Ubiquitous Computing and Communication Journal (ISSN …

GRID SCHEDULING USING VARIOUS PERFORMANCE MEASURES

– A COMPARATIVE STUDY

1Dr.K.Vivekanandan,

2D.Ramyachitra

1Professor, BSMED, Bharathiar University, Coimbatore

E mail: [email protected]

2Assistant Professor, School of Computer Science and Engineering, Bharathiar University, Coimbatore

E mail: [email protected]

ABSTRACT

Grid Computing, an extension of distributed computing, allows sharing of geographically

distributed resources across multiple administrative domains. As the users can access the

resources transparently without knowing where they are physically located, there are many

challenges that have to be considered. One of the challenges involves scheduling the jobs to

the appropriate resources. This paper gives a survey on scheduling algorithms used in grid

environment. The algorithms have been implemented using gridsim, a simulator used for

creating a grid environment. Various performance measures such as makespan, resource

utilization, cost and profit are used for comparing the algorithms.

Keywords:MET, MCT, Max Min, Min Min, OLB, GA, Tabu Search, SA, Grid, Scheduling.

1 NTRODUCTION

A Computational Grid is a collection of

heterogeneous resources such as computational

devices, networks, online instruments, storage

archives etc that provides an enormous potential of

capabilities that can be brought to bear on large

distributed applications and are becoming prevalent

platforms for high performance and resource intensive

applications [1]. In the current networking technology

and based on the availability of the bandwidth, the

computing resources are aggregated together to form a

grid computing environment. These computational

resources which provide free or chargeable services

may consists of one or more processing elements of

same or different technologies [2]. Data Grids are

predicted to be the solution to the large computational

power and data storage requirements of many research

projects. It also enables the sharing of distributed

computational and storage resources among users

located all over the world [3]. Traditional approaches

maintain a centralized server or hierarchically

organized servers to index the resource information.

Centralized system has all the functional software

components on a single computer but centralized

servers becomes a problem in the case of highly

dynamic environment where many resources join,

leave and change characteristics at any time and also

it does not scale to large number of grid nodes across

autonomous organizations. As grid computing is a

form of distributed computing, where the resources

are geographically distributed, centralized form of

systems will be a bottleneck for grid computing.

Hierarchical approaches provide better scalability and

fault tolerance but it takes a long time for resource

information to be updated from the leaf nodes to the

root node [4]. For peer to peer architecture single

point of failure does not exist, but in the case of

centralized system, if the centralized hub is broken, it

will lead to failure of the entire system. For grid

UbiCC Journal, Volume 6: Issue 3 864

Ubiquitous Computing and Communication Journal (ISSN 1992-8424)

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computing, communication topology is required

because it deals with different computers in different

geographical locations. Even though there is a

communication overhead in the case of grid

computing, several computers with unutilized

resources in different geographical locations are used

to solve a single large problem so that overall

computational power is enhanced.

One of the characteristic of the grid is users no

need to have idea where the required resource for

solving their problem is located. Users also no need to

care about the resources they use when their jobs are

running. From the viewpoint of the resources, they do

not need to give a permit to each user to access them

directly but only tasks which have limited access

rights can be allowed to be executed [5]. As grid

involves heterogeneity which arises mainly from the

large variety of resources available within each

category, the Grid resource broker hides the

complexities of the grid computing environment from

a user. This grid resource broker discovers resources

that the user can access using information services,

negotiates for access costs; maps application jobs to

resources, starts the execution process and monitors

the progress of execution [6]. These heterogeneous

resources are managed using metadata, whose

purpose is to provide information about the features of

resources and their effective use. Meta data can

provide information regarding which resources are

available, how resources can be accessed, when they

will be available etc. So, metadata can represent a key

element for the discovery and utilization of resources

on the grid [7]. Apart from hiding the heterogeneity of

the resources, another interesting characteristic is the

transparent pooling of many kinds of resources such

as computing power, storage, data and services. This

enables the applications deployed on the grid to

transparently share resources and also the ability to

allocate and reserve virtualized resources. This

virtualization facilitates sharing of resources and also

allows the preservation of QoS for time critical

applications [8].

Grid computing can be used for applications that

can be split and sent to different resources for

execution. After completion of the execution, the

results can be concatenated and analyzed. There are

many policies for sending the jobs based upon their

characteristics. This will result in efficient utilization

of the idle resources. There is no single point of

failure. If one of the resources gets failed, other will

get over it. It is so scalable and upgrading can be done

on the fly. The split up jobs can also be run in parallel

with high speed on many nodes. MPI can also be used

for message passing among computer resources.

These features make grid computing attractive for the

enterprises also. This enterprise grid computing

reflects the use of grid computing within the context

of a business or enterprise rather than for scientific

applications. Stateful nature of business applications,

the typical underlying multi-tier architecture where

request execution follows a complex path through a

diverse set of components etc makes these enterprise

applications harder to deploy on a Grid infrastructure

[8].

As grid environments include many servers

across various administrative domains, managing and

utilizing those resources and keeping configurations

in synchronization will be challenging in large

environments. Simulation is the only feasible way to

analyze algorithms on large scale distributed systems

as in grid environment. Simulation works well by

making the analysis system simple by avoiding the

overhead of co-ordination of real resources compared

to using the real system in real time environment.

Gridsim toolkit allows modeling and simulation of

entities in grid computing users , applications,

resources and resource brokers for design and

evaluation of scheduling algorithms. Some of the

features of gridsim includes modeling of

heterogeneous types of resources, resource capability

can be defined in the form of MIPS, resources can be

located in any time zone, advance reservation can be

made for the resources, submission of unlimited

number of applications for the execution, network

speed between the resources can be specified etc [9].

The remaining part of the paper is organized as

follows. Section 2 deals with scheduling in grid

environment. Section 3 shows the comparison of the

scheduling algorithms’ using simulation results and

finally section 4 gives the conclusion.

2 SCHEDULING IN GRID ENVIRONMENT

Scheduling is the process of allocation of tasks to

the appropriate resources. Before scheduling the tasks



to the resources, the sequence of tasks in which the

execution will be performed and the time duration of

each activity should also be determined. Job shop

scheduling is one of the most well known online

problems and the basic models were developed with

makespan as the objective. Makespan is the time

taken by a given set of resources to execute a

sequence of tasks. Scheduling algorithms at the

beginning were developed with an objective of

minimization of the makespan.

During 1960’s mathematical programming such

as integer and dynamic programming has been applied

to job shop scheduling problems. But as job shop

scheduling problems are NP Complete problems, it is

not possible to find the exact schedule. So many

researchers proposed new techniques and heuristics

that give optimal schedule which was better compared

to scheduling using mathematical programming.

Dispatching rules or heuristics have been applied to

scheduling problems which is classified into different

classes according to the performance criteria for

which they have been applied [10]. Class 1 contains

simple priority rules based on processing times,

deadlines, slack and arrival time. Class 2 contains

combinations of rules from class 1. Class 3 contains

rules that are referred to as Weight Priority Indexes.

Even though a considerable number of works studied

on the above said rules, most of the work concentrated

on processing time i.e., makespan. Performance

measures other than makespan that were optimized

were deadline, tardiness, throughput and utilization.

Some research works developed optimization

measures on the resource provider’s side also. Apart

from these performance measures, other Quality of

Service includes cost for resource consumer,

reliability of the resource, income, profit for resource

provider etc. This paper has compared several

algorithms using the QoS such as makespan, cost,

income and profit.

In general scheduling algorithms can be classified

into local and global scheduling [11]. Processes are

assigned to the time slices of a single processor in the

case of local scheduling. Decision has to be taken

where to execute the process in the case of global

scheduling. Global scheduling uses information about

the system to allocate processes to multiple processors

and obviously grid scheduling falls into the global

scheduling branch [12]. Global scheduling can be

either static or dynamic. In static scheduling, all the

information regarding tasks and resources should be

known before the execution starts. In the case of

dynamic scheduling, task is allocated on the fly as the

application executes. Both static and dynamic

scheduling is adopted in grid computing depending

upon the application and the available information.

Some of the heuristics such as OLB, MET, MCT, Min

min, Max min, Duplex, Genetic Algorithm, SA, Tabu

Search, A* [13], ACO , PSO etc are widely adopted

for scheduling in grid environment and are discussed

here.

Opportunistic Load Balancing (OLB) assigns

each to the machine that is available in an arbitrary

order and it does not consider the expected execution

time on that machine. In Minimum Execution Time

(MET), the task is assigned in an arbitrary manner to

the machine with the best expected execution time for

the task, regardless of the availability of the machine.

Minimum Completion Time (MCT) assigns each task

in arbitrary order to the machine with the minimum

expected completion time for that task. Min min is

based on the MCT but Min min considers all

unmapped tasks during each mapping decision and

MCT only considers one task at a time. In Max min,

the task with maximum completion time is allocated

to the machine that has minimum completion time.

Duplex performs both Min min and Max min and uses

the better solution [13]. Genetic algorithm is an

evolutionary technique for large space search. It

operates on a population of solutions rather than on a

single solution. Much work has been done using GA

for grid scheduling [14]. A set of chromosomes are

initialized and the fitness is calculated for the

chromosomes. Good chromosomes are selected,

crossover is performed and mutation is done. This

process is repeated until a good mapping is

performed. Simulated annealing is an iterative

technique like genetic algorithm but only considers

one possible solution for each task at a time. It

probabilistically allows poorer solutions to be

accepted and this probability is based on a system

temperature that decreases for each iteration. Initial

temperature is the makespan of the initial mapping.

Then the mapping is mutated and the temperature is

reduced by certain percentage and the makespan is

found out. This process is iterated until an optimum

solution is got. Tabu search(TS) explores the search

space of all feasible solutions by a sequence of moves.

It performs a number of iterations and at each

iteration, TS moves to the best solution that is not



forbidden and thus independent of local optima. A* is

a search technique based on a µ-ary tree, beginning at

a root node that is a null solution. The nodes in the

tree represent partial mapping and these partial

mappings has one more task mapped than the parent

node. Each parent node generates children and

becomes inactive. The tree is pruned to make the

execution time traceable. When a node is added, the

tree is pruned by deactivating the leaf node with the

largest makespan of the partial solution. This process

continues until a leaf node representing a complete

mapping is reached [13].

3 COMPARISON USING SIMULATION

The scheduling algorithms have been tested using

Gridsim, a simulator for the grid environment.

Consistent and inconsistent resources as well as

heterogeneity between the tasks and resources are

used for testing the algorithms. Heterogeneity in tasks

is variation in size between the tasks and in resources;

it is variation in speed between them. Table 1 shows

the comparison of various scheduling algorithms for

low heterogeneity tasks and low heterogeneity

consistent resources, table 2 shows the results for low

heterogeneity tasks and high heterogeneity resources,

table 3 shows the results for high heterogeneity tasks

and low heterogeneity resources and finally table 4

shows the results for high heterogeneity tasks and

high heterogeneity resources. In table 1, the processor

speed ranges from 0.10MIPS to 0.29MIPS for 1024 x

32 matrix, 0.1MIPS to 0.224MIPS for 512 x 16

matrix, 0.1MIPS to 0.25MIPS for 256 x 8 matrix and

0.1MIPS to 0.24MIPS for 128 x 4 matrix. Here, the

speed of the processors differs from 0.01MIPS to

0.19MIPS for 1024 x 32 matrix, 0.001MIPS to

0.15MIPS for 512 x 16 matrix, 0.01MIPS to

0.15MIPS for 256 x 8 matrix and 0.04MIPS to

0.14MIPS for 128 x 4 matrix. Various performance

measures such as makespan, resource utilization, cost

and profit has been used for comparing the

algorithms.

Makespan is found out using

for n number of jobs and m number of

machines, where C[i] is the completion time of a job

i.From table 1, it is seen that the performance of MET

is not good compared to other algorithms with respect

to all the performance measures. Makespan is high,

resource utilization is poor, the cost of using it is also

high and the profit obtained out of it also is very less.

The results for various resource matrices 128 tasks x 4

resources, 256 tasks x 8 resources, 512 tasks x 16

resources, 1024 tasks x 32 resources shows that MET

does not perform as well as other algorithms. This

performance can be seen from the results of the table

7 and table 8, where only one resource that has the

highest speed is utilized whereas other resources are

not utilized. OLB performs slightly better compared

to MET in terms of makespan and resource utilization.

The cost incurred is also less for OLB compared to

MET and profit obtained for OLB is better compared

to MET. This is because, in OLB, the machines that

are available are considered for allocation of tasks to

the machines. Max min, Min min and MCT

performance with respect to makespan and resource

utilization are more or less similar. GA , Tabu and SA

performs better and time taken by these algorithms is

less than 25% compared to all the above algorithms

for various resource matrices and resource utilization

is also more than 50%. The reason for less time taken

by the algorithms GA, Tabu and SA is all the

resources are more or less utilized in an average

manner. This performance can be seen from the

results of tables 2 also. Table 3 shows the results of

scheduling algorithms using consistent resources for

high heterogeneity in tasks and low heterogeneity in

resources. Here, OLB takes more time compared to

other algorithms and this may be due to high

heterogeneity in size of the tasks. Tables 5 and 6

shows the results of scheduling algorithms using

inconsistent resources. Here also, OLB takes more

time for execution of all the tasks compared to other

algorithms. In the case of inconsistent resources, the

speed of a resource is not constant for all the tasks it

executes and it changes randomly between the tasks.

Tables 7 show the individual resource utilization for

consistent resources. It is seen from the table that for

low heterogeneity tasks, some of the resources for

Max Min, Min Min and MCT algorithms are not

utilized, for MET only single resource that has the

highest speed is utilized, but for GA, Tabu and SA, all

the resources are utilized. As the speed of a resource

varies between the tasks, all the resources are utilized

for all the algorithms. Figures 1 to 11 shows the

pictorial comparison of all the algorithms.



Scheduling

algorithm

Resource

Matrix

Makespan

(ms)

Resource

Utilization(%) Cost (Rs.) Profit (Rs.)

MET

128 x 4

214155 9.02 5655 13195

OLB 172288 18.11 3892.59 14957

Max – Min 136604 16.03 5185.15 13664.85

Min –Min 137146 15.72 2072.94 16777.059

MCT 134933 15.86 5187.95 13662.05

GA 43760 68.67 4048.57 14796.63

TABU 44040 68.18 440.40 3963.60

SA 46160 66.86 4138.65 15159.74

MET

256 x 8

422477 4.63 8107.89 30501.11

OLB 298776 10.33 8509.73 30099.27

Max – Min 263171 7.46 9843 28766

Min –Min 263544 7.39 4729.475 33879.525

MCT 263716 7.37 9873.51 28735.49

GA 44240 70.42 8541.34 30056.55

TABU 46120 67.55 461.20 4150.80

SA 46476 67.57 8515.73 30081.96

MET

512 x 16

835115 2.32 16035.18 60322.819

OLB 554619 5.26 17927.92 58430.075

Max – Min 520090 3.86 19469.039 56888.96

Min –Min 521897 3.87 9048.098 67309.902

MCT 521998 3.89 19440.33 56917.67

GA 45160 64.54 17712.32 58615.56

TABU 46890 59.25 984.69 3704.31

SA 48670 60.25 17705.06 58641.44

MET

1024 x 32

1574416 1.08 23103.89 130922.1

OLB 1068390 2.51 33685.08 120340.92

Max – Min 1030180 1.63 27030.594 126995.405

Min –Min 1030251 1.63 22333.769 131692.23

MCT 1030579 1.63 27051.40 126974.598

GA 45509 59.25 33514.54 120427.57

TABU 44130 60.79 1323.90 3089.10

SA 45674 58.85 33426.08 120376.31 Table. 1 Performance comparison of scheduling algorithms using consistent resources for low

heterogeneity tasks and low heterogeneity resources

Scheduling

algorithm

Resource

Matrix

Makespan

(ms)

Resource

Utilization(%) Cost (Rs.)

Profit

(Rs.)

MET

128 x 4

162539 3.81 7539.99 11310

OLB 144258 9.57 4840.4 14009.6

Max – Min 134909 4.09 6600.3 12249.7

Min –Min 135194 4.20 7256.41 11593.59

MCT 135119 3.96 6605.9 12244.1

GA 29590 49.08 5005.59 13818.45

TABU 31257 47.91 937.71 3750.84

SA 28841 50.107 5000.05 13826.42

MET 256 x 8 316146 1.82 3860.9 34748.1



OLB 289870 8.40 8352.38 30256.62

Max – Min 263977 2.07 8295.069 30313.93

Min –Min 263636 2.30 15153.394 23455.605

MCT 263886 2.04 8373.04 30235.96

GA 88140 28.73 8500.34 30066.37

TABU 96280 27.30 962.80 3851.20

SA 93440 27.54 8524.95 30059.36

MET

512 x 16

613820 0.84 19089.5 57268.5

OLB 559846 3.87 14871.6889 61486.311

Max – Min 520982 0.96 17830.418 58527.5819

Min –Min 522346 0.86 34838.15 4151.8599

MCT 525856 0.93 17840.945 58517.055

GA 113025 19.09 5041.09 61403.78

TABU 118025 18.35 141.63 4579.37

SA 112025 19.20 14787.43 61472.4

MET

1024 x 32

1459203 0.88 46207.79 107818.20

OLB 1051922 1.24 30216.24 123809.758

Max – Min 1279819 0.95 38513.40 115512.599

Min –Min 1288136 2.48 22330.879 131695.120

MCT 1284228 0.96 38467.85 115558.15

GA 48285 28.18 29490.48 39922.44

TABU 51208 26.72 46.09 4562.63

SA 52107 26.45 29358.95 124628.81 Table 2 Performance comparison of scheduling algorithms using consistent resources for low

heterogeneity tasks and high heterogeneity resources

Scheduling

algorithm

Resource

Matrix

Makespan

(ms)

Resource


MET

128 x 4

3880766 24.07 268352.09 626154.90

OLB 5113812 25.09 204296.55 690210.45

Max – Min 1898531 63.53 224420.3 670086.7

Min –Min 2304391 51.06 92482.245 802024.755

MCT 3262297 42.88 194271.599 700235.4

GA 1938757 64.43 219516.81 674984.92

TABU 2305957 48.24 166028.9 387400.78

SA 3129511 45.39 194109.51 700393.58

MET

256 x 8

7484688 12.02 377329.88 1419479.11

OLB 8664125 15.27 351807.69 1445001.31

Max – Min 2225282 55.67 420123.31 1376685.68

Min –Min 5541282 26.90 151009.95 1645799.05

MCT 2907328 42.99 452693.55 1344115.45

GA 2266728 56.02 418039.74 1378759.28

TABU 6128712 12.44 198570.27 536875.17

SA 2519668 51.27 287936.65 1345575.56

MET

512 x 16

7970094 5.83 389804.73 1466408.26

OLB 9250594 8.11 424681.035 1431531.965

Max – Min 2226797 27.35 431989.34 1424223.652

Min –Min 4418719 15.99 162751.122 1693461.87

MCT 2738828 21.57 427949.85 1428263.15

GA 2354960 27.54 437389.14 1418792.89



TABU 3212140 19.53 `128838.94 484679.8

SA 2333864 25.27 434205.31 1357046.95

MET

1024 x 32

12647516 2.87 504720.44 2860082

OLB 14406703 3.98 675408.861 2689394.13

Max – Min 2303109 18.76 711705.40 2653097.598

Min –Min 4978312 9.82 379995.305 2984807.695

MCT 2537031 15.63 705748.263 2659054.73

GA 2318479 20.46 729954.02 2634764.97

TABU 3332178 15.92 439423.70 1025321.98

SA 2696481 20.27 756135.12 2670361.54 Table 3 Performance comparison of scheduling algorithms using consistent resources for high

heterogeneity tasks and low heterogeneity resources

Scheduling

algorithm

Resource

Matrix

Makespan

(ms)

Resource


MET

128 x 4

695314 20.17 178398.4 267597.6

OLB 152393 10.12 4890.4499 13959.55

Max – Min 332308 71.80 137410.90 308585.1

Min –Min 714637 40.64 133536.659 312459.34

MCT 425172 58.68 131669.50 314326.5

GA 318895 76.325 137384.07 308494.46

TABU 762741 42.34 22882.23 91528.92

SA 694739 44.11 127048.58 318917.42

MET

256 x 8

3942846 11.67 298031.6 2682284.4

OLB 12732685 13.96 746916.669 2233399.33

Max – Min 2347847 31.87 472019.379 2508296.62

Min –Min 4174881 23.81 747866.54 2232449.46

MCT 4071218 22.95 645159.48 2335156.52

GA 2385458 37.35 465104.28 2515170.82

TABU 5692421 24.165 461086.10 41449774.91

SA 9110100 14.44 469720.71 2510525.67

MET

512 x 16

8325021 5.86 1793097 5379291

OLB 8526668 7.05 1498694.768 5673693.232

Max – Min 6181945 8.77 1737850.466 5434537.534

Min –Min 6482455 7.98 1857856.23 5347676.67

MCT 6519620 7.54 1726440.364 5445947.636

GA 6178605 14.40 1649616.19 5522631.89

TABU 7732511 12.58 603909.11 5435181.98

SA 27122721 7.28 2213584.76 4958678.28

MET

1024 x 32

4400751 2.39 979892.99 2286417

OLB 3190903 8.51 646476.81 2619833.18

Max – Min 1031158 11.71 686496.57 2579813.42

Min –Min 1397532 10.08 1479460.41 1786849.58

MCT 1073271 10.19 671482.305 2594827.695

GA 725923 34.538 644928.78 2621080.31

TABU 1690028 19.38 45546.25 140356.83

SA 922670 28.83 619216.77 2627355.22 Table 4 Performance comparison of scheduling algorithms using consistent resources for high

heterogeneity tasks and high heterogeneity resources

Scheduling Resource Makespan Resource Cost (Rs.) Profit (Rs.)



algorithm Matrix (ms) Utilization(%)

MET

128 x 4

147860 3.82 4119.6 14730.4

OLB 188031 14.99 3976.55 14873.45

Max – Min 140235 31.33 4145.34 14704.65

Min –Min 153515 7.68 3877.3 14972.7

MCT 148922 6.48 4049.29 14800.7

GA 10962 94.77 11637.53 18239.73

TABU 11126 89.84 2160.98 3821.93

SA 10446 93.23 11306.73 16083.28

MET

256 x 8

275282 2.14 8640.77 29968.23

OLB 2574657 12.03 8592.38 30016.62

Max – Min 262484 7.80 8784.32 29824.68

Min –Min 263547 2.16 8462.16 30146.83

MCT 262625 1.90 8145.44 30463.56

GA 11342 87.69 30053.6 15053.79

TABU 13917 81.43 2477.78 1635.20

SA 12030 86.47 29564.4 15610.46

MET

512 x 16

523093 0.88 17981.58 58376.41

OLB 840437 5.59 17283.22 59074.77

Max – Min 526516 6.76 17695.15 58662.84

Min –Min 518453 0.99 17901.78 58456.22

MCT 518094 0.88 17576.33 58781.662

GA 12523 86.96 48507.83 34162.31

TABU 13137 85.83 9535.8 3654.64

SA 12487 71.38 48642.87 35811.2

MET

1024 x 32

1034484 0.44 33519.42 120506.58

OLB 1165657 2.77 33623.71 120402.28

Max – Min 1030047 3.07 33611.97 120414.02

Min –Min 1030109 0.48 33744.819 120281.181

MCT 1030266 0.44 33642.17 120383.82

GA 13270 84.66 131978.52 88504.52

TABU 14070 81.28 4099.86 1739.19

SA 14197 83.43 139052.32 96023.78 Table 5 Performance comparison of scheduling algorithms using inconsistent resources for low

heterogeneity tasks

Scheduling

algorithm

Resource

Matrix

Makespan

(ms)

Resource


MET

128 x 4

1081344 23.92 229105.6 665401.4

OLB 3126406 24.56 145151.60 749355.39

Max – Min 567141 57.84 195582.3 698924.7

Min –Min 847547 38.48 225574.69 668932.3

MCT 719656 49.30 219846.45 674660.55

GA 454623 70.54 222395.9 466477.65

TABU 1771476 36.05 648961.13 1147758.68

SA 700102 48.56 221234.97 800757.16

MET

256 x 8

1560797 14.15 300485.24 1496323.76

OLB 3373360 14.66 298103.26 1498705.74

Max – Min 589000 66.17 419947.11 1376861.89



Min –Min 3067328 20.86 389669.33 1407139.66

MCT 743078 38.54 449502.08 1347306.92

GA 755001 49.79 1014982.4 750375.83

TABU 1740652 23.96 169084.57 815115.19

SA 1320677 28.01 2314509.38 176405.65

MET

512 x 16

1747219 6.56 398947.56 1457265.43

OLB 4801890 7.05 459938.89 1396274.10

Max – Min 2127859 13.84 419777.74 1436435.25

Min –Min 1632328 10.31 422842.51 1433370.48

MCT 1036578 10.90 358612.471 1497600.52

GA 531628 36.02 878863.56 547907.17

TABU 1303488 16.22 202318.77 19903.12

SA 773794 31.63 1166248.24 601501.78

MET

1024 x 32

2864813 3.47 698004.23 2666798.76

OLB 6951562 3.92 732777.02 2632025.97

Max – Min 6509391 5.66 796115.65 2568687.34

Min –Min 1695610 6.01 742257.79 2622545.20

MCT 1310531 7.69 637659.57 2727143.42

GA 620573 27.62 1924807.93 1611180.62

TABU 3037900 8.12 1295783.10 636772.85

SA 1603407 13.25 2998679.22 1888930.15 Table 6 Performance comparison of scheduling algorithms using inconsistent resources for high

heterogeneity tasks

ResUtil (l-l

heterogeneity) MET OLB

Max-

Min

Min-

Min MCT GA Tabu SA

R1 0.0 31.40 0.0 0.0 0.0 100 40.08 44.25

R2 0.0 19.54 0.0 0.0 0.0 77.22 51.36 54.24

R3 36.09 11.93 29.66 28.76 28.77 48.72 67.75 68.94

R4 0.0 9.56 34.45 34.11 34.66 64.41 100 100

ResUtil (l-h


Max-

Min

Min-

Min MCT GA Tabu SA

R1 15.25 3.37 7.82 7.98 7.61 20.51 19.03 21.26

R2 0.0 17.60 0.0 0.0 0.0 100.0 100 22.91

R3 0.0 4.25 8.555 8.82 8.24 22.72 19.88 56.26

R4 0.0 13.06 0.0 0.0 0.0 53.09 52.73 100

ResUtil (h-l


Max-

Min

Min-

Min MCT GA Tabu SA

R1 0.0 11.25 51.11 40.22 26.72 62.84 10.52 3.66

R2 0.0 30.31 51.68 33.71 98.34 62.57 27.42 36.14

R3 96.29 1.50 99.67 95.89 19.87 100 55.04 41.79

R4 0.0 57.30 51.68 34.43 26.58 32.34 100 100

ResUtil (h-h


Max-

Min

Min-

Min MCT GA Tabu SA

R1 80.66 3.7 66.25 33.72 33.98 71.05 16.95 16.75

R2 0.0 21.11 88.52 83.33 85.86 100 19.62 41.07

R3 0.0 4.81 66.72 22.15 70.9 64.62 33.59 18.63

R4 0.0 11.24 65.71 23.36 43.97 69.63 100 100 Table 7 Performance comparison of scheduling algorithms using individual resource utilization for consistent

resources for 128 x 4 resource matrixes.



ResUtil (low

heterogeneity

tasks) MET OLB

Max-

Min

Min-

Min MCT GA Tabu SA

R1 3.02 16.69 32.16 5.80 3.91 89.02 67.88 80.12

R2 3.40 16.01 15.48 4.12 3.20 100 97.27 96.89

R3 3.34 19.26 18.44 3.66 3.86 98.72 100 95.94

R4 5.52 8.00 59.25 17.13 14.95 91.34 94.24 100

ResUtil (high

heterogeneity

tasks) MET OLB

Max-

Min

Min-

Min MCT GA Tabu SA

R1 19.91 54.95 43.22 19.11 57.97 35.41 5.08 30.11

R2 9.71 8.71 43.03 24.53 20.34 91.46 6.67 100

R3 63.69 28.87 45.92 8.34 91.21 55.30 32.45 52.05

R4 2.35 5.71 99.17 23.94 27.67 100 100 12.10 Table 8 Performance comparison of scheduling algorithms using individual resource utilization for

inconsistent resources for 128 x 4 resource matrixes.

Fig. 1 : Comparison of scheduling algorithms using consistent

resources and makespan for low heterogeneity tasks and low

heterogeneity resources


resources and resource utilization for low heterogeneity tasks

and low heterogeneity resources


resources and cost for low heterogeneity tasks and low



resources and profit for low heterogeneity tasks and low





resources and makespan for low heterogeneity tasks and high

heterogeneity

resources


resources and resource utilization for low heterogeneity tasks

and high heterogeneity resources


resources and cost for low heterogeneity tasks and high



resources and profit for low heterogeneity tasks and high



resources and makespan for high heterogeneity tasks and low


010203040506070

12

8,4

25

6,8

51

2,1

6

10

24

,32

R

e

s

U

t

l

%

Resource Matrix

MET

OLB

Max - Min

Min - Min

MCT

GA

TABU

SA




resources and resource utilization for high heterogeneity tasks

and low heterogeneity resources

0500000

1000000150000020000002500000

C

o

s

t

Resource Matrix

MET

OLB

Max -MinMin -MinMCT

GA

Fig. 11: Comparison of scheduling algorithms using consistent

resources and cost for high heterogeneity tasks and low


4 CONCLUSION

Scheduling is one of the important challenges in

grid computing environment as resources are

geographically distributed over multiple administrative

domains. So, there is a need to find a good schedule,

automate the scheduling process and to build a flexible

and scalable scheduling mechanism. This paper has

given a survey on grid scheduling algorithms

implemented using gridsim. Performance measures

such as makespan, resource utilization, cost and profit

are used for comparing the algorithms. The limitations

of scheduling algorithms has been clearly depicted by

individual resource utilization. From the results, it is

seen that the resources are not utilized in a fair manner.

Some resources are not at all utilized, whereas some

resources are utilized to the maximum. Rescheduling

of the tasks to the resources may result in better

makespan.

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