OPTIMIZED RESOURCE PROVISIONING METHOD FOR COMPUTATIONAL GRID

International Journal of Grid Computing & Applications (IJGCA) Vol.7, No.1, March 2016
DOI:10.5121/ijgca.2016.7102 7
OPTIMIZED RESOURCE PROVISIONING METHOD
FOR COMPUTATIONAL GRID
P. Suresh and N. V. Keerthana
Department of Information Technology, Kongu Engineering College, Anna University,
Tamil Nadu, India – 638 052
ABSTRACT
Grid computing is an accumulation of heterogeneous, dynamic resources from multiple administrative
areas which are geographically distributed that can be utilized to reach a mutual end. Development of
resource provisioning-based scheduling in large-scale distributed environments like grid computing brings
in new requirement challenges that are not being believed in traditional distributed computing
environments. Computational grid is applying the resources of many systems in a network to a single problem
at the same time. Grid scheduling is the method by which work specified by some means is assigned to the
resources that complete the work in the environment which cannot fulfill the user requirements considerably.
The satisfaction of users while providing the resources might increase the beneficiary level of resource
suppliers. Resource scheduling has to satisfy the multiple constraints specified by the user. The option of
resource with the satisfaction of multiple constraints is the most tedious process. This trouble is solved by
bringing out the particle swarm optimization based heuristic scheduling algorithm which attempts to select
the most suitable resource from the set of available resources. The primary parameters that are taken in
this work for selecting the most suitable resource are the makespan and cost. The experimental result
shows that the proposed method yields optimal scheduling with the atonement of all user requirements.
KEYWORDS
Grid computing, Resource scheduling, HPSO
1.INTRODUCTION
Grid computing has come off as a computing platform to provide a vast amount of resource
sharing to large-scale scientific applications. Computational grids can be set as an environment
that organizes geographically distributed and heterogeneous resources in different administrative
areas with different security policies into a single computing system. Due to heterogeneous and
dynamic nature of the resources in the grid, resources are inexorably unreliable, which induces a
great effect on scheduling [6]. So, grid resource management has become one of the most
important key concerns in the field of grid computing [7, 8]. A set of heterogeneous services is
utilized in order to create the grid and, in addition, these savings are not done by a single soul but
by different administrators in each of the systems. For the efficient procedure of a computational
grid, several factors must be considered as load balancing, resource sharing and effective
programming.
Grid resource management can be delineated as a process consisting identification of the
prerequisites of the resources, matching resources to the applications followed by resource
allocation and scheduling as well as monitoring the grid resources finally to run grid applications
as efficiently as possible [5]. A grid resource management scheme is commanded to guide
resource management decisions which include resource provisioning and scheduling by providing
Quality of Service (QoS) metrics delivered to the users. These metrics introduce a number of
challenging issues that need to be addressed such as resource provisioning-based resource
scheduling and ensuring the scheduling of jobs on the trustworthy nodes [4]. Grid user often has a

8
limited control over grid resources and the resource handler is not always able to fulfill all the
requirements due to the large number of resources as well as user’s requests.
Grid scheduling is determined as the operation of making scheduling decisions involving
allocation of tasks to resources over multiple administrative domains [4]. The end of this
procedure is to maximize various optimization criteria such as machine usage, fairness and flow
time or to guarantee non trivial QoS. Scheduling process should be flexible and fast so that it is
capable to efficiently respond to dynamic changes in the grid environment (job arrival, failure,
imprecise job runtime estimate, etc.). Effective scheduling in the grid environment is a complex
problem which is not fully and efficiently solved in now a day’s production system.
Consequently, a sound approach is to search for suboptimal solutions, where sufficiently efficient
algorithms exist. Nonetheless, production schedulers usually use very simple algorithms based on
priority queues [8]. Referable to the limited and often imprecise information used by these
algorithms their efficiency are frequently very depressed, especially when sufficient QoS is
needed.
Heuristic methods often help to solve NP-complete problems [4]. Heuristic approaches can easily
be used to grid scheduling problems because grid scheduling consists of various important issues
such as heterogeneity of resources, the dynamic and independent nature of grid resources and
finally the issue of different policies being followed by the resource providers and resource
consumers for execution of their projects. The primary motif of this study is to propose a hyper-
heuristic-based scheduling algorithm being able to be applied in grid environment and hence
scheduling resources to the preferred jobs leading to the delivery of optimum results to the grid
users.
Our contributions in this theme are: 1) Creation of Grid environment. 2) Workload and resource
monitoring. 3) Implementation of multi-constraints strategy. 4) Heuristic Particle Swarm
Optimization scheduling algorithm. 5) Result analysis.
2.RELATED WORK
Resource provisioning and scheduling are major things of computational grids and assist it
achieve high performance in its execution environment. Referable to the heterogeneous and
dynamic nature of grid resources, this basically has taken the strain of a large-scale optimization
problem.
Hongbo Liu et al. [2] proposed a Fuzzy Particle Swarm Optimization (FPSO) for scheduling the
job. This optimization algorithm dynamically generates an optimal schedule so has to complete
the jobs within a minimal period of time as well as utilizing the resource in an effective fashion.
The location and the velocity of jobs are represented in fuzzy matrices. The dynamic availability
of resources and the cost of information transmission are not counted. Then it allocates more
memory due to static allocation of resources.
Hesam Izakian et al. [1] represented a Novel Particle Swarm Optimization (NPSO) algorithm for
grid scheduling. This method minimizes the makespan and flowtime simultaneously by regularly
updating the resources status in the grid information system. Thereby it yields a more beneficial
result than FPSO by dynamically scheduling the tasks in the distributed grid environment. This
method does not deal with the constraints such as user expected cost and protection.
Rajni Aron et al. [3] constructed a resource provisioning framework based on QoS parameters. A
resource provisioning policy based on QoS parameters is required for efficient grid resource

9
provisioning. The motivation of this method stems for the challenges in managing and an
efficient utilization of grid resources. The constraints, including QoS satisfaction, minimum
price and minimum implementation time are addressed by this method. The performance of the
grid is better by believing the median response time as a metric that is not taken in this fabric.
In grid computing, job scheduling is a complex undertaking. A good scheduling system must
optimize QoS parameters such as price, utilization time and protection. The choice of services with
minimal cost and protection is complex and finding the best pair of jobs and resources is an NP-
complete problem [4]. To surmount these challenges, several methods for programming are
introduced in the existing schemes. But they bear some problems like dynamic allocation of
resources and efficiency of the organization, less implementation time, load balancing, minimum
price and minimum makespan which are required to be seen in the projected scheme. Our proposed
concept of hyper-heuristic-based resource scheduling algorithm minimizes the cost and makespan
simultaneously. On the other hand, particle swarm-based heuristic has not been utilized for
resource scheduling in the grid earlier and we are providing a new way while designing our
resource scheduling algorithm.
3.PROPOSED WORK
3.1.Problem Definition
Grid resources are heterogeneous and dynamic in nature and the concept of job scheduling is the
tedious task. Thus, an efficient scheduling should be packed away. Scheduling system must
optimize QoS parameters such as price, usage time, security and makespan. Choice for inspection
and repairs with minimal cost and security is more complex in the gridiron. To formulate the
problem, the set of n resources are considered as {r1, r2 … rn} and the set of m jobs are considered
as {j1, j2 … jm}. The jobs are considered as atomic and independent and can be executed in
parallel with other available jobs. Scheduling function is specified as a mapping of tasks to
resources as: S = J→R.
3.2.Creation of Grid Environment
The main elements of grid environment are grid user, grid resources, resource broker and GIS.
Initially, the grid user interacts with the resource broker and sends their task to compute. Thus,
the number of gridlets (jobs) is created which is received from the user. It holds all the
information about jobs like length of the job, file size and output size. Then resource broker
discovers the resources for scheduling strategies and job processing. Thus, the number of
heterogeneous resources with dynamically varying cost, Central Processing Unit (CPU) available,
CPU speed and memory available are created and those resources are registered on to GIS, where
GIS worked as an agent and collect all the relevant data about the resources.
In the proposed system, heterogeneous grid resources are considered for the allocation. The set of
tasks would be garnered from the user along with their constraints. The multi-constraints gather
from the user which in terms led to the successive execution of the set of jobs. Founded on these
requirements job scheduling would be executed. Grid scheduling being a two-step process as
follows: 1) Identifies the required set of resources as per the user requests. 2) Maps the jobs to the
actual circle of resources. So the scheduling ensures a near-optimal satisfaction of QoS
parameters. Consequently, the user now selects the most appropriate resources among all the
resources shown that matches the user’s budget and the other specifications
.

10
3.3.Multi-Constraints Strategy
In grid scheduling, the principal destination of the resource providers is to minimize the
makespan whereas the destination of the user is to downplay the monetary value for power
system application. Fitness function [4] is calculated as:
(1)
where 0 ≤ ϴ < 1 and 0 ≤ δ < 1 are weights to prioritize components of the fitness function. The
price of the job which executes on the resource and makespan is the completion time of the latest
job and it is illustrated as:
(2)
(3)
where Fj is the finishing time of the latest job.
The completion time of the task must be defined before calculating the makespan. Completion
time indicates the fourth dimension in which the resource can complete the execution of all the
tasks. The completion time is the summation of Availability Time (AT) and the Expected Time to
Complete (ETC) is counted as:
(4)
where AT (rk) is the availability time of the resource and ETC (ji,rk) is the expected time to
complete for job ji in the resource rk.
The current load of the resource is calculated by adding all the length of the line of works
submitted to the particular resource with their Million Instructions Per Second (MIPS) rating and
AT of the resource. The rule is illustrated as:
(5)
The resource utilization is the total amount of resources utilized by the user from executing the
jobs in the system. The interpretation of a formula is given as follows:
(6)
The success rate is defined as the percentage of jobs scheduled successfully from the amount of
jobs submitted for scheduling. The formula is given only for illustrative purposes:
(7)
3.4.Heuristic Particle Swarm Optimization (HPSO) Scheduling
Here we present our HPSO scheduling algorithm. Resource provisioning is done with the
thoughtfulness of the multi-constraints strategy evaluation. The user wishes to downplay the cost
whereas the resource provider wishes to minimize the makespan. Smaller values of makespan
indicate that the scheduler is planning the jobs in an efficient way. Another measure is cost,
which brings up to the price of the job execution on the resource along with the security

11
assurance cost. HPSO is based along the biological behavior of the birds which foraging for food.
This is executed with the thoughtfulness of the various tasks that are assembled from the users.
The HPSO algorithm is presented in algorithm 1.
Algorithm 1: HPSO based scheduling Algorithm
Given n - set of resources and m - set of jobs
Initialize the jobs randomly in the waiting line
For i=1 to n do
For each job calculation fitness function for both planetary and local situations
Fitness function = cost + Makespan
If (Fitness global > Fitness local)
Fitness global = Fitness local
Else
Return Fitness local
Allocate the jobs to the resources with minimum Fitness and minimum load
End for
Apply heuristic
While there are unscheduled jobs in the queue do
Get the jobs from the queue
Repeat the process until all jobs are allocated
4.RESULT ANALYSIS
4.1.Environment Creation Setup
Table 1 Environment Creation Setup
Grid Resources User Defined (1-5)
Machines for Each Resource User Defined (1-5)
PEs and MIPS rating for each Resource Random
Speed and Bit Rate for each Resource Random
Price for Each Resource Random
Gridlets User Defined (10-40)
Length and File I/O size for each Gridlet Random
Table 1 expresses the environment creation setup for power system system using GridSim. The
resources and the machines are user-specified. The initial position of each resource is stored in the
data server. Initially, grid user creates 10 gridlets. Job 1 arrives at job scheduler, and then the
scheduler identifies the resource which is desirable for the peculiar task of computing the fitness
function and load of the resource. Granting to the results, jobs are allocated to it. The operation
will go on until all the chores in the queue are scheduled.
4.2.Parameter Evaluation
The accepted value of the simulation is presented in Table 2 and the number of grid lets is
created for analyzing the parameter of the arrangement demonstrated in Table 3.

12
Table 2 Assumed Values for Simulation
No. of
Resources
No. of
Machine
per
Resource
No. of
PEs per
Machine
MIPS per
Machine
Bit Rate per machine
(bps)
5 4 1-5 400-800 102
-103
Table 3 Performance Analysis based on Parameters
No.of Jobs
Resource
Utilization
(%)
User
satisfaction
(%) based
on Cost
Makespan (ms)
10 92 95 133.876
20 94 93.5 171.364
30 96 94.5 187.56
The resource utilization parameter is analyzed in percentage with the number of jobs
like 10, 20 and 30. The usage of the resource increases as the task increases because the
resources are used more efficiently.The user satisfaction is analyzed with a percentage based on the
price of the scheme. In which the user satisfaction slightly varies as the job increases because the
price of the system increases when the resource usage increases.The overall performance of the
system is calculated in millisecond. The makespan increases as the job increase because the
time needed for all the tasks to finish processing take more time.By analyzing these
parameters, the system performance is increased by reducing the response time, overall cost of the
scheme, and the makespan. It also maximizes the resource use rate and throughput with the user
satisfaction. It generates the success rate equals to 1. It implies that all the chores in the queue were
scheduled completion.
5.CONCLUSION AND FUTURE ENHANCEMENTS
The significant problem addressed in the system is resource management, cost of the computing
systems, and makespan of the tasks. To overwhelm the problem, the system integrates load
balancing and optimal scheduling. It is settled by collecting the information of processing speeds,
time, load and cost of the computing resources. Agreeing to this information the scheduler submits
their jobs to the resource that fulfills the anticipated price of the user. The HPSO strategy of the
scheduling system improves the resource usage rate, minimizes the cost of scheduling the jobs,
and the finishing time of the jobs. The proposed system not only downplays the cost, but also
minimizes the makespan.
In future, the ability of a system to perform its function correctly even in the presence of
failure of the imaginations. It cuts down the resource availability which tends to produce the
resource unusable and result in the loss of user submitted jobs. The active nature of the grid
environment introduces challenging security. Users must be able to produce new services like

13
resources dynamically without administrator permission. These services should coordinate and
interact with other services. Thus, the security should be provided by name the service with
acceptable identity to the user i.e., user credentials.
REFERENCES
[1] Abraham A, Izakian H (2009), ‘A novel particle swarm optimization approach for grid job
scheduling’, Information Systems, Technology and Management, Vol.21, pp.100-109.
[2] Abraham A, Liu H (2006), ‘Scheduling jobs on computational grids using a fuzzy particle swarm
optimization algorithm’, In: Proceedings of 10th
International Conference on Knowledge Based and
Intelligent Information and Engineering Systems, England, pp.500-507.
[3] Aron R, Chana I (2012), ‘Formal QoS Policy Based Grid Resource Provisioning Framework’,
Journal on Grid Computing, Vol.10, No.4, pp.249–264.
[4] Aron R, Chana I, Abraham A (2015), ‘A hyper-heuristic approach for resource provisioning-based
scheduling in grid environment’, Journal on Supercomputing, Vol.71, pp.1427-1450.
[5] Buyya R, Abramson D, Giddy J, Stockinger H (2002), ‘Economic models for resource management
and scheduling in grid computing’, Concurr Comput, Vol.14, pp.1507–1542.
[6] Foster I, Kesselmen c (2004), ‘The Grid: blueprint for a future computing infrastructure’, Morgan
Kaufmann Publishers, USA.
[7] Garg SK, Buyya R, Siegel HJ (2010), ‘Time and cost trade-off management for scheduling parallel
applications on utility grids’, Future Gener Comput System, Vol.8, No.26, pp.1344–1355 (ISSN:
0167–739X, Elsevier Science, Amsterdam, The Netherlands).
[8] Garg S, Konugurthi P, Buyya R (2008), ‘A linear programming driven genetic algorithm for
metascheduling on utility Grids’, In: Proceedings of the 16th International Conference on Advanced
Computing and Communication (ADCOM 2008, IEEE Press, New York, USA). Chennai, India.
[9] He X, Sun X, Laszewski G (2003), ‘A Qos Guided Min-Min Heuristic for Grid Task
Scheduling’, Journal on Computing Science and Technology, Vol.18, No.4, pp.442-451.
[10] Moshen A S, Deldari H, Doori B M (2008), ‘Balancing Load in Computational Grid Applying
Adaptive Intelligent Colonies of Ants’, Journal on Informatica, Vol.32, pp. 327-335.
[11] Visalakshi P, Sivanandam S N (2009), ‘Dynamic Task Scheduling with Load
Balancing using Hybrid particle Swarm Optimization’, International Journal Open
Problems on Computing and Mathematics, Vol.2, No.3, pp. 475-488.
Author
Dr.P.Suresh is an Assistant Professor in Dept. of Information Technology in Kongu
Engineering College, Tamil Nadu, India. His research interests lies in Grid Computing
and C omputer Networking.
Ms. N.V.Keerthana is pursuing M.Tech - Information and Cyber Warfare (Dept. of
Information Technology) in Kongu Engineering College, Erode, Tamil Nadu, India. She
has completed her B.Tech in 2014 from Kongu Engineering College, Tamil Nadu, India.

OPTIMIZED RESOURCE PROVISIONING METHOD FOR COMPUTATIONAL GRID

More Related Content

What's hot (16)

Similar to OPTIMIZED RESOURCE PROVISIONING METHOD FOR COMPUTATIONAL GRID (20)

More from ijgca (20)

Recently uploaded (20)

OPTIMIZED RESOURCE PROVISIONING METHOD FOR COMPUTATIONAL GRID