A SURVEY OF RESOURCE SHARING IN CLOUD COMPUTING WITH DISSIMILAR FACTORS

Mr.N.Vijayaraj1 and Dr.SenthilMurugan 2

1 Research Scholar, Department of Computer Science & Engineering in Vel Tech Dr.RangarajanandDr.SakunthalaRangarajan Technical University, Chennai, India.

2 Associate Professor, Department of Computer Science & Engineering in Vel Tech Dr.Rangarajan andDr.SakunthalaRangarajan Technical University, Chennai, India.

ABSTRACT

A cloud computing is a new age technology it enables users to access services, applications, and

infrastructure resources by using thin clients anywhere and at any time. In this paradigm, multiple users

can share cloud infrastructure resources. As military, academic, and commercial computing systems

evolve from autonomous entities that deliver computing products into network centric enterprise systems

that deliver computing as a service, opportunities emerge to consolidate computing resources, software,

and information through cloud computing. Resource sharing task is quite a challenging in cloud

environment. Usually resources are shared by user needs. These issues are , resource and reputation

management strategies are not well designed and they are not powerful. If the client selects the resource,

then the other resource nodes are neglected and there is no full utilization of resources and it doesn’t meet

client Qosdemands. Resource sharing is the key technology in cloud computing. This paper provides the

survey on Resource sharing.

I.INTRODUCTION

Cloud computing has become a needed one by which we can provide services to the clients over the

internet. There are many number of cloud service providers such as Amazon, Google, and IBM etc. These

service providers charge according to the storage that we use, bandwidth and various other parameters.

The service provider cannot provide the services using only a cloud that is one cloud and it is not possible

when the clients are increasing. It also cannot provide resources to an application wholly in some

situations during high time [1]. In order to provide services, the researchers need to connect multiple

clouds having a implicit lab environment in order to provide super-computing capabilities to the clients in

order to fully make use of the resources. Due to this characteristics and developments in cloud computing,

the demand for collaborative cloud computing has grown. Due to CCC, we can provide services to people

where the resources belonging to different organizations are highly pooled. CCC interlinks various

physical resources to empower sharing of resources in the clouds in order to provide implicit perspective

of resources to its clients. This perspective is beneficial when a client requests resources and cloud doesn’t

have sufficient resources. It has to discover and use the resources in different clouds [1]

II.RELATED WORK

Alarge-scale resource sharing system builds a virtual supercomputer by provided that an infrastructure for

sharing fabulous amounts of resources above the web. Cloud computing has been proposed to connect a

large number of clouds as an alliance that come together to share resources in order to better respond to

large-scale application requirements. collaborative cloud computing (CCC) can handle the situation when

a single cloud is not sufficient to provide sustainable high-quality service to some applications with

demand for scalable resources or when researchers want to build a virtual lab environment across

geographical distribution of physical hosts.[For example, cloud customer Drop box had around 100

million users in 2012 , and around50 million users in 2011, which is three times the number of 2010. As a

cloud may be overloaded during peak periods and stay idle in time periods with few service requests, it is

promising to integrate many dispersed clouds from different corporations and organizations to fully utilize

cloud resources.

The large-scale resource sharing system is a kinds possible the sharing of a different of resources

comprising software, data (music, video, books) storage, memory, CPU time, network bandwidth and

devices distributed over web. A computing resource (e.g., virtual machine) is stated by a set of attributes

such as CPU speed, memory and OS version and device name. A data resource also can be described by a

few keyword attributes. For example, if a node having a following features like OS name=”Linux”, “Free

memory =1024 MB”,CPU speed =”1000 MHz” [1]

Fig1: Typical Mobile cloud computing architectures

The traditional client/server architecture, dispersed client/server architecture, grid architecture entirely

different from mobile cloud computing architecture (MCC). The merging of cloud computing into the

mobile domain creates the appealing paradigm of mobile cloud computing (MCC). MCC compromises a

favorable solution not only to extend the limited competencies of mobile devices, but also to lower energy

consumption if designed in a green manner [2].

As illustrated in Fig.1, the traditional common cloud it’s using mobile devices and shared resources in

remote data centers and acts as an agent between the real content providers and mobile devices. The

backbone of network is access the resources/services from data area to mobile devices often need to go

through. In contrast, the lightweight cloudlet can balance the scale of shared resources and the approach

overhead. A cloudlet is a trusted, resource-rich, Internet connected computer or a cluster of computers that

can be utilized by mobile devices via a high-speed wireless local area network (WLAN). In this MCC

architecture, mobile devices function as the clients and cloudlets as the service providers. Logical

interaction between them can be more easily achieved in the cloudlet’s physical proximity with the low

one-hop communication latency. Due to the spatial distributions of cloudlets and their distinct capabilities

or hosted resources, the mobile devices have different preferences over the cloudlets [2].

Deployment

model

Ownership Location Resource

Sharing

Advantages Disadvantages Example

Public

Cloud

Third party Off-site Low End-users rent

part of the

resources

Security issues Amazon,

Google

Private

Cloud

Organization

or third party

On-site High Resource

dedicated to

organization,

more secure

Less flexible Nebula

Hybrid

Cloud

Organization

or third party

On-site or

off-site

Medium Maximum

flexibility,

dedicated

resources

need additional

IT skills to

operate and

manage

Community

Cloud

Organization

or third party

On-site or

off-site

High Facebook

Table.1 Cloud Deployment models with resource sharing

III.RESOURCE SHARING IN CLOUD (RSC) AT A GLANCE

The various parameter to RSC and the way to resource sharing vary based on the service & cloud. The

schematic diagram in fig.2 propose and classify the resource sharing in cloud paradigm. The following

section discusses the RSC.

A. Virtual machines

Different kind of resource sharing mechanism are proposed in cloud . in the work done by Sheng Di[3],

actual task is optimization of cloud resource allocation with scheduling the task using different types of

TASK SCHEDULINGQOS

QOS

ACCESS CONTROL

Multi Tenant

Bandwidth allocation,Traffic flow, Network sharing

Resource discovery,DHT,LSH & LIS Resource

Pricing ,Clabacus,Financial Option, Fuzzy logic

Preference based resource allocation,GATA

H-CRAN,CRRM,SPECTRUM SHARING

Preference based resource allocation,GATA

VM

RISK

BAND WIDTH

PRICING

Efficient ,Fidelity,

Flexibility

SOCIAL CLOUD

HYBRID &Heterogeneous CLOUD

Economic,Energy

RSC

scheduling techniques. This work does not use detailed knowledge of adaptive solution that can

dynamically optimize the performance in both competitive and non-competitive situation also plan to

improve the ability of fault tolerance and resilience in cloud system.

Figure 2. Resource sharing in cloud computing with various parameters

B.Risk

Risk is one important parameter in cloud computing paradigm. Abdurahman Almutiri[4] has proposed an

efficient risk aware virtual resource responsibility mechanism for clouds multitenant environment. In this

paper introduce impressionable in datacenters and minimize the data leakage in high sensitive data center

to low sensitivity data center.

Abdurahman Almutiri[4] had implemented the three assignment heuristics like security –aware scheduling

,access Control in cloud data center and vulnerability models. In organization , define the permission for

Role based access control model (RBAC) policy. The policy like a set of Roles( R), set of user(U) and set

of permission (P). this work does not detailed knowledge in data leakage in sensitive data center.

C.Bandwidth

Resource sharing in cloud computing over the web is based on bandwidth sharing. In bandwidth allocation

among tenants based on different requirements like network proportionality, min-guarantee and high

utilization. Network sharing policy to achieve both min-guarantee and proportionality, while prevent

tenants earning unfair bandwidth. Bandwidth sharing based on the analysis pricing model, foreign link

transmission , bandwidth allocation enhancement and traffic flow arrangement policy. Haiyingshen

proposed [5] bandwidth sharing and pricing policies to transform the competitive environment to a win-

win cooperative environment for tenants strive to increase the utilities of cloud provider. Haiyingshen

further will consider rewarding tenants for reducing demand to maintain the uncongested link states.

D .Efficient

Efficient resource sharing method based on Distributed Hash Tables(DHT) maintain for number of

resource information service systems have been put forth based on that offer scalable key-based lookup

functions. Effective resource sharing based on three challenges. The first challenge it achieved high

efficiency in an environment characterized by dynamically, geographically in large-scale scattered

resource. The second challenge is resource sharing with high fidelity. Fidelity defined by the ability to

locate all resource in the system that satisfy a resource request. The third challenge is flexibility it defined

by the ability to allow nodes to specify unlimited expensiveness with desired resource and similar resource

searching based on exact matching searching.

Haiyingshen[6] proposed the resource discovery mechanism , which all attributes into a set of indices

using locality sensitive hashing(LSH) with DHT and this paper shows an LSH-based resource information

service(LIS) that combine with efficiency, fidelity and flexibility. Alpha –LIS and Hilbert-LIS provide

attribute likeness search that can attribute with similar character. Haiyingshen will further study the

effectiveness with Alpha –LIS and Hilbert-LIS in such feature similarity search and their side effects

caused by the alphanumeric transformation and develop an effective and robust resource information

service.

E.Pricing

Resource pricing in one the key parameter in resource sharing in cloud computing. BhanuSharma[7]

proposed the cloud compute community(C3)pricing architecture called clabacus(cloud abacus) with

moores law, that captures the technological advances of the resources. Mapping C3and contribution with

three major parallels between financial option and C3.The first major mapping is cloud resource pricing to

well established financial option pricing models and apply them to compute prices for the cloud resources.

Second mapping is estimating the risk associated with investment and its requires intricate mathematical

formulation. The third mapping techniques is to use the principles of collateral service agreements (CSA).

Pricing algorithm based on five parameter pertinent to pricing cloud resource like capital investment,

contract time, rate of depreciation, quality of service, age of resource. Value at risk(VaR) is based on two

different techniques like fuzzy logic and genetic algorithm. BhanuSharma[7] will next step is blended

effect of the parameter with resource pricing and multi objective price optimization.

F.Social cloud

Social cloud network platform have rapidly changed the way that people communicate and interact. It

enabled the establishment of participation in digital communities as well as representation, documentation

and explore the relationship. Following challenges proposed in social cloud like technical facilitation to

enable edge users to provide resource to, and consume resource from , one another it need to traverse

network address translation , handle non static IP address with QoS.

Simon caton[8] was implemented & proposed different types of algorithm like polynomial time algorithm,

deferred acceptance(DA) algorithm, welfare optimal (WO) algorithm, heuristic algorithm(HA), genetic

algorithm(GA), GATA. Simon caton[8] will include additional way for users to provide their preferences,

as well as method to detect them automatically from their social network.

G.Hybrid And Heterogeneous Cloud

Resource sharing in hybrid and heterogeneous cloud is a one primary parameter. In hybrid cloud, sharing

the resource in secured & authorized manner it was reduced the duplication using different types of

encryption and decryption algorithm. Jin Li[8] was implemented in symmetric encryption, convergent

encryption in private cloud and public cloud and also achieved the differential authorization and

authorized duplicate check.

H.Economic and Energy

Sharing the resource in cloud computing based on Economic and Energy. Manjinder Nir[9] was proposed

computation offloading by using the task scheduler based on centralized broker node and it also produced

the optimal solution. In resource augmentation providing the paying for resources, squeak of resources

and accessibility of resources.

Manjinder Nir[9] will also extend the work in scheduler model to consider network congestion based on

task priority and task execution redundancy while scheduling task offloading.

ADVANTAGES AND LIMITATIONS

There is lot of benefits in resource sharing while using cloud computing irrespective of size of the

organization and business markets. But resource sharing in cloud computing are some limitations as well,

since it is an evolving technology. Let’s have a close to look at the advantages and limitations of resource

sharing in cloud.

A. Advantages:

1) The user does not need to exhaust on hardware and software systems in cloud computing.

2) Cloud providers can stake their resources over the internet during resource scarcity.

3) The biggest benefit of resource sharing is that user neither has to install software and hardware to

access the applications, to develop the application and to host the application over the web.

4 )The next major benefit is that there is no limitation of place and medium. We can send our applications

and data anywhere in the world, on any system.

B. Limitations

1) In public cloud, the clients’ data can be susceptible to hacking and phishing attacks. Since the servers

on cloud are interconnected, it is easy for malware to spread.

2) Migration difficulty occurs, when the users wants to change to some other provider for the better

storage of their data. It’s very difficult to transfer huge data from one provider to the other provider.

3) Since users rent resources from local servers for their purpose, they don’t have control over their

resources.

4) Peripheral devices like printers and scanners might not work with cloud. Many of them require software

to be installed locally. Networked peripherals have lesser problems.

5) More and deep knowledge is required for assigning and managing resources in cloud, since all

knowledge about the working of the cloud rely upon the cloud service provider.

V. CONCLUSION

Cloud computing expertise is more and more used in business markets. In cloud computing paradigm, an

effective resource sharing strategy is required for achieving people satisfaction and higher the profit for

cloud service providers. This paper summarizes the dissimilar factors of RSC and its impacts in cloud

system. Some of the ways are discussed above mainly focus on bandwidth, memory, pricing, economic

&energy resources but are lacking in some determinant. Hence this survey paper will hopefully motivate

future researchers to come up with smarter , protected and best resource sharing algorithms and

framework to strengthen the cloud computing standard architecture.

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