A STUDY ON JOB SCHEDULING IN CLOUD ENVIRONMENT

www.ijiarec.com
Author for Correspondence:
*1
Mr.B.Uvaraja, Department of CSE, Nandha Engineering College, Erode, Tamilnadu, India.
E-mail:bsku.cse@gmail.com.
*2
Dr.N.Shanthi, Professor &Dean, Department of CSE, Nandha Engineering College, Erode, Tamilnadu, India.
E-mail:shanthimoorthi@yahoo.com
SEP-2014
International Journal of Intellectual Advancements
and Research in Engineering Computations
A STUDY ON JOB SCHEDULING IN CLOUD ENVIRONMENT
*1
Mr.B.Uvaraja, *2
Dr.N.Shanthi
ABSTRACT
Cloud computing provides reliable, customized and dynamic services using very large scalable and
virtualized resources over the Internet. Due to novelty of cloud computing field, there is no standard task scheduling
algorithm used in cloud environment. Especially that in cloud, there is a high communication cost that prevents well
known task schedulers to be applied in large scale distributed environment. Some intensive researches have been
done in the area of job scheduling of cloud computing. The scheduling algorithms should order the jobs in a way
where balance between improving the performance and quality of service and at the same time maintaining the
efficiency and fairness among the jobs. This paper aims at studying various scheduling algorithms recently proposed
in cloud computing.
Index terms: Cloud Computing, Job Scheduling, Scheduling Algorithm.
I INTRODUCTION
Cloud computing is Distributed Computing
paradigm which provides services to the customers.
Cloud Providers provides services to their
customers and charges as per usage by particular
customer. That is, use as much or less you want to
use, use services when you want to use and pay for
only what you have used. Cloud computing is a
construct that allows you to use applications that
actually reside on a location different from your
machine location. The cloud environment provides
a different virtualized platform that helps user to
accomplish their jobs with minimum completion
time and minimum costs. Figure 1 shows the
framework of cloud. In the cloud computing
model, computing power, software, storage
services, and platforms are delivered on demand to
external customers over the internet. Cloud makes
it possible for users to use services provided by
cloud providers from anywhere at any time. The
high growth in virtualization and cloud computing
technologies reflect the number of jobs.
ISSN:2348-2079

108
B.Uvaraja. et al., Inter. J. Int. Adv. & Res. In Engg. Comp., Vol.–02 (04) 2014 [107-114]
Copyrights © International Journal of Intellectual Advancements and Research in Engineering Computations, www.ijiarec.com
that are increasing nowadays, require the services
of the virtual machine. Different types of job
scheduling algorithms have been applied on
different types of data workloads. And results are
measured with different performance parameters to
evaluate the performance. Job-scheduling
algorithms are developed to accomplish several
goals like expected outcome, efficient use of
resources, low makespan, high throughput, better
quality of service, maintaining efficiency. In job
scheduling algorithms, priority of jobs is a
challenging issue because some jobs need to be
serviced first than those other jobs which can stay
for a long time. Suitable job scheduling algorithm
must consider the priority of a job [1]. In Fig 1,
Cloud computing architecture is presented. Cloud
services are divided into three types namely,
Infrastructure as a Service (IaaS), Platform as a
Service (PaaS) and Software as a Service (SaaS)
respectively. Fig 1 shows the essential
characteristics of cloud computing such as resource
po oling, broad network access,
elasticity, on-demand services, physical cloud
resources (System Level) and middleware
capabilities form the basis provider of delivering
IaaS and PaaS in the form of a collection of
transparently data centres and runtime environment
and composition tools which ease the creation,
deployment and execution process of application in
the cloud. Finally, to provide the above mentioned
services, deployment models such as Public Cloud,
Private Cloud, Hybrid Cloud and Community
Cloud are used by the cloud providers. The
infrastructure of the cloud is provided publicly to
all the general public by the organization in public
cloud. Anyone can access services from anywhere
publicly. Where, private cloud is used for a single
organization only. Community Cloud is formed by
several organizations and supports a specific
community that has shared concerns for their future
use. It might be managed by the any one of the
shared organization or a third party organization.
Last type is Hybrid Cloud, is a cloud formed by the
composition of two or more clouds that is private,
community, or public. Hybrid computing is bound
together by standardized technology which enables
data and application portability.
II PROBLEM AND ANALYSIS
A Scheduling model based on minimum
network delay using Suffrage Heuristic coupled
with Genetic algorithms for scheduling sets of
independent jobs algorithm is proposed, the
objective is to minimize the makespan.[4].A
heuristic for genetic algorithm based task
scheduling in multiprocessor systems by
choosing the eligible processor on educated
guess. From comparison it is found that this new
heuristic based GA takes less computation time
to reach the suboptimal solution[6]. One primary
issue associated with the efficient and effective
utilization of mobile resources in a mobile grid is
scheduling of tasks. A task scheduling algorithm
is proposed based on the dynamic prediction of
resource mobility and battery power in the
mobile grid environment[7].The goal of the job
scheduler is to maximize the resource utilization
and minimize the processing time of the jobs.
Existing approaches of Grid scheduling doesn’t
give much emphasis on the performance of a
Grid scheduler in processing time parameter.
Schedulers allocate resources to the jobs to be
executed using the First come First serve
algorithm[8].Task scheduling problems are
premier which relate to the efficiency of the
whole cloud computing facilities. Task
scheduling algorithm is an NP- completeness
problem which play key role in cloud
computing[9]. Ant Colony Optimization (ACO)
is random optimization search approach that will
be used for allocating the incoming jobs to the
virtual machines. The main contribution of our
work is to balance the system load while trying
to minimizing the makespan of a given tasks
set[10]. If a job is of high priority it will have to
wait until and unless a job which is getting
executed gets over. This may lead to a delay
which is definitely going to affect the final result.
So, Hybrid approach for load balancing in virtual
environment using FCFS, RBAC, Round Robin,

109
& Priority queue which are going to reduce the
burden of the executor[13]. we present a
technique that enables existing middleware to
fairly manage mixed workloads: long running
jobs and transactional applications. Our
technique permits collocation of the workload
types on the same physical hardware, and
leverages virtualization control mechanisms to
perform online system reconfiguration[14]. In
hybrid clouds, jobs can be allocated on either a
private cloud or a public cloud on a pay per use
basis. The capacity of the communication
channels connecting these two types of resources
impacts the makespan and the cost of workflow
execution[15]. A priority-based method to
consolidate parallel workloads in the cloud. We
leverage virtualization technologies to partition
the computing capacity of each node into two
tiers, the foreground virtual machine (VM) tier
(with high CPU priority) and the background
VM tier (with low CPU priority). We provide
scheduling algorithms for parallel jobs to make
efficient use of the two tier VMs to improve the
responsiveness of these jobs[16]. a scheduling
technique formulti-job MapReduce workloads
that is able to dynamically build
performance models of the executing workloads,
and then use these models for scheduling
purposes. This ability is leveraged to adaptively
manage workload performance while observing
and taking advantage of the particulars of the
execution environment of modern data analytics
applications, such as hardware heterogeneity and
distributed storage[17]. We first model the
selfish behavior of the users supplying resources
and aiming to maximize their own benefits, and
compute the performance of the resulting non-
cooperative equilibrium, which is highly
inefficient. We then augment the existing job
allocation schemes currently implemented in
social cloud systems with a novel class of
incentive mechanisms based on reputation-based
pricing and collective punishment schemes that
compel suppliers to change their selfish strategies
in a manner that improves the efficiency of the
system[22].
III MECHANISM AND SOLUTION
SCHEDULING
In multiprogramming systems, when there
is more than one run able process, the operating
system must decide which one to activate. The
decision is made by the part of the operating
system called the scheduler, using a scheduling
algorithm. In the beginning there was no need for
scheduling, since the users of computers lined up in
front of the computer room or gave their job to an
operator.
BATCH PROCESSING
The jobs were executed in first come first
served manner.
MULTIPROGRAMMING
The scheduler is concerned with deciding
policy, not providing a mechanism. Scheduling
refers to a set of policies and mechanisms to
control the order of work to be performed by a
computer system. Of all the resources in a
computer system that are scheduled before use, the
CPU is by far the most important.
Multiprogramming is the (efficient) scheduling of
the CPU. The basic idea is to keep the CPU busy as
much as possible by executing a (user) process
until it must wait for an event, and then switch to
another process. Processes alternate between

110
consuming CPU cycles(CPU-burst) and performing I/O (I/O-burst).
STAGES OF SCHEDULING
In general, (job) scheduling is performed
in three stages: short-term, medium-term, and long-
term. The activity frequency of these stages are
implied by their names. Long-term (job) scheduling
is done when a new process is created. It initiates
processes and so controls the degree of multi-
programming (number of processes in memory).
Medium-term scheduling involves suspending or
resuming processes by swapping (rolling) themout
of or into memory. Short-term (process or CPU)
scheduling occurs most frequently and decides
which process to execute next.
COMPARISON OF THE EXISTING LITERATURE AND OUR WORK
System
architecture
Server-
client
P2P P2P P2P
Resource
management
Centralized Distributed Distributed Distributed
Supplier Obedient Cooperative
Self-
interested
Self-
interested
Incentive
Design
No No Yes Yes
User
interaction
N/A One-shot One-shot Repeated
System
optimization
Yes No No Yes
Monitoring
Requirements
N/A
Individual
Behavior
Individual
Behavior
Collective
Behavior

111
COMPARISION
Scheduling Algorithm
Scheduling
Method
Scheduling
Parameter
Scheduling
Factor
Findings Environment
Resource-Aware-
Scheduling algorithm (RASA) Batch Mode Make Span
Grouped
task
1. It is used to reduce
makes pan
Grid
environment
RSDC (Reliable
Scheduling DistributedIn
Cloud Computing)
Batch Mode
Processing
time
Grouped task
1. It is used to reduce
processing time.
2. It is efficient for load
balancing.
Cloud
environment
An Optimal Model
for Priority based Service
Scheduling Policy for Cloud
Computing
Batch Mode
Quality of
Service,
Service
request time
An array of
workflow
instances
1. High QoS
2.High throughput
Cloud
environment
A Priority based
Job Scheduling Algorithm in
Cloud Computing
Dependency
mode
Priority to
each
Queue
An array of
job
Queue
1. Less finish time
Cloud
environment
Extended Max-Min
Scheduling Using Petri Net and
Load Balancing
Batch Mode
Load
balancing,
Finish time
Grouped
Task
1.It is used for Efficient
load balancing.
2. Petrin net is used to
remove Limitation of
max-min algorithm.
Cloud
environment
An Optimistic Differentiated
Job Scheduling System for
Cloud Computing
Dependency
mode
Quality of
service,
Maximum
profit
Single Job
with
Multiple user
1. The Qos Requirements
of the cloud computing
user and the maximum
profits of the cloud
computing service
provider are achieved.
Cloud
environment
Improved Cost-Based
Algorithm for Task
Scheduling
Batch mode
Cost,
Performance
Unscheduled
task
Group
1.Measures both
resource cost and
computation
performance.
2. Improves the
computation
communication ratio.
Cloud
environment
Performance and
Cost evaluation of Gang
Scheduling in a Cloud
Computing System with Job
Migrations and Starvation
Handling
Batch mode
Performance,
Cost
Workflow
With large
Number of
job
1. The application of
migrations and starvation
handling had a significant
effect on the model.
2. It improves
performance.
Cloud
environment

112
SCHEDULING ALGORITHM
First Come First Serve Algorithm:
Jobs are in the queue. so, which come first
is first served. This algorithm is simple and fast.
Shortest Job First(SJF):
Which job is execution time is very less,
that job is first served.
Round Robin algorithm:
In the round robin scheduling, processes are
given a limited amount of CPU time called a time-
slice or a quantum in FIFO manner. If a process does
not complete execution before its CPU-time expires,
the CPU is pre-empted and given to the next process
waiting in a queue. And the pre-empted process is
placed at the end of the ready queue.
Min–Min algorithm:
This algorithm chooses small jobs to be
executed firstly, which in turn large jobs delays for
long time.
Max – Min algorithm:
This algorithm chooses large jobs to be
executed firstly, which in turn small jobs delays for
long time
Random Algorithm
In random algorithm, the selected jobs are
randomly selected for execution and assigned to
Virtual Machine. The algorithm does not take into
considerations the status of the Virtual Machine,
which will either be under heavy or low load.
Most fit task scheduling algorithm:
In this algorithm task which fit best in
queue are executed first. This algorithm has high
failure ratio.
Priority scheduling algorithm:
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.
IV CONCLUSION
Scheduling is one of the most important
task in cloud computing environment. In this paper
we have analyze various scheduling algorithm,
namely Short Job Scheduling, Priority based Job
Scheduling Algorithm, and Enhanced Max-min Task
Scheduling Algorithm have been studied and
analyzed. Based on their own experimental result, it
is shown that some of the scheduling algorithms are
beneficial to be used in Cloud computing. There is
not a single scheduling algorithm which can solve
problem of various types of quality services.
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A STUDY ON JOB SCHEDULING IN CLOUD ENVIRONMENT

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