The document reviews cloud computing research, focusing on various scheduling algorithms aimed at optimizing resource allocation and energy consumption in cloud environments. It discusses the characteristics of cloud computing, types of distributed systems, and addresses major issues such as performance, security, and bandwidth costs. Additionally, it categorizes and evaluates several existing scheduling algorithms and models, highlighting their advantages and disadvantages in enhancing resource utilization.

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Cloud computing
Review over various scheduling algorithms
Meenakshi Bhagtani
PhD Scholar, University Of Kota, Kota
mm12dec@yahoo.co.in
Abstract: Cloud computing has taken an important
position in the field of research as well as in the
government organisations. Cloud computing uses virtual
network technology to provide computer resources to
the end users as well as to the customer’s. Due to
complex computing environment the use of high logics
and task scheduler algorithms are increase which results
in costly operation of cloud network. Researchers are
attempting to build such kind of job scheduling algorithms
that are compatible and applicable in cloud computing
environment.In this paper, we review research work which is
recently proposed by researchers on the base of energy saving
scheduling techniques. We also studying various scheduling
algorithms and issues related to them in cloud computing.
Keywords: Cloud computing, virtualization, schedulers
CLOUD COMPUTING
Cloud computing will spark a revolution in a way
organizations provide or consume information and
computing. Today’s most popular social networking site,
e-mail services , document sharing and online gaming
sites, are hosted on a cloud network of servers. Whereas
the giants of computer field like Microsoft are also taken
initiative to develop a cloud network for their users across
the globe. And to do that, more than half of their
developers and R&D are working on the project.
We define cloud computing, based on capabilities, which
are provided “as software”, “as a platform” and “as an
infrastructure” for consumers and enterprise to access on
demand regardless of time and location4.
Three basic services provided by cloud computing are as
follows:
Software as a service
Platform as a service
Infrastructure as a service
While doing study about cloud computing, found that
scheduling and resource allocation are the important
research topic. A scheduler is required to schedule number
of virtual machine, as virtual machine are used to request
from consumer, to save maximum energy and achieve
greater degree of load balancing and less resource
utilization from network which makes cloud computing
more responsive.
The main objective of scheduling algorithms in
distributed systems is to spreading the load on
processors and maximizing their utilization while
minimizing the total task execution time while performing
Job scheduling, one of the most known optimization
problems, plays an important role for creating a flexible
and reliable systems. The main purpose of using such kind
of scheduler is to schedule jobs to the adaptable resources
in accordance with adaptable time, which involves finding
out a proper sequence in which jobs can be executed
under transaction logic constraints.
Background
Distributed computing is a field of computer science that
studies distributed systems. A distributed system consists
of multiple autonomous computers that work together and
communicate through a computer network.
Types of distributed computing systems:
1. Cluster computing systems: It is not a new area of
computing. There is an increase usage of it in all areas,
where application is traditionally used in parallel or
distributed computing platforms.
2. Grid computing systems: Computing becomes pervasive
and individual users have gain access to computing
resource as needed with little knowledge of where those
resources are located or stored, and what the underlying
technologies, hardware, operating system and so on are.
3. Peer to peer computing: A class of systems and
applications that offers distributed resources to platform a
function in a decentralized manner. The resources are
encompasses by computing power data, network
bandwidth, and presence of human, computers or other
resources.
4. Cloud computing system: With cloud computing, users
use a variety of devices, including PC’s, laptops, and smart
phones to access program storage and application-
development platforms over the internet, via services
offered by cloud computing providers.
Cloud computing possess the following key
characteristics:-
1.On-demand self-service: A user having provision
computing capabilities, such as server time and network
storage, works automatically without having human
interaction with each service provider.
2. Broad network access: Cloud computing provide

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resources to the users with various capabilities over the
network which is accessed through standard mechanisms
that promote use by heterogeneous thin or thick client
platforms such as mobile phones, laptops etc.)
3.Resource pooling: It provider’s computing resources which
are pooled to serve multiple consumers using a multi-tenant
model, having different physical and virtual resources
which are dynamically assigned and reassigned according
to consumer demand. Examples of resources include
storage, processing, memory, network bandwidth, and virtual
machines.
4. Rapid elasticity: Capabilities of such kind of systems can
be rapidly and elastically provisioned, but in some cases
automatically, to quickly scale out, and rapidly released to
quickly scale in to the consumer, the capabilities available
for provisioning often appear to be unlimited and can be
purchased in any quantity at any time for the use.
5. Measured Service: Cloud systems are built in a way that
they automatically control and optimize the resource use by
leveraging a metering capability at some level of abstraction
appropriate to the type of service (e.g., storage, processing,
bandwidth, and active user accounts). Resource usage can
be monitored, controlled, and reported, for providing
transparency for both the provider and consumer of the
utilized service.
Types of Cloud Models:
1. Public Cloud: It is developed where several
organizations have similar needs and they need to seek to
share infrastructure. It helps in allowing freeing use from
performing important task like installation of resource,
their configuration and storage.
2. Private Cloud: It enables the remote access of
applications by smart phones. The cloud-based resources
are delivering to one platform and can be access from local
PC.
3. Community Cloud: It is developed to share
infrastructure between several organizations from a
specific community with common concerns, and can be
managed internally or by a third party hosted internally or
externally.
4. Hybrid Cloud: The cloud infrastructure is a
composition of two or more clouds(private, community
or public) that remain unique entities but are bound
together by standardized or proprietary technology that
enables data and application portability.
Issues in Cloud
One of the major issues in implementing cloud computing
is taking virtual machines in use, which contain critical
applications and sensitive data to public and shared through
cloud environment. The following are certain issues in cloud
computing.
1.Performance: The major problem arises in the
performance can be for some intensive transaction-oriented
and other data-intensive applications, in which cloud
computing may lack adequate performance. Also, the users
who are using the cloud network from a long distance may
experience high latency and delays.
2.Security and Privacy: Customers are worried about their
data and the vulnerability of attacks, when information and
critical IT resources are outside the range of firewall.
3.Control: Some IT departments are concerned because the
cloud computing providers have the full control over the
platforms. Cloud computing providers typically do not design
platforms for specific companies and their business practices.
4.Bandwidth Costs: With cloud computing, companies can
save money on hardware and software; however they could
have to pay higher network bandwidth charges. Bandwidth
cost may be low for smaller Internet-based applications,
which are not data intensive, but could significantly grow for
data intensive applications.
5. Reliability: Cloud computing still does not always offer
round-the-clock reliability. There were cases where cloud
computing services suffered few-hours outages.
6.Security Policy: It is very difficult to choose whether the
user would have same security policy control over their
applications and services or the cloud provider will provide
its own policies. If so, then the issue of trusting third party
vendor arises.
7.Scheduling: Scheduling is the method of time division by
which threads, processes or data flows are given access to
system resources (e.g. processor time, communications
bandwidth). This is usually done to balance the load on a
system effectively or achieve a target quality of service. The
need for such kind of scheduling algorithm arises because
from the requirement for most modern systems to perform
multitasking (execute more than one process at a time) and
multiplexing (transmit multiple flows simultaneously).The
scheduler is concerned mainly with the following:
Throughput : The total number of processes that complete
their execution per time unit.
Latency, specifically, Turnaround time - total time taken
between submission of a process and its completion.
Response time–total duration of time it takes from when a
request was submitted until the first response is produced.
Scheduling for Cloud Computing
Mainly there are several type of scheduling techniques are
use by the cloud network. 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), Min-Min algorithm and Max-Min
algorithm.
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.

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A. First Come First Serve Algorithm: The jobs in a queue
which arrive first get processed first.
B. Round Robin algorithm: 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 pre-emptedand given to the next
process waiting in a queue. The pre-empted process is
then placed at the back of the ready list.
C. Min-Min algorithm: This algorithm searches for the
small task among the group of task for processing, which in
turn large task delays for long time.
D. Max-Min algorithm: This algorithm chooses large tasks
to be executed firstly, which in turn small task delays for
long time.
E. Most fit task scheduling algorithm: In this algorithm
task which fit best in queue are executed first. This
algorithm has high failure ratio.
F. 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.
G. Shortest-Job-First (SJF) algorithm: This 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 CPUburst.
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.
EXISTING SCHEDULINGALGORITHMS
The following scheduling algorithms are presently
established in the cloud computing environment.
1.Fuzzy-Genetic Algorithm based Task scheduling
Optimizations
An optimized algorithm is proposed based on the Fuzzy-
Genetic Algorithm optimization which makes a
scheduling policy by evaluating the entire group of task in
the job queues. Fuzzy sets were used to represent
imprecisescheduling parameters and also to represent
satisfaction grades of each objective. Genetic algorithms
with different components were developed on the based
technique for task level scheduling in HardtopMap Reduce.
To gain a better balanced load across all the nodes in the
cloud environment, the scheduler is revised by predicting
the execution time of tasks assigned to certain processors
and making an optimal decision over the entire group of
tasks. Although this method meets user’s requirement and
gets good resource utilization, the predicted execution time
is a disadvantage of this scheduling method since it is not
possible topredict the execution time of tasks effectively
before executing the tasks.
2. The Analytic Hierarchy Process for Task scheduling and
resource allocation
Daji Ergu et al. presented a model for task-oriented
resource allocation in a cloud computing environment .In
this model computing tasks is collected in the Task Pool.
These tasks are
ranked using the pair wise comparison matrix technique and
the Analytic Hierarchy Process giving the available
resources and user preferences and are submitted to
computing resources
distributed in Cloud Computing Nodes. The
computing resources can be allocated in terms of the rank
of tasks. When all tasks are ranked according to available
resources this model improves the resource utilization and
also meets user requirements. But here it is not possible to
allocate resources dynamically.
3. A Priority based Job Scheduling Algorithm
A new priority based job scheduling algorithm (PJSC) is
proposed in cloud computing environment based on multiple
criteria decision making model, using analytical hierarchy
process. Provided a discussion about some issues related to
the proposed algorithm such as complexity, consistency and
finish time. The proposed algorithm has reasonable
complexity. But the main disadvantage is that the finish time
cannot be calculated and response time is more .Also for
more number of jobs allocations it is not suitable since
finding priority of each job is tedious one.
4. Market Oriented Scheduling Policies
By considering the time and cost of resource provisioning,
two Market oriented scheduling policies (MOSP) were
proposedthat aim at satisfying the application deadline by
extending the
computational capacity of local resources via hiring
resource from Cloud providers. The policies are not
having any earlier knowledge about the application
execution time. The proposed the Cost Optimization and
the Time Optimization scheduling policies increase the
computational capacity of the local resources by
hiring
resources from IAAS providers.
5. Online Optimization for Scheduling
Preemptable TasksJiayinLi et al. proposed a resource
optimization mechanism in federated IaaS cloud system
which enables preemptable task scheduling. In this model,
every data centre has a manager server that knows the
current statuses of VMs in its own cloud. And manager
servers communicate with each other. When a cloud
receives requests from users, its manager server
communicate with manager servers of other clouds
and distribute its tasks across thewhole cloud system by
assigning them to other clouds or executing them by itself.
The proposed algorithms, dynamic cloud list scheduling
(DCLS) and dynamic cloud min-min scheduling
(DCMMS) adjust the resourceallocation dynamically
according to the updated information of actual task
execution. Also they have proposed energy aware local
mapping mechanism which can reducethe
energyconsumption in federated cloud system.
6. Resource Scheduling Strategy based on Genetic
Algorithm
Jianhua Gu et al. presented a scheduling strategy on load
balancing of virtual machine resources using
Genetic Algorithm (RSGA). It uses historical data and
current states of VMs. In the proposed method starting
from the initialization incloud itself they look for the best
scheduling solution by genetic algorithm in every

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scheduling and when there are no VM resources in the
whole system use the algorithm to choose scheduling
solution according to the computed probability. Even though
this method can better realize load balancing and proper
resource utilization, it does not deals with the dynamic
behaviour of resource allocation.
7. Job scheduling algorithm based on Berger model
Berger model theory on distributive justice in the field of
social distribution is introduced into the job scheduling
algorithm in cloud computing.Job scheduling algorithm
based on Berger model(JSBM) concentrates on the fairness
of theresource allocation.The proposed model agrees with
the Quos parameters like completion time and bandwidth.
COMPARISON OF DIFFERENT SCHEDULING POLICIES
Table 1: list out the environment, algorithmand schedulingparameters used in different scheduling policies. Also compares
various scheduling policies in terms of their advantages anddisadvantages.
Sl no Paper title/
Author
Algorithm/techniq
ue used
Scheduling
parameters
considered
Advantage Disadvantage
1 Task scheduling
optimizations for
the cloud
computing system,
Sandeep Tayal
Genetic algorithm
based scheduling
Execution time of
tasks
Meet user
requirements and
improved resource
utilization
Execution time is
more
2 The analytic
hierarchy process:
Task scheduling
and resource
allocation in cloud
computing
environment,
DajiErgu, Gang
Kou, YiPeng,
YongShi,YuShi
Ranking of tasks is
done by using
reciprocal pair wise
comparison matrix
and analytical
hierarchy process
Response time,
task expense
Improves resource
utilization
Cannot allocate
tasks dynamically
3 A Priority based
job scheduling
algorithm in cloud
computing,
shamsollah
Ghanbari,Mohame
d Othman
Based on the
theory of
Analytical
hierarchy process
Make span Since priority is
considered
important task will
not be lagged
Increased make
span
4 Adapting market
oriented scheduling
policies for cloud
computing,
Mohsen Amini
Salehi,Rajkumar
Buyya
Deadline budget
constraint based
Time and cost
optimization
scheduling policy
Response time,
execution time,
cost
Increase the
computational
capacity of the
local resources by
hiring resources
from IaaS
providers
Increased
completion time
5 Online
optimization for
scheduling
preemptable tasks
on IaaS cloud
systems, JiayinLi,
MeikangQiu,
ZhongMing,
GangQuan,
XiaoQin, Zonghua
Gu
Based on cloud list
scheduling and
cloud min-min
greedy algorithm
for scheduling
Arrival time and
execution time
The
dynamic procedure
provides
significant
improvement in the
fierce resource
contention
situation.
Preemption leads
to increased
response time and
overhead to the
cloud providers
6 Evaluation of gang
scheduling
performance and
cost in cloud
computing system,
Ioannis A,
Moschakis,
Helen.D, Karatza
Gang scheduling
approach based
shortest queue first,
adaptive first come
first served and
largest job first
algorithm
Waiting time,
response time, cost
Improved resource
utilization
Not considered the
priority among the
tasks
7 Anew resource
scheduling strategy
based on genetic
algorithm in cloud
computing
Based on genetic
algorithm and
spanning tree
principle
Number of virtual
machines,
execution time
This method can
better realize load
balancing and
proper resource
utilization
It does not deal
with the dynamic
behaviour of
resource allocation.

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environment,
Jianhua Gu, Jinhua
Hu, Tianhai Zhao,
Guofei Sun
REVIEW OF RELATED WORK
In this decade we refer many approaches viz. algorithm,
methods, paradigms, techniques how to schedule
virtual machines running on physical machines and also
concentrate on energy consumption less, optimization ,
fully workload distribution , exploitation with physical
machine rateability.
M. Devare et al,proposed a scheduling policy
toimplement Scheduler which assign number of virtual
machine requests coming from consumer to virtual
machines on the base of ‘bully’ and “non-bully” approach.
The solutions in the context of Haizea are shown, through
experiments. The big improvement in utilization and energy
consumption is found as workloads are running with lower
frequencies. The coincidence of energy consumption and
utilization is improved.
Jiandun Li et al introduce a hybrid energy-
efficientscheduling algorithm for private clouds,
concentrated on load balancing, Load migration on the
base of state of virtual machines, count response time. If
response time increases then energy also increases. So
they minimised response time in their algorithm.
Gregor Von Laszewski et al proposed scheduling virtual
machine in a compute cluster to reduce power
consumption through Dynamic Voltage Frequency
Scaling (DVFS),implementation of energy efficient
algorithm to allocate virtual machine.
Bo Li, Jianxin Li et al states Energy aware heuristic
algorithm on base of distributes workload in virtual
machine with minimum number of virtual machines or
nodes required
that workload. So that workload migration, workload
resizes virtual machine migration these approaches are
used in algorithm.
CONCLUSIONS AND FUTURE WORK
This paper is based on cloud computing technology which
has a very vast potential and is still unexplored. The
capabilities of cloud computing are endless. Cloud
computing provides everything to the user as a service
which includes platform as a service, application as a
service, infrastructure as a service.One of the major issues
of cloud computing is scheduling mechanism because
overloading of a system may lead to poor performance
which can make the technology unsuccessful. So there is
always a requirement of efficient scheduling algorithm for
efficient utilization of resources. Our paper focuses on the
various scheduling algorithms and their applicability in
cloud computing environment.
We first categorized the algorithms asBatch mode heuristic
scheduling algorithms(BMHA) and online mode heuristic
algorithms. Then we analyzed the various algorithms
which can be applied in BMHA environments. After that
we described the various dynamic scheduling mechanism
algorithms. For solving any particular problem some
special conditions need to be applied. So we have
discussed some additional algorithms which can help in
solving some sub-problems in scheduling mechanism
which are applicable to cloud computing. In our future
work we will analyze the algorithms with numerical
analysis and simulation, which are energy efficient, have
less power consumption.
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