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© 2015, IJCSE All Rights Reserved 46
International Journal of Computer Sciences and EngineeringInternational Journal of Computer Sciences and EngineeringInternational Journal of Computer Sciences and EngineeringInternational Journal of Computer Sciences and Engineering Open Access
Research Paper Volume-3, Issue-8 E-ISSN: 2347-2693
Performance Evaluation of FCFS and EBF in Linear and Non-
Linear Gridlet Size
Neha Bhardwaj1*
, Karambir2
, Ajay Jangra3
1*,2,3
Department of Computer Engineering
UIET, Kurukshetra University, Haryana, India
www.ijcseonline.org
Received: Jul /17/2015 Revised: Jul/30/2015 Accepted: Aug/25/2015 Published: Aug/30/ 2015
Abstract— Grid computing define as the infrastructure in which hardware as well as software resources situated at different places;
shared and uses by the different organizations which coordinated to provide consistent, pervasive and transparent access. Workflow is
a set of task or subtasks having dependency among them. Resource allocation is one the objective of grid computing. Efficiently use of
resources to run the workflow tasks in order to achieve maximum utilization of resources. Throughput is amount of information
process in given amount of time. This parameter is mainly applied to various phenomenon’s of networking systems. In this paper, first
come first serve and easy backfilling algorithm performance evaluated on the basis of linear and non-linear increase in gridlet size and
compare the result in both the cases. The results indicate that EBF has better resource utilization and throughput than FCFS.
Keywords— Grid Computing; Workflow; Resource Utilization; Throughput.
I. INTRODUCTION
Grid computing is an IT infrastructure which changes
computation and communication. Depending on the service
various types of grid are present some of them are access
grid, computational grid, data grid etc [1]. Computational
Grids came into existence to solve the problem which
require large amount of data. But there is difficulty that
demand of computational power increases. Companies and
administrations don’t use their resources to their full
utilization. The main purpose of computational grid is
sharing of computational resources. e-Science Grids are used
to find the solution of the problem arise in the field of
science and engineering by accessing computational and
data resource. Data grids manage the large amount of
distributed data and handle its sharing and access of data.
Data Grid provides the functionality of data repository.
Replication algorithm has very important to improve the
performance of grid. Also, To achieve the high throughput
data copy and transfer is important. Enterprise Grids in
today’s era Grid computing is becoming a popular
component of business as well. E- business easily respond to
the demand of consumer and dynamically adjust the
marketplace shifts [2].
In movies, like in spiderman there is many special effects
which require huge amount of computation or CPU cycles.
Requirement of huge amount of computation not only
limited to the animation or special effects it’s also require in
the calculation of weather forecasting [3]. Grid computing is
not a technology which has been from scratch but it is
developed from the existing technology such as peer to peer,
high performance, cluster computing, web service
computing.
In fig.1, cluster computing and P-2-P has been evolved from
distributed computing and high performance computing
respectively. In cluster computing, different resources like
server, machine etc. is connected for high performance
computing. Parallel Programs for cluster computing has
been written through MPI and PVM. In P-2-P, resources are
shared by the peer computer or machines. Unstructured Peer
to- Peer example is Guntella in which
information are stored in heartbeat manner. Structured Peer-
to- Peer uses mesh, ring [3]. Cluster and P2P computing on
combination give the grid computing which is accepted as
IT virtualization technology. In conceptual point of view,
grid combines the main points of both cluster and P2P
computing and web services [3].
Fig.1 Evolution of Grid Computing [3]
Resource management is one of the challenge in grid which
arises due to heterogeneity of resources and diversity of
resources having different hardware and software [4]. A
common problem arising in grid computing is to select the

International Journal of Computer Sciences and Engineering Vol.-3(8), PP(46-49) Aug 2015, E-ISSN: 2347-2693
most efficient resource to run particular program. Resource
allocation has three phases:
• Resource discovery is the first phase in which candidate
resources are selected from all the resources on the basis
of resources information in the Grid Information services
[5]. Resource registry handle and store the information of
resources [6]. This phase include 3 steps: Authorization
filtering, Application definition, Minimal application
requirement.
• Second phase is system selection in which the resources
which is best for the task execution are selected [7]. This
phase includes 2 steps: Information gathering, System
selection.
• Third phase is execution phase in which execution of job
on selected resources in second phase and also monitor
the execution [8]. This phase includes 6 steps: Advance
reservation, Job submission, Preparation tasks,
Monitoring progress, Job completion, Clean-up Tasks.
II. RELATED WORK
In [9], resource allocation is challenge in the grid
computing and proposed a dynamic allocation mechanism
of resources. The dynamic allocation can be achieved by
merging the concept of best fit algorithm and process
migration.
In [10], proposed PRAG (preemptive resource allocation
technique). By advance reservation and time limit addresses
the problem occur in preemptive technique. Job scheduling
was performed on the basis of advance reservation request
and immediate reservation. This technique reduce the total
time required in execution of job, waiting time and increase
the resource utilization and maintain the load.
In [11], Grid system used in finding planet movies and in
understanding the disease. Grid system als makes a
remarkable job in business and in future develops method
for service delivery. Question arises is What impact of
resource allocation methodology on resource utilization and
its performance?. Study allocation method by its
characteristics and its effect on parameter i.e. resource
utilization.
In [12], various method of security have in developed in for
distributed and cluster computing having homogeneous.
Bur, in heterogeneous we need complete attention of
computation, resource allocation and security. In grid every
resources have their own protocols and policy hence it
provides various opportunities in resource sharing,
utilization. Proposed various security mechanisms at
different levels like resource level, service level,
authentication level, information level and management
level along with the consideration of load factor.
III. RESOURCE UTILIZATION AND THROUGHPUT
Grid is a parallel and distributed computing that applies
resources which are placed at different places within a
network in order to solve complex scientific problems that
require large amount processing cycles. Resources are
allocated to resources in such a way so that resource
utilization is maximum. Resource allocation is one the
objective of grid computing. Efficiently use of resources to
run the workflow tasks in order to achieve maximum
utilization of resources. Sometimes resources are required
to reserve in advance to implement the task of workflow.
Throughput is amount of information process in given
amount of time. This parameter is mainly applied to various
phenomenon’s of networking systems. Various measures
that are related to it some of them is speed through which
workload has been executed; response time i.e. whole time
between single interactive user request and response.
IV. RESULT AND ANALYSIS
Simulation is the process of showing the real world
application behavior which cannot be possible to simulate
in real world like grid computing, networking. GridSim
simulation toolkit is used for result purpose.
Performance is evaluated of the FCFS and EBF algorithm
on different gridlet size. In linear, size of gridlet varies from
1000 to 5000. In non-linear size of gridlet vary from 1000,
3000,6000,10000 and 17000.
• Linearly Increase in Gridlet Size
Fig.1 Average Utilization in FCFS based Policy with Various Gridlet Size
Results clearly indicate that efficient backfill is better than
first come first serve policy for workflow based
optimization in grid systems. Average resource utilization is
improved in case of backfilling policy and increase as the
gridlet size increase.
Fig.2 Comparison of Throughput FCFS and EBF

Results of fig.2 shows that the throughput is maximize in
case of EBF from gridlet to 1000 to 5000 for workflow
based optimization in grid system. Easy backfilling has
better throughput than FCFS. Throughput is improved in
case of backfilling policy and increase as the gridlet size
increase.
• Non-Linearly Increase in Gridlet Size
Fig.3 Comparison of Average Utilization in FCFS and EBF in non-linear
Results in fig.3 clearly indicates that in case of non-linear
increase of gridlet size easy backfill is better than first come
first serve policy for workflow based optimization in grid
systems. Average resource utilization is improved in case of
backfilling policy.
Fig.4 Comparison of throughput in FCFS and EBF based Policy in non-
linear Gridlet Size
Results of fig.4 shows that the throughput is maximize in
case of EBF in different gridlet size for workflow based
optimization in grid system. Easy backfilling has better
throughput than FCFS.
Overall size of gridlet increases linearly or non-linearly
EBF is better than FCFS in terms of both resource
utilization and throughput parameter.
V. CONCLUSION AND FUTURE WORK
Grid computing is an infrastructure for which changes the
way of communication, collaboration and computation.
First come first serve and easy backfilling algorithm
performance evaluated on the basis resource utilization and
throughput of linear and non-linear increase in gridlet size.
Results in both cases linear and non-linear compare and
indicates that EBF is better than FCFS in both resource
utilization and throughput parameter. In future work, can
add more parameter such load balancing.
REFERENCES
[1] D. B. Skillicorn, “Motivating Computational Grids”, 2nd
IEEE/ACM International Symposium on Cluster Computing
and the Grid, Page No (401–406), May 2002.
[2] F. Xhafa and A. Abraham, "Meta-Heuristics for Grid
Scheduling Problems", Metaheuristics for Scheduling in
Distributed Computing Environments, Springer-Verlag Berlin
Heidelberg, Page No (1-37), 2008.
[3] A. Chakrabarti, “Grid Computing Security”, Springer-Verlag
Berlin Heidelberg, 2007.
[4] I. Foster, “The physiology of the grid. Grid computing:
making the global infrastructure a reality”, Page No (217–
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[5] E. Elmroth and J. Tordsson, “Grid resource brokering
algorithms enabling advance reservations and resource
selection based on performance predictions”, Future
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(585-593), 2008.
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[7] N. Malarvizhi and V. R. Uthariaraj, “A Broker-Based
Approach to Resource Discovery and Selection in Grid
Environments”, International Conference on Computer and
Electrical Engineering, 2008.
[8] B. Schnizler, “Resource Allocation in the Grid: A Market
Engineering Approach”, Univ.-Verl. Karlsruhe, 2007.
[9] Ismail and Leila, "Dynamic Resource Allocation Mechanisms
for Grid Computing Environment", 3rd
International
Conference on Testbeds and Research Infrastructure for the
Development of Networks and Communities, Page No (1-5),
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[10] K. Srikala, and S. Ramachandram, "Pre-emptive Resource
Allocation in Grid Computing (PRAG)", IEEE Confernce on

Information & Communication Technologies, Page No (240-
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[11] Krawczyk, Stefan, and K. Bubendorfer, "Grid Resource
Allocation: Allocation Mechanisms and Utilisation Patterns",
Proceedings of the sixth Australasian workshop on Grid
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[12] J.C Patni, P. Rastogi, V.K Jayant, M.S. Aswal, “Methods and
mechanisms of security in Grid Computing”, 2nd
IEEE
International Conference on Computing for Sustainable
Global Development, Page No (1040-1043), 2015.

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