3.388 2.776 1.000

Table 5: Rating of Each Node for NOAR

N1 N2 N3 N43rd root of

productPriority Vector

N1 1.000 0.923 0.769 0.846 0.844 0.185

N2 1.083 1.000 0.833 0.917 0.827 0.181

N3 1.300 1.200 1.000 1.100 1.716 0.377

N4 1.181 1.090 0.909 1.000 1.170 0.257

Sum

4.564 4.213 3.511 3.863 4.557 1.000

Table 6: Rating of Each Node for ICD

N1 N2 N3 N43rd root ofproduct

Priority Vector

N1 1.000 1.500 1.000 2.000 1.442 0.356

N2 0.667 1.000 0.667 1.333 0.593 0.146

N3 1.000 1.500 1.000 2.000 1.442 0.356

N4 0.500 0.750 0.500 1.000 0.572 0.142

Sum 3.167 4.750 3.167 6.333 4.049 1.000

7. Calculation of the weighted average rating for each node, chose the one with highest score

In the final step of AHP, IMMLB calculates the score for each node according to each parameter by givenformula. Node having the highest score is the best option for load transfer.

Final scores for each node according to each parameter is shown in table 7. The node N3 with highest score wins

according to AHP calculation and IMMLB processing. Node N1 considered to be overloaded. The application

can be transferred from node N1 to node N3.


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Table 7: Final Scores for Each Node According to Each Parameter.

Criteria

Options

E.T

0.277

NOAR

0.453

ICD

0.320

Score

1.000

N1 0.280 0.185 0.356 0.261

N2 0.310 0.181 0.146 0.199

N3 0.280 0.377 0.356 0.348

N4 0.130 0.257 0.142 0.192

SUM 1.000 1.000 1.000 1.000

8. Results and Analysis

As IMMLB found that node N3 is the best node for load transfer, the load canbetransferred from N1 to N3 to

get higher performance. We get total execution time = 2.169 ms after transferring the load from N1 to N3 asshown in figure 4. Total execution time varies according to environment.

Figure 4.Snapshot of total execution time in IMMLG Model.

For analyzing the performance of multi-pametric load balancing, the total execution timeafter load transfer iscompared with the total execution time of single parametric models. Total execution time of ICD, NOAR and E.T

are 2.466 ms, 2.941 ms and 3.061 ms respectively. Only single parameter Inter-communication delay, Number of

available resources and Execution time are considered for load balancing decision in ICD, NOAR and E.Tmodels respectively.

As the total execution time varies according to environment and load, we have calculated the total execution time

at ten different times and averaged the results for finding the averaged total execution time.The comparison of

average total execution time of IMMLB model with the single parametric models is shown in table 8.

Table 8: Average Execution Time for IMMLB, ICD, NOAR and ET


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Total ExecutionTime

Calculation Slot

IMMLG ICD NOAR ET

TIME 1 2.169 2.466 2.946 3.061

TIME 2 2.035 2.600 2.710 2.192

TIME 3 2.240 2.411 2,566 3.002

TIME 4 2.240 3.100 2.842 2.192

TIME 5 2.005 2.210 2.901 2.171

TIME 6 2.135 3.130 2.533 2.361

TIME 7 2.180 2.630 2.802 2.502

TIME 8 2.130 2.503 2.792 2.220

TIME 9 2.005 2.311 2.601 3.080

TIME 10 2.118 2.402 2.531 2.310AVERAGE 2.126 2.576 2.722 2.509

Table 8 shows that the average total execution time of IMLLB is less as compare to ICD, NOAR and E.T. Hence

considering multiple parameters for decision making for load balancing improves the overall performance as

compare to decision making using single parameter. The graph of total execution time of IMLLB, ICD, NOAR

and ET at ten different time slots is shown in figure 5.

Fig 5.Total execution time of IMLLB, ICD, NOAR and ET at ten different time slots

The performance graph of IMMLB is smoother than single parametric model as shown in figure 5. It depicts that

if multiple parameters are considered for decision making criterion in load balancing, the total execution time of

the grid is less and more stable. Hence the overall performance of the grid in terms of execution time will beincreased.

9. Conclusion And Future Work

The overall performance of IMMLB is better than single parametric model. We are considering three parameters

which can affect the overall performance. In IMMLB, the decision for load balancing is made by considering

overall effect of all three parameters. While in single parametric model, other parameters may restrict the

performance due to its effect.In future work, we can enhance the performance of other grid models and also by considering some other

parameters according to environment. To minimize the decision time we can use fuzzy AHP in place of

0

0.5

1

1.5

2

2.5

3

3.5

Time 1 Time 2 Time 3 Time 4 Time 5 Time 6 Time 7 Time 8 Time 9 Time 10

IMMLB

ICD

NOAR

E.T.


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traditional AHP. Fuzzy AHP uses micro functions by which criterion can be compare with minimum difference

and the decision for load balancing may be more refined [20].

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[11] Krzysztof Kurowski Et al, scheduling jobs on grid Multicriteria approach computational methods in science and technology12(2), 123-138 (2006).

[12] Francesco Palmieri, Network Aware Scheduling For Real Time Execution Support In Data-Intensive Optical Grids FutureGeneration Computer System ELSEVIER 25(2009) 794-803.

[13] Jang UK In, Soocheol Lee, Seungmin Rho and Jong Hyuk Park, Policy-Based Scheduling and Resource Allocation forMultimedia Communication on grid Computing Environment, IEEE system journal vol. 5 no. 4, december 2011.

[14] Luiz F. Bittencourt and Edmundo R. M. Madeira, A performance-oriented adaptive scheduler for dependent tasks on grids,concurrency andcomputation: practice and experience Concurrency Computat.: Pract. Exper.2008;20:10291049 Published online

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[20] Fatma Tiryaki and Beyza Ahlatcioglu, Fuzzy portfolio selection using fuzzy analytical hierarchy process, ELSEVIER,information science 179 (2009) 53-69.

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COMPUTATION: PRACTICE AND EXPERENCE, pract. Exper 0123;34:1-2011.[22] Rajkumar Buyya et al, Constructing A Grid Simulation with Differentiated Network Service Using GridSim, A review IIT

MADARS, distributed computing lab-2012.


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Index

TTest case repository (TCR), 302303

WW-shaped framework, 298

ACO classifier and selector, 303for multi-faceted test case classification and selection, 299300

fuzzy synthesis based classifier and selector, 303multi-faceted test cases optimization, 302

objectives, 301302

optimization stopping criteria, 302parameters and stopping criteria identification, 301302

TCR, 302303

Documents

Session 03_Paper 41