Cloud Computing: A Perspective on Next Basic Utility in IT World

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
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Cloud Computing: A Perspective on Next Basic Utility in IT World
Sonam Seth1, Dr. (Mrs.) Nipur Singh2
1Sonam Seth, Research Scholar, Kanya Gurukul Campus, Dehradun (U.K.) India, 2Dr. (Mrs.) Nipur Singh, Professor,
Department of Computer Science, Kanya Gurukul Campus, Dehradun (U.K.), India,
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Abstract- Technology is growing rapidly. Cloud
Computing is the most promising and latest technology in
the IT era. In real world everyone want fast, secure and
reliable access of data so cloud computing is the next basic
need in the IT world. Cloud computing is the abstraction of
web enabled computers, resources and services to utilizethe
resources optimally. Cloud is a network of virtually
distributed resources and clusters. Virtualization of
resources facilitates resource utilization on demand. The
deployment of virtual clusters offer full connectivity to the
virtual machines connected in different network. To deal
with big data management cloud architecture is introduced
that facilitate big data processing using big data analytics.
Cloud computing is the advanced version of distributed
computing and parallel processing.
Keywords: Cloud Computing, Distributed Computing,
Virtualization, Virtual machine, Big Data.
1 INTRODUCTION
In recent trend,itiscommon toaccess information
over the internet independently without hosting the
infrastructure. This infrastructure made up ofdata centres
that are monitored and maintained by the cloudproviders.
Cloud is virtualization of resourcesandcomposition.Cloud
Computing has been recognized as a model that provide
infrastructure, platform and services. Each service is
respectively called Infrastructure as Service (IaaS),
Platform as Service (PaaS), and Software as Service(SaaS).
“Cloud computing is a model for enabling ubiquitous,
convenient, on-demand network access to a sharedpool of
configurable computing resources (e.g., networks,servers,
storage, applications, and services) that can be rapidly
provisioned and released withminimal management effort
or service provider interaction. The cloud model is
composed of five essential characteristics, three service
models, and four deployment models” [1]. In business
perspective cloud computing can be defined by 4E
approach [2]. 4E approach is given by Ashok Soota:
i) Explore: Promises & challenges.
ii) Envision: How this can transform organization.
iii) Enable: Resources & skills.
iv) Execute: Design, development & operation of
cloud.
Science Cloudsprojectswere startedbyUniversity
of Chicago (UC) and University of Florida (UFL) as a
product. The first cloud at the University of Chicago,
became available on March 3, 2008, and was named
“Nimbus” [4]. “A cloud is a type of parallel and distributed
system consisting of a collection of inter-connected and
virtualized computers that are dynamically provisioned
and presented as one or more unified resources based on
service-level agreements established through negotiation
between the service provider and consumers” [3]. A
client/user requests a resource and if the request is
authorized, a Virtual machine is deployed on host.TheUFL
cloud configuration contains an innovation: private IP
addresses are used in deployed virtual machines and
network virtualization is used to connect virtual machines
to the client/owner machines. The Cloud computing
models are based on virtualization of computing resources
allowing customerstoprovisionresourceson-demandon a
pay-as-you-go basis [3] to optimize performance
evaluation parameters. Virtual machines (VMs)establisha
development path forincorporating new functionalitysuch
as server consolidation, migration, and secure computing.
“Cloud Computing is a computing technology that provide
on demand reliable quality of service to end-users that
optimize the usage of resources as well as cost of
resources”. Cloud computing is the latest computing
technology that delivers IT resources as services in which
users are free from the burden of the low-level
implementation or system administration details.
Big Data Analytics (Technology) is used to store
large amount of data (in terabytes) and handled by
Relational Database Management System (RDBMS). Big
Data Technology is more suitable to maintain data having
high volume, high velocity and high variety that is need of
recent trends [5]. The data is not the “stock” in a data
warehouse but a continuous flow [6]. “Big data is defined
as large amount of data which requires new technologies
and architectures so that it becomes possible to extract
value from it by capturing and analysis process” [7]. Big
data technology helps to collect large data on cloud and
cloud computing technology helps toprovidethiscollected
large data; Virtualization technology by creating virtual
machines on hosts, is used to maximize the utilization of
computing resources and manages memory.
In this paper we have discussed the view of cloud
computing and cloud computing architecture, explore the
key issues and challenges. The paper is divided into six
sections. Cloud computing architecture is discussed in
section 2, in section 3 deployment models are given, cloud
computing challenges and issuesarediscussedinsection4,
literature survey of some existing resource allocation

methods for identified challenged is explained insection5,
and conclusion is given in section 6.
2 CLOUD COMPUTING ARCHITECTURE
In paper [8] authorshaveproposedarchitectureof
a data centre’s resource management system where
resource management is divided into local and global
policies. At the local level, the system facilitates the guest
OS’s power management strategies. The global manager
gets the information on the current resource allocation
from the local managers and applies its policy to decide
whether the VM placement needs to be adapted.
2.1 IaaS: This is the base layerofcloudstack.Itworks
as a base for the other two layers, for their execution.Stack
is based on Virtualization. Examples are Amazon, GoGrid,
3Tera etc.
2.2 PaaS: This layer provides the platform that is
development environment upon which other applications
run. Examples are LAMP platform, Google’s App Engine,
Force.com etc.
2.3 SaaS: In this layer or model, a complete
application is offered to the user, as a serviceondemand. A
single instance of the service runs on the cloud and
multiple end users are served. Examples are Google,
Microsoft, and Salesforce etc.
3 DEPLOYMENT MODELS
3.1 Private Cloud: Private Cloud is used by one
organization. The cloud infrastructure is operated only for
an organization. It may be managed by the organization or
a third party. Private Cloud is used by one organization.
Services are paid.
3.2 Public Cloud: Mega-scale cloudinfrastructureis
made available to the general public or a large industry
group and is owned by an organization selling cloud
services [9]. Public cloud is used by general people. Public
clouds are owned by large organization such as Google,
Amazon, and Microsoft.
3.3 Community Cloud: Community clouds are
shared by more than one organization. Services are based
on pay per use. These types of clouds are setup for specific
purpose; especially for research purpose.ExampleisNASA
etc.
3.4 Hybrid Cloud: The cloud infrastructure is a
composition of two or more clouds (private or public) that
work together by using virtualization technology that
enables data and application portability. In recent
trend of research Hybrid clouds are used. These cloudsare
combination of other clouds.
4 CHALLENGES WITH CLOUD COMPUTING
In cloud computingframework schedulingoftasks
with QoS constraints is a challenging technical problem.
Dynamic resource provisioning for Big data application
scheduling is a challenge in modern high performance
computing systems. A key challenge for these systemsisto
provision shared resources on demand to meet QoS. Cloud
computing is based on virtualization and distributed
computing to support cost-efficient usage of computing
resources, focuses on resource scalability and on demand
services. Traditional data-centre oriented models are
converted into distributed clouds with a loosely coupled
network that offers enhanced communication and
computational services to end-userswith qualityofservice
(QoS) requirements [10].
4.1 Resource Allocation, Scheduling and
Optimization Issue
Resource allocation indicates that the resources
are allocated to end users on-demand. Resources are
distributed among various ports to fulfil their requests.
Virtual resource model for virtualization of resources
increases the utilization of resources optimally and
describes the execution time.Virtualization of resources
can conquer some limitations and allow on-demand
creation/deployment of multiple isolatedvirtual networks
that enable the creation of virtual private clusters on a per
user basis. The virtualization technology allows Cloud
providers to create multiple virtual machine (VMs)
instances on a single physical server, and the utilization of
resources increases and increases the return on
investment [8].
4.2 Cost Optimization Issue
Cost is calculated in two aspects: computing cost
and communication cost. Computing cost is the cost
associated with resource computing capacity and
communication cost is the data transfercost.Therearetwo
types of computing resources: on-demand instances and
reserved instances. On-demand instances are paid only
when utilized and they are useful to satisfy dynamic
demand. While reserved instances are paid for a certain
time period and are independent of usage.
4.3 Processing Time and speed
Another issue is to maximize the throughput in
less time with high speed. Performance is designed to an
application’s capabilities within the cloud infrastructure
itself. Limited bandwidth, disk space, memory, and CPU
cycles.

4.4 Memory management, Storage
Memory management is one of themainchallenge
in cloud computing. Capacity of the cloud computing
systems can vary using cache memory by applying
virtualization concept.
4.5 Dynamic Load Balancing, Scalability
Load balancing is a technique that provide
maximum throughput with minimum response time. Load
balancing is dividing the load among all servers, so the
requests are serviced without any delay with load
balancing. Load balancing is usedtodistributea largerload
to smaller processing nodes for enhancing the overall
performance of system. Under provisioning and over
provisioning are also two major issues in load balancing.
4.6 Security Issue
There are three types of security concern:
Physical, Operational and Programmatic security. Security
is one of the major issue which reducesthegrowthofcloud
computing and complications with data privacy and data
protection like virus in the system. In cloud computing
system privacy of data (content) can be handled by the
feature ‘obfuscation’, where this is possible otherwise
simple Encryption-Decryptiontechniquescanalsobeused.
The obfuscation method uses a key which is chosen by the
user and known by the privacy manager, but which is not
communicated to the service provider. Thus the service
provider is not able to de-obfuscate the user’s data. This
reduces the risk of unauthorized access of data on cloud.
4.7 Fault tolerance and Reliability
Fault tolerance is one of the major issue in cloud
computing. Fault tolerance techniques are in use during
the procurement, or development of the software.
Performance efficientresourcemanagementstrategiesthat
can be applied in a virtualized data centre by a Cloud
provider (e.g. Amazon EC2).
4.8 QoS (Quality of Service)
QoS is the combined effortofservice performance,
which determines the degree of satisfaction of a user for
the service. Managing the QoS parameters on the resource
provider’s side such as price and load is the recent
challenge in Cloud Computing.QoScomprisescomputation
time, execution price, packet loss rate, throughput, and
reliability [11].
4.9 SLA (Service Level Agreement)
In the consideration of profit of both parties
service providers and consumers, SLA based schedulingin
cloud computing is the major challenge in recent trend to
optimize the response time, throughput and QoS.
5 LITERATURE SURVEY OF CHALLENGES
WITH CLOUD COMPUTING ALONG WITH
RESOURCE SCHEDULING ALGORITHMS
Following table shows challenges with cloud computing
and resource scheduling algorithms to handle key issues:
Table 1: Literature survey of some Resource Scheduling
Algorithms to handle Key Issues
Key Issues Model Used Outcomes Limitations
Cost and
Time
Pre-emptable
shortest job next
scheduling
Algorithm(PSJN)
[12]
Cost and
improved
response and
execution time.
Need to improve to
handle under-
provisioning and
over-provisioning.
Makespan User priority
guided Min-Min
scheduling
Algorithm [13]
Average SLA. Need to improve SLA
based parameters.
Makespan,
Economic
Cost, Energy
Consumption,
Reliability
Scheduling with
Genetic [14]
Consume more
energy and
achieve higher
level of load
balancing.
Rescheduling of
unexecuted task is
required to minimize
the computation cost.
Resource
utilization,
Time
A Particle Swarm
Optimization
based Heuristic
for Scheduling
[14]
Task scheduling Lack of both
reliability and
resource availability
criteria.
Process
completion
Time
Improved Particle
Swarm
Optimization [15]
Average lower
SLA and average
completion
time.
Need to improve SLA.
Cost,
Performance
Improved Cost-
Based Algorithm
for Task
Scheduling [16]
Improves the
computation
and
communication
ratio.
Need to minimize the
execution time that
minimizes the
makespan.
Makespan,
Load Balance
Heuristic based
strategy list
scheduling [14]
Optimization of
load balancing
and reduced
makespan.
Need to optimize
scheduling
algorithm.
Resource
allocation
based on load
balance
Meta Heuristic
Techniques like
GA, ACO, PSO [17]
Reduce the
power
consumption
and execution
time.
Can achieve more
optimized
performance and
optimal use of
resources.
Cost,
Virtualization,
Time
Cost-Effective
Virtual Machine
Allocation
Algorithm within
Execution Time
Bound [18][19]
A two-step
heuristic
scheduling
method has
been used to
maximize the
resource
Need to optimize
QoS.

utilization.
Delay time and
makespan are
considered to
reduce.
Cost and time Dynamic resource
provisioning
techniques
[19][20]
Time and
dynamic load
assignment.
Need to optimize the
result by modifying
the model in the
consideration of
dynamic nature of
users.
Scheduling,
VM
management
Dynamic
Provisioning
Dynamic
Scheduling
[19][21]
Better
performance of
VM.
Need to design
efficient algorithm
to handle
unpredictable
workloads
optimally.
6 CONCLUSION
Cloud computing is the technology which enables
the user to access resources using front end machines,
there is no need to install any software. In this paper
authors have discussed the concepts and definitions of
cloud computing and cloud computing systems with the
key issues or challenges and related existing some models
to handle key issues of cloud systems. Also authors have
discussed the architecture models of cloud computing
paradigm. As clouds are designed to provide services to
external users, providers need to be compensated for
sharing their resources and capabilities. This field is
growing up hence further research in this field is required
in the direction of interaction protocols to support
interoperability between different cloud serviceproviders
and optimized models to handle key issues.
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