Job Resource Ratio Based Priority Driven Scheduling in Cloud Computing

IJSRD - International Journal for Scientific Research & Development| Vol. 1, Issue 2, 2013 | ISSN (online): 2321-0613
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Job Resource Ratio Based Priority Driven Scheduling in Cloud
Computing
Pinal Salot1
Purnima Gandhi2
1,2
Alpha College of Engineering, GTU, Gujarat, India
Abstract— Cloud Computing is an emerging technology in
the area of parallel and distributed computing. Clouds
consist of a collection of virtualized resources, which
include both computational and storage facilities that can be
provisioned on demand, depending on the users’ needs. Job
scheduling is one of the major activities performed in all the
computing environments. Cloud computing is one the
upcoming latest technology which is developing drastically.
To efficiently increase the working of cloud computing
environments, job scheduling is one the tasks performed in
order to gain maximum profit. In this paper we proposed a
new scheduling algorithm based on priority and that priority
is based on ratio of job and resource. To calculate priority of
job we use analytical hierarchy process. In this paper we
also compare result with other algorithm like First come first
serve and round robin algorithms.
Key Words: Priority based scheduling, Job scheduling, cloud
computing, optimization algorithm.
I. INTRODUCTION
There has been various types of scheduling algorithm exist
in distributed computing system. Most of them can be
applied in the cloud environment with suitable verifications.
The main advantage of job scheduling algorithm is to
achieve a high performance computing and the best system
throughput. Traditional job scheduling algorithms are not
able to provide scheduling in the cloud environments.
According to a simple classification, job scheduling
algorithms in cloud computing can be categorized into two
main groups; Batch mode heuristic scheduling algorithms
(BMHA) and online mode heuristic algorithms. In BMHA,
Jobs are queued and collected into a set when they arrive in
the system. The scheduling algorithm will start after a fixed
period of time. The main examples of BMHA based
algorithms are; First Come First Served scheduling
algorithm (FCFS), Round Robin scheduling algorithm (RR).
By On-line mode heuristic scheduling algorithm, Jobs are
scheduled when they arrive in the system. Since the cloud
environment is a heterogeneous system and the speed of
each processor varies quickly, the on-line mode heuristic
scheduling algorithms are more appropriate for a cloud
environment. Most fit task scheduling algorithm (MFTF) is
suitable example of On-line mode heuristic scheduling
algorithm.
First Come First Serve Algorithm:1)
Job in the queue which come first is served. This algorithm
is simple and fast.
Round Robin algorithm:2)
In the round robin scheduling, processes are dispatched in a
FIFO manner but are given a limited amount of CPU time
called a time-slice or a quantum. If a process does not
complete before its CPU-time expires, the CPU is
preempted and given to the next process waiting in a queue.
The preempted process is then placed at the back of the
ready list.
Most fit task scheduling algorithm:3)
In this algorithm task which fit best in queue are executed
first. This algorithm has high failure ratio.
Priority scheduling algorithm:4)
The basic idea is straightforward: each process is assigned a
priority, and priority is allowed to run. Equal-Priority
processes are scheduled in FCFS order. The shortest-Job-
First (SJF) algorithm is a special case of general priority
scheduling algorithm. An SJF algorithm is simply a priority
algorithm where the priority is the inverse of the (predicted)
next CPU burst. That is, the longer the CPU burst, the lower
the priority and vice versa. Priority can be defined either
internally or externally. Internally defined priorities use
some measurable quantities or qualities to compute priority
of a process.
Scheduling ProcessA.
Scheduling process in cloud can be generalized into three
stages namely–
 Resource discovering and filtering – Datacentre
Broker discovers the resources present in the network
system and collects status information related to them.
 Resource selection – Target resource is selected
based on certain parameters of task and resource. This is
deciding stage.
 Task submission -Task is submitted to resource
selected.
Fig. 1. Scheduling Process

Job Resource Ratio Based Priority Driven Scheduling in Cloud Computing
(IJSRD/Vol. 1/Issue 2/2013/0077)
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374
II. RELATED WORK
The Following scheduling algorithms are currently prevalent
in clouds.
Resource-Aware-Scheduling algorithm (RASA):A.
Saeed Parsa and Reza Entezari-Maleki [2] proposed a new
task scheduling algorithm RASA. It is composed of two
traditional scheduling algorithms; Max-min and Min-min.
RASA uses the advantages of Max-min and Min-min
algorithms and covers their disadvantages. Though the
deadline of each task, arriving rate of the tasks, cost of the
task execution on each of the resource, cost of the
communication are not considered. The experimental results
show that RASA is outperforms the existing scheduling
algorithms in large scale distributed systems.
RSDC (Reliable Scheduling Distributed in CloudB.
computing):
Arash Ghorbannia Delavar,Mahdi Javanmard , Mehrdad
Barzegar Shabestari and Marjan Khosravi Talebi[1]
proposed a reliable scheduling algorithm in cloud computing
environment. In this algorithm major job is divided to sub
jobs. In order to balance the jobs the request and
acknowledge time are calculated separately. The scheduling
of each job is done by calculating the request and
acknowledges time in the form of a shared job. So that
efficiency of the system is increased.
An Optimal Model for Priority based ServiceC.
Scheduling Policy for Cloud Computing Environment:
Dr. M. Dakshayini, Dr. H. S. Guruprasad [3] proposed a
new scheduling algorithm based on priority and admission
control scheme. In this algorithm priority is assigned to each
admitted queue. Admission of each queue is decided by
calculating tolerable delay and service cost. Advantage of
this algorithm is that this policy with the proposed cloud
architecture has achieved very high (99%) service
completion rate with guaranteed QoS. As this policy
provides the highest precedence for highly paid user service-
requests, overall servicing cost for the cloud also increases.
A Priority based Job Scheduling Algorithm in CloudD.
Computing:
Shamsollah Ghanbari, Mohamed Othman proposed a new
scheduling algorithm based on multi – criteria and multi -
decision priority driven scheduling algorithm. This
scheduling algorithm consist of three level of scheduling:
object level, attribute level and alternate level. In this
algorithm priority can be set by job resource ratio. Then
priority vector can be compared with each queue. This
algorithm has higher throughput and less finish time.
Extended Max-Min Scheduling Using Petri Net andE.
Load Balancing:
El-Sayed T. El-kenawy, Ali Ibraheem El-Desoky, Mohamed
F. Al-rahamawy[5] has proposed a new algorithm based on
impact of RASA algorithm. Improved Max-min algorithm is
based on the expected execution time instead of complete
time as a selection basis. Petri nets are used to model the
concurrent behavior of distributed systems. Max-min
demonstrates achieving schedules with comparable lower
makespan rather than RASA and original Max-min.
An Optimistic Differentiated Job Scheduling System forF.
Cloud Computing:
Shalmali Ambike, Dipti Bhansali, Jaee Kshirsagar, Juhi
Bansiwal[6] has proposed a differentiated scheduling
algorithm with non-preemptive priority queuing model for
activities performed by cloud user in the cloud computing
environment. In this approach one web application
is created to do some activity like one of the file uploading
and downloading then there is need of efficient job
scheduling algorithm. The QoS requirements of the cloud
computing user and the maximum profits of the cloud
computing service provider are achieved with this
algorithm.
Improved Cost-Based Algorithm for Task Scheduling:G.
Mrs.S.Selvarani, Dr.G.Sudha Sadhasivam [7] proposed an
improved cost-based scheduling algorithm for making
efficient mapping of tasks to available resources in cloud.
The improvisation of traditional activity based costing is
proposed by new task scheduling strategy for cloud
environment where there may be no relation between the
overhead application base and the way that different tasks
cause overhead cost of resources in cloud. This scheduling
algorithm divides all user tasks depending on priority of
each task into three different lists. This scheduling algorithm
measures both resource cost and computation performance,
it also Improves the computation/communication ratio.
Performance and Cost evaluation of Gang SchedulingH.
in a Cloud Computing System with Job Migrations and
Starvation Handling:
Ioannis A. Moschakis and Helen D. Karatza has proposed a
gang scheduling algorithm with job migration and starvation
handling in which scheduling parallel jobs, already applied
in the areas of Grid and Cluster computing. The number of
Virtual Machines (VMs) available at any moment is
dynamic and scales according to the demands of the jobs
being serviced. The aforementioned model is studied
through simulation in order to analyze the performance and
overall cost of Gang Scheduling with migrations and
starvation handling. Results highlight that this scheduling
strategy can be effectively deployed on Clouds, and that
cloud platforms can be viable for HPC or high performance
enterprise applications.
III. ANALYTICAL HIERARCHY PROCESS
In this section we explain the Analytical Hierarchy Process
briefly. It is a multi-criteria decision-making (MCDM) and
multi-attribute decision-making (MCDM) model. Basically
architecture of AHP is consisted of three levels which are
objective level, attributes level and alternatives level
respectively. The foundation of AHP is comparison matrix
which can be shown as Eq. (1).
aij = { (1)
Each entry in the matrix A is positive ( ). Also A is a square
matrix ( ). For any arbitrary comparison matrix such as A we
can compute a vector of weights such as associated with A.
Relationship between A and can be shown as Eq.(2)

375
A = { (2)
An essential step in AHP is to calculate vector of weights.
Vector of weights can be computed through the Eq. (3)
(3)
Actually Eq. (3) is denoted the principal eigenvalue of
A and is denoted the corresponding eigenvector. If A is
absolutely consistent then . In this case A will be
consistent. consistency ratio (CR) can be defined as Eq. (4).
CR = (4)
In Eq. (4), RI is denoted the random index(RI), RI can be
calculated randomly based on rank of comparison matrix.
IV. PROPOSED ALGORITHM
Suppose that is a set of jobs that request
resources in a cloud environment. Also we assume that
is a set of resources available in cloud
environment( ). Each job requests a resource with a
determined priority. The priority of each job is compared
with other jobs separately.
Assume that are d comparison matrixes
of jobs which are created according to priority of resource
accessibilities. For each of comparison matrixes we should
compute a priority vector (vector of weights). The priority
vector can be obtained by solving Eq. (3). There are several
methods for calculating priority vector [1- 5]. An iterative
method for solving Eq. (3) can be found in [6].That method
solves the Eq. (3) by using numerical methods. Using
iterative methods can calculate priority vector (vector of
weights) without concerning about consistency problems. In
this case we can define a normal matrix of jobs level as Eq.
(5).
Δ = ] (5)
It is clear that Δ is a matrix with m (the number of jobs)
rows and d (the number of resources) columns. The next
step of the proposed algorithm is to make a comparison
matrix for resources according to priorities. This matrix
determines that which resource has higher priority than
others based on decision maker(s). In this case, we will have
a matrix with d rows and d columns. Assume that S is
comparison matrix for resources level, thus will be defined
as priority vector of S. The next step of the algorithm is to
calculate PVS which is denoted as priority vector of
scheduling jobs. PVS can be calculated by Eq. (6). Finally,
we choose the maximum element of PVS, then select
corresponding element of ᴪ in order to allocate a suitable
resource.
PVS = Δ.ᵧ (6)
 Steps of algorithm :
1) J = set of Jobs
2) C = Set of Resources
3) Make consistent comparison matrix according to
priority of resources
4) Compute priority vector for all matrix
5) Make matrix with priority vector name it ∆.
6) For C compute consistent matrix .
7) Compute priority vector and name it Υ.
8) Compute PVS. PVS = ∆. Υ.
9) Choose a job according to priority value
10) Update list of job according to priority.
V. SIMULATION RESULT
The CloudSim toolkit is used to simulate heterogeneous
resource environment and the communication environment
[4]. CloudSim(2.1.1) simulator is used to verify the
correctness of proposed algorithm. The experiments are
performed with Sequential assignment which is default in
CloudSim and the proposed algorithm. The jobs arrival is
Uniformly Randomly Distributed to get generalized
scenario. The configuration of datacenter created is as
shown below - Number of processing elements – 1 Number
of hosts – 1.
RAM(MB) 2048
Processing Power(MIPS) 1000
VM scheduling Time Shared with Priority
Table 1. Resources of Virtual Machines
The configuration of Virtual Machines used in this
experiment is as shown in Table 2.
Virtual Machines 3
RAM (MB) 256
Processing Power 1000
Processing Element 1
Table 2. Configuration of Virtual Machines
Now we will compare result with existing algorithm with
this configuration. It will take different finish time with
different algorithm. It can be shown by figure 2.
Fig. 2 : Comparison with other algorithm
VI. NUMERIC EXAMPLE
In our example we have 3 resources and 3 jobs. Priority of
each job according to resources can be shown in table.
0 2000400060008000
FCFS
Round Robin
Priority driven
Makespan
Makespan

376
Priority
Resource
1
Resource
2
Resource
3
Priority
Vector
Resource
1
1 0.25 3 0.213
Resource
2
4 1 7 0.701
Resource
3
1/3 1/7 1 0.085
Table 3: Priority of resources
Resource 1 Job 1 Job 2 Job 3 Priority Vector
Job1 1 4 3 0.632
Job 2 1/4 1 ½ 0.137
Job 3 1/3 2 1 0.239
Table 4: Priority of resource 1 according to jobs
Job1 1 5 3 0.619
Job 2 1/5 1 1/4 0.096
Job 3 1/3 4 1 0.284
Table 5: Priority of resource 2
Job1 1 1/3 1/7 0.093
Job 2 3 1 ½ 0.292
Job 3 7 2 1 0.615
Table 6: Priority of resource 3
Comparison matrix can be shown as follows :
[ ]
For other comparison matrixes in table 2 to 4 we should
investigate consistency condition.
According to table 2 to 4 and step 3-5 of proposed algorithm
we have:
[ ]
[ ]
Thus,
[ ]
Job 1 has highest priority then job 3 has 2nd
priority and job
2 has less priority.
VII. CONCLUSION AND FUTURE WORK
Scheduling is one of the most important tasks in cloud
computing environment. In this paper we have analyze
various scheduling algorithm and tabulated various
parameter. Priority is an important issue of job scheduling in
cloud environments. In this paper we have proposed a
priority based job scheduling algorithm which can be
applied in cloud environments. We have named it “PJSC”.
We can get less finish time than any other algorithm. Job
which has maximum priority can be served fast.
The proposed algorithm can be further improved by
considering following suggestions –
 By using this algorithm we can also minimize cost
also this can be future work.
 Analytic hierarchy process is only method so that
we can choose one of the best alternatives from others.
Complexity is also future work for proposed algorithm.
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International Journal of Computer Science, Engineering
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Task Scheduling Algorithm in Grid Environment” in
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Job Resource Ratio Based Priority Driven Scheduling in Cloud Computing

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Job Resource Ratio Based Priority Driven Scheduling in Cloud Computing