unabridged collation about multifarious computing methods and outreaching cloud computing based on...

7/31/2019 Unabridged collation about multifarious computing methods and outreaching cloud computing based on innovativ

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Unabridged collation about multifarious computing methods and

outreaching cloud computing based on innovative procedure

Mehdi Darbandi1, Mohammad Abedi

1, Meysam Panahi

2, Ali Hamzenejad

2, Mohsen

Kariman Khorasani3

1

Department of Electrical Engineering and Computer Science at Iran University of Science and Technology(IUST), Tehran, Iran2Department of Management System and Productivity, Faculty of Industrial Engineering, Tehran South Branch,

Islamic Azad University (IAU), Tehran, Iran3Department of Communication Engineering, Islamic Azad University, Gonabad Branch, Gonabad, Iran

Abstract Cloud computing is one of today's mostexciting technologies due to its ability to reduce costs associated

with computing while increasing flexibility and scalability for

computer processes. During the past few years, cloud

computing has grown from being a promising business idea to

one of the fastest growing parts of the IT industry. IT

organizations have expresses concern about critical issues(such as security) that exist with the widespread

implementation of cloud computing. These types of concerns

originate from the fact that data is stored remotely from the

customer's location; in fact, it can be stored at any location.

Security, in particular, is one of the most argued-about issues

in the cloud computing field; several enterprises look at cloud

computing warily due to projected security risks. The risks of

compromised security and privacy may be lower overall,

however, with cloud computing than they would be if the data

were to be stored on individual machines instead of in a so -

called "cloud" (the network of computers used for remote

storage and maintenance). Comparison of the benefits and

risks of cloud computing with those of the status quo are

necessary for a full evaluation of the viability of cloud

computing. Consequently, some issues arise that clients need to

consider as they contemplate moving to cloud computing for

their businesses. Cloud computing is emerging as a prominent

computing model. It provides a low-cost, highly accessible

alternative to other traditional high-performance computing

platforms. It also has many other benefits such as high

availability, scalability, elasticity, and free of maintenance.

Given these attractive features, it is very desirable if automated

planning can exploit the large, affordable computational power

of cloud computing. However, the latency in inter-process

communication in cloud computing makes most existing

parallel planning algorithms unsuitable for cloud computing.

In this paper, at first we review different aspects of cloud

computing and tell about all features and advantages and

disadvantages of such network and after that we try to finddynamical system model for cloud computing based on

Kalman Filtering and demonstrate basic fundamental

equations of these model. This model can be used for modeling

and making decision about all aspects of cloud computing, for

example we can use this model for making decision about

security of such network by making a model for cloud

platforms and estimate and update information about the

presence of hackers and malicious actions. Also we can use

such dynamical modeling for calculating crowd on different

sections of cloud computing resources.

Keywords- Control model, Kalman estimator,estimation and prediction.

I. INTRODUCTIONCloud Computing is evolving as a key technology for

sharing resources. Grid Computing, distributed

computing, parallel computing and virtualization

technologies define the shape of a new era. Traditional

distance learning systems lack reusability, portabilityand interoperability. Network-based cloud computing

is rapidly expanding as an alternative to conventional

office-based computing. As cloud computing becomes

more widespread, the energy consumption of the

network and computing resources that underpin the

cloud will grow. This is happening at a time when

there is increasing attention being paid to the need to

manage energy consumption across the entire

information and communications technology (ICT)

sector. While data center energy use has received

much attention recently, there has been less attention

paid to the energy consumption of the transmissionand switching networks that are key to connecting

users to the cloud. With the advent internet in the

1990s to the present day facilities of ubiquitous

computing, the internet has changed the computing

world in a drastic way. It has traveled from the concept

of parallel computing to distributed computing to grid

computing and recently to cloud computing. Although

the idea of cloud computing has been around for quite

some time, it is an emerging field of computer science.

Cloud computing can be defined as a computing

environment where computing needs by one party can

be outsourced to another party and when need be arise

to use the computing power or resources like database

or emails, they can access them via internet. Cloud

computing is a recent trend in IT that moves

computing and data away from desktop and portable

PCs into large data centers. The main advantage of

cloud computing is that customers do not have to pay

for infrastructure, its installation, required man power

to handle such infrastructure and maintenance. In

recent years, State Grid Corporation of China has been

vigorously promoting smart grid construction, and

cloud computing is developing rapidly. Trend of theelectric power enterprise informatization construction

will be the private cloud computing, which will

JOURNAL OF COMPUTING, VOLUME 4, ISSUE 9, SEPTEMBER 2012, ISSN (Online) 2151-9617

https://sites.google.com/site/journalofcomputing

WWW.JOURNALOFCOMPUTING.ORG 26

2012 Journal of Computing Press, NY, USA, ISSN 2151-9617


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become the comprehensive platform of smart grid.

Mobile Cloud Computing (MCC) which combines

mobile computing and cloud computing, has become

one of the industry buzz words and a major discussion

thread in the IT world since 2009. As MCC is still at

the early stage of development, it is necessary to grasp

a thorough understanding of the technology in order to

point out the direction of future research.With the development of parallel computing,

distributed computing, grid computing, a new

computing model appeared. The concept of computing

comes from grid, public computing and SaaS. It is a

new method that shares basic framework. The basic

principles of cloud computing is to make the

computing be assigned in a great number of distributed

computers, rather than local computer or remoter

server. The running of the enterprise's data center is

just like Internet. This makes the enterprise use the

resource in the application that is needed, and access

computer and storage system according to therequirement.

Virtual machine (VM) is a key component of cloud

computing technology. Therefore developing an

optimal scheduling mechanism for balancing VM

operations at cloud computing framework is an

intriguing issue for cloud computing service

performance.

The industry-driven evolution of cloud computing

tends to obfuscate the common underlying

architectural concepts of cloud offerings and their

implications on hosted applications. Patterns are one

way to document such architectural principles and to

make good solutions to reoccurring (architectural)

cloud challenges reusable. To capture cloud computing

best practice from existing cloud applications and

provider-specific documentation, we propose to use an

elaborated pattern format enabling abstraction of

concepts and reusability of knowledge in various use

cases.

Cloud computing paradigm allows subscription-based

access to computing and storages services over the

Internet. Since with advances of Cloud technology,

operations such as discovery, scaling, and monitoringare accomplished automatically, negotiation between

Cloud service requesters and providers can be a

bottleneck if it is carried out by humans. Therefore,

our objective is to offer a state-of-the-art solution to

automate the negotiation process in Cloud

environments. In previous works in the SLA

negotiation area, requesters trust whatever QoS criteria

values providers offer in the process of negotiation.

Development of Internet technology and social

network has greatly changed the traditional software

engineering based on single Turing machine. Software

development will be cooperated and completed on thenetwork with collective intelligence. The interaction

among human-machine and machine-machine

becomes the kernel of Internet computing, while

Turing model studied on Entscheidungs problem based

on an automatic computer theoretical model without

interaction with people. Clusters or virtual clusters

become the basic platform of cloud computing centers.

And SaaS (Software as a Service), PaaS (Platform as a

Service), IaaS (Infrastructure as a Service) become thecommon knowledge for software engineers.

Furthermore, the research of network science has

discovered lots of physical law about the distribution

of information resources, such as the power law

distribution of Web services.

As more and more IT services are provided via cloud

computing technologies, businesses are worried about

acceptable levels of availability and performance of

applications hosted in the cloud. Since services in

cloud are interdependent. An infrastructure failure may

cause a number of service interruptions and result in

great business losses. In a word, incident managementis critical in cloud environments. Traditional incident

management concerns only IT performance but

overlooks business performance.

Extensive computing power has been used to tackle

issues such as climate changes, fusion energy, and

other pressing scientific challenges. These

computations produce a tremendous amount of data;

however, many of the data analysis programs currently

only run a single processor.

Cloud computing has elevated IT to newer limits by

offering the market environment data storage and

capacity with flexible scalable computing processing

power to match elastic demand and supply, whilst

reducing capital expenditure. However the opportunity

cost of the successful implementation of Cloud

computing is to effectively manage the security in the

cloud applications. Security consciousness and

concerns arise as soon as one begins to run

applications beyond the designated firewall and move

closer towards the public domain.

Recently, a number of cloud computing paradigms

have been proposed. The new term of cloud computing

is not a new concept, is a long-held dream ofcomputing as a utility [1]. From the view of

datacenters, the common understanding of the cloud

computing concept is Software as a Service (SaaS),

utility computing and application virtualization. In the

domain of Rich Internet Application (RIA) domain,

the view is different.

Cloud computing provides a multitenant feature that

enables an IT asset to host multiple tenants, improving

its utilization rate. The feature provides economic

benefits to both users and service providers since it

reduces the management cost and thus lowers the

subscription price. Many users are, however, reluctantto subscribe to cloud computing services due to






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security concerns. To advance deployment of cloud

computing, techniques enabling secure multitenancy,

especially resource isolation techniques, need to be

advanced further. Difficulty lies in the fact that the

techniques range and cross various technical domains,

and it is difficult to get the big picture.

In the recent era, cloud computing has evolved as a net

centric, service oriented computing model. Consumerspurchase computing resources as on-demand basis and

get worry free with the underlying technologies used.

Cloud computing model is composed of three service

models Software as a Service (SaaS), Platform as a

Service (PaaS) and Infrastructure as a Service (IaaS)

and four deployment models Public, Private,

Community and Hybrid. A third party service

provider, stores & maintains data, application or

infrastructure of Cloud user. Relinquishing the control

over data and application poses challenges of security,

performance, availability and privacy. Security issues

in Cloud computing are most significant among allothers. Information Technology (IT) auditing

mechanisms and framework in cloud can play an

important role in compliance of Cloud IT security

policies.

Cloud computing is a way to increase the capacity or

add capabilities dynamically without investing in new

infrastructure, training new personnel, or licensing

new software. In the last few years, cloud computing

has grown from being a promising business concept to

one of the fast growing segments of the IT industry.

Cloud computing has been considered as the 5th utility

as computing resources including computing power,

storage, development platform and applications will be

available as services and consumers will pay only for

what consumed. This is in contrast to the current

practice of outright purchase or leasing of computing

resources. When the cloud computing becomes

popular, there will be multiple vendor offering

different services at different Quality of Services and

at different prices. The customers will need a scheme

to select the right service provider based on their

requirements. A trust management system will match

the service providers and the customers based on therequirements and offerings.

Cloud computing is the new paradigm that has

changed traditional computer business schemes: static,

close, centralized, and proprietary methods cannot

cope with the new requirements that have emerged.

Still, this new scenario poses a number of

opportunities to use and novel problems to be faced.

Specifically, we focus on the accounting of cloud

computing services. These may include relations

between different service providers, user connections

to different simultaneous services, and the need for

new services to be incorporated into the accountingsystems to enable emerging business models, and so

on. Classic solutions fail to provide a proper answer as

they were not specifically design for cloud computing.

Against this background, we put forward a flexible

accounting model that allows the deployment of cloud

computing services to accomplish all the service

providers' requirements.

We live in space time dimensions and all physical and

social sciences are based on the dimensions. Therepresentation and digitization of scientific phenomena

into data and computation of the digitized data greatly

depends on the spatiotemporal principles that govern

the relationships of phenomena. The latest

advancement of cloud computing is not an exception.

Conducting cloud computing in a spatiotemporal

fashion will help use spatiotemporal principles, which

exist in all physical and social sciences, to optimize

cloud computing and science discoveries.

Many current users of cloud computing document-

sharing services such as Google Docs (i.e., those who

primarily access client-only mind map features)require a fast and simple mechanism for accessing

mind map files in clouds.

MapReduce has been widely used as a powerful

parallel data processing model and is adopted by most

cloud providers to build cloud computing framework.

However, in open cloud systems, security of

computation becomes a great challenge. Moreover,

MapReduce data-processing services are long-running,

which increases the possibility that an adversary

launches an attack on the workers and make them

behave maliciously and then tamper with the

computation integrity of user tasks where their

executions are generally performed in different

administration domains out of the user control. Thus,

the results of the computation might be erroneous and

dishonest.

The rapid deployment of cloud computing promises

network users with elastic, abundant, and on-demand

cloud services. The pay-as-you-go model allows users

to be charged only for services they use. Current

purchasing designs, however, are still primitive with

significant constraints. Spot Instance, the first

deployed auction-style pricing model of Amazon EC2,fails to enforce fair competition among users in facing

of resource scarcity and may thus lead to untruthful

bidding and unfair resource allocation. Dishonest users

are able to abuse the system and obtain (at least) short-

term advantages by deliberately setting large

maximum price bids while being charged only at lower

Spot Prices. Meanwhile, this may also prevent the

demands of honest users from being satisfied due to

resource scarcity. Furthermore, Spot Instance is

inefficient and may not adequately meet users' overall

demands because it limits users to bid for each

computing instance individually instead of multipledifferent instances at a time.






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Data Disclosure due to laptop loss, especially in travel,

is a top threat to businesses, governments, and non-

profit organizations. An effective protection against

this threat should guarantee the data confidentiality,

even if the adversary has physically possessed the

laptop. Current technology does not satisfy this

requirement.

Cloud computing is an emerging computing paradigmwhich allows sharing of massive, heterogeneous,

elastic resources among users. Despite of all the hype

surrounding the cloud, users are still reluctant to adopt

cloud computing because public cloud services process

users' data on machines that users do not own hence

there is a fear of leakage of users' commercially

sensitive data. Due to these reasons, it is very

necessary that cloud users' be vigilant while selecting

the service providers present in the cloud.

Cloud computing has become another buzzword after

Web 2.0. However, there are dozens of different

definitions for cloud computing and there seems to beno consensus on what a cloud is. On the other hand,

cloud computing is not a completely new concept; it

has intricate connection to the relatively new but

thirteen-year established grid computing paradigm,

and other relevant technologies such as utility

computing, cluster computing, and distributed systems

in general.

Cloud computing is a promising technology, where the

infrastructure, developing platform, software and

storage are delivered as a service. With the

development of cloud computing, more and more

cloud service providers emerge. However, there are no

metrics can be referred to compare these providers, so

it is difficult for cloud consumers to select the most

reliable providers or resources.

Cloud computing discusses about sharing any

imaginable entity such as process units, storage

devices or software. The provided service is utterly

economical and expandable. Cloud computing

attractive benefits entice huge interest of both business

owners and cyber thefts. Consequently, the computer

forensic investigation step into the play to find

evidences against criminals. As a result of the newtechnology and methods used in cloud computing, the

forensic investigation techniques face different types

of issues while inspecting the case. The most profound

challenges are difficulties to deal with different rulings

obliged on variety of data saved in different locations,

limited access to obtain evidences from cloud and even

the issue of seizing the physical evidence for the sake

of integrity validation or evidence presentation.

Cloud computing bring a tremendous complexity to

information security. Many researchers have been

done to establish and maintain the trust relationship in

cloud. Remote attestation is one of the most importantfeatures of trusted computing. But conventional ways

of remote attestation can only attest to the presence of

a particular binary. They cannot measure program

behavior. Existing dynamic remote attestation

technologies can solve some of these problems. But

they are not suitable for cloud computing when users

lose their control over their critical data and business

processes.

A secure, reliable and economic power supply isclosely linked to a fast, efficient and dependable

communications infrastructure. The appliance of the

cloud computing model meets the requirements of data

and computing intensive smart grid applications.

Using internal network improves the calculation,

storage capacity, data security of the overall system,

reducing the system expansion investment, thus

providing ideas and strong technical support in smart

grid and large scale computing can be achieved over

existing network.

Cloud computing systems promise to offer

subscription-oriented, enterprise-quality computingservices to users worldwide. With the increased

demand for delivering services to a large number of

users, they need to offer differentiated services to users

and meet their quality expectations. Existing resource

management systems in data centers are yet to support

Service Level Agreement (SLA)-oriented resource

allocation, and thus need to be enhanced to realize

cloud computing and utility computing. In addition, no

work has been done to collectively incorporate

customer-driven service management, computational

risk management, and autonomic resource

management into a market-based resource

management system to target the rapidly changing

enterprise requirements of Cloud computing.

Over the recent years, Cloud Computing has evolved

as a new computing paradigm which aims at providing

high-quality, customized and dynamic computing

services. Despite initial positive results, it is

challenging in theory and practice to find an

appropriate provider matching the individual

requirements. For doing this, the customer has to be

clear about his individual targets that should be

achieved with cloud computing. That is quitchallenging because there are a lot more dimensions to

consider than costs and flexibility. Moreover, the

selection process is complicated by a number of new

entrants as well as offers of non-transparent services,

which sometimes differ significantly.

In universities, teaching and research require a large

number of scientific computing, and scientific

computing need to invest huge funds to purchase

hardware resources. As hardware replacement cycle is

very short, and the university departments often repeat

purchase of equipment, that resulting in low utilization

of resources and low sharing rate.






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Cloud computing is an evolving term these days. It

describes the advance of many existing IT

technologies and separates application and information

resources from the underlying infrastructure. Personal

Cloud is the hybrid deployment model that is

combined private cloud and public cloud. By and

large, cloud orchestration does not exist today. Current

cloud service is provided by web browser or hostinstalled application directly. According to the ITU-T

draft, we might consider cloud orchestration

environment in collaboration with other cloud

providers.

For many organizations, one attractive use of cloud

resources can be through what is referred to as cloud

bursting or the hybrid cloud. These refer to scenarios

where an organization acquires and manages in-house

resources to meet its base need, but can use additional

resources from a cloud provider to maintain an

acceptable response time during workload peaks.

Cloud bursting has so far been discussed in the contextof using additional computing resources from a cloud

provider. However, as next generation applications are

expected to see orders of magnitude increase in data

set sizes, cloud resources can be used to store

additional data after local resources are exhausted.

In recent years, as the rapid development of the

technology about Peer-to-Peer (P2P) networks and the

cloud computing technology, various applications of

P2P technology become very widespread in most

cloud computing distributed network applications. P2P

cloud computing networks are unstructured and are an

important component to implement next generation

internet. How to quickly and efficiently search the

resources in P2P networks has become one of the most

critical issues, and it is one of the greatest concerns to

users.

Cloud computing paradigm contains many shared

resources, such as infrastructures, data storage, various

platforms and software. Resource monitoring involves

collecting information of system resources to facilitate

decision making by other components in Cloud

environment. It is the foundation of many major Cloud

computing operations.Cloud computing is a trend which facilitates the

development of the distributed applications and

reduces the cost of the deployments, and it has

impacted the IT industry a lot. Cloud computing

depends a lot on the characteristics of the network, as

the remote processing and large data center are vital

for cloud computing. And the evolution of the

networks will play an important role for the evolution

of cloud computing. As many problems are emerging

in cloud computing, such as data security, data

availability and so on.

Cloud computing has emerged as one of the mostinfluential paradigms in the IT industry in recent years.

Since this new computing technology requires users to

entrust their valuable data to cloud providers, there

have been increasing security and privacy concerns on

outsourced data. Several schemes employing attribute-

based encryption (ABE) have been proposed for

access control of outsourced data in cloud computing;

however, most of them suffer from inflexibility in

implementing complex access control policies.In spite of the dramatic growth in the number of

smartphones in recent years, the challenge of limited

energy capacity of these devices has not been solved

satisfactorily. However, in the era of cloud computing,

the limitation on energy capacity can be eased off in an

efficient way by offloading heavy tasks to the cloud. It

is important for smartphone and cloud computing

developers to have insights into the energy cost of

smartphone applications before implementing the

offloading techniques.

Security issues are delaying fast adoption of cloud

computing and security mechanisms to ensure itssecure adoption has become a crucial immediate need.

On the other hand, cloud computing can help enable

security controls to be delivered in new ways by

service providers. To this end, we need frameworks for

efficient delivery of cloud-based security services and

for provisioning desirable solutions to customers based

on their requirements.

Cloud computing represents a paradigm shift, a

transition from computing-as-a-product to computing-

as-a-service. Instead of buying hardware and software

products, which require installation, configuration, and

maintenance, cloud computing lets you use

applications and computing infrastructures in the cloud

as a service, so you pay only for resources used.

Clouds thus offer businesses and individual access to

advanced IT infrastructures and applications that might

otherwise be out of their reach. Emerging markets

have been quick to recognize this and other benefits of

cloud computing.

The analysis and research of power system

necessitates the current computing. However, the

bottleneck of current computing lies in the limited

computing capacity in power system. Cloudcomputing's service-oriented characteristics advance a

new way of service provisioning called utility based

computing, which could provide powerful computing

capability for current computing. However, toward the

deployment of practical current computing Cloud, we

encounter one challenge that the existing job

scheduling algorithms under utility based computing

do not take hardware/software failure and recovery in

the Cloud into account.

Cloud Computing has emerged as a major information

and communications technology trend and has been

proved as a key technology for market developmentand analysis for the users of several field. The practice






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of computing across two or more data centers

separated by the Internet is growing in popularity due

to an explosion in scalable computing demands.

However, one of the major challenges that faces the

cloud computing is how to secure and protect the data

and processes the data of the user. The security of the

cloud computing environment is a new research area

requiring further development by both the academicand industrial research associations. While cloud-

bursting is addressing this process of scaling up and

down across data centers. To provide secure and

reliable services in cloud computing environment is an

important issue. One of the security issues is how to

reduce the impact of denial-of-service (DoS) attack or

distributed denial-of-service (DDoS) in this

environment.

The systems of Autonomic computing are the first to

be mixed up with cloud computing. This form of

computing differs in the way it works. The goal of

autonomic computing is to provide systems that workautonomous (White 2004). This means that they have

to be able to do self-managing. They must configure

and fix failures themselves. It is similar to cloud

computing because it also consists of large computer

systems that have a high-level guidance from humans.

The difference between cloud computing and grid

computing is more refined, but it is easy to explain.

Grid computing focuses on large scale whereas cloud

computing provides services for both smaller and

larger scale. Grid computing usually provides high

performance constantly, and (the major advantage of)

cloud computing provides the performance when

necessary (Buyya 2003). Another comparison is drawn

with mainframes; the difference might be clear with a

mainframe, but there also similarities. A mainframe

could be seen as a cloud. Though it is clear that a

mainframe provides access to employees in large

organization and the mainframe is completely

centralized. That is what differs with cloud computing,

as also is the performance. Mainframes provide

continuously high performance and cloud computing

only whenever necessary (Armbrust et al 2009). The

comparison also has been drawn with peer-to-peersystems. This is because there is a whole cloud of

users which are both client and servers (Stoica2002). This is also the difference. In cloud computing

clients themselves do not act as providers of any

service. The last comparison that is discussed is the

comparison with service oriented computing. Off

course cloud computing is service oriented. But

service oriented computing focuses more on

techniques that run in the SaaS. Cloud computing, as

mentioned several times before, focuses on providing

computing services rather than the techniques.

Dealing with "single cloud" providers is predicted tobecome less popular with customers due to risks of

service availability failure and the possibility of

malicious insiders in the single cloud. A movement

towards "multi-clouds", or in other words, "inter-

clouds" or "cloud-of-clouds" has emerged recently.Cloud computing is the development of parallel

computing, distributed computing and grid computing.

It has been one of the most hot research topics. Now

many corporations have involved in the cloudcomputing related techniques and many cloud

computing platforms have been put forward. This is a

favorable situation to study and application of cloud

computing related techniques. Though interesting,

there are also some problems for so many platforms.

For to a novice or user with little knowledge about

cloud computing, it is still very hard to make a

reasonable choice. What differences are there for

different cloud computing platforms and what

characteristics and advantages each has? To answer

these problems, the characteristics, architectures and

applications of several popular cloud computingplatforms are analyzed and discussed in detail. From

the comparison of these platforms, users can better

understand the different cloud platforms and more

reasonability choose what they want.

Cloud computing is a new way of delivering

computing resources and is not a new technology. It is

an internet based service delivery model which

provides internet based services, computing and

storage for users in all markets including financial

health care and government. This new economic

model for computing has found fertile ground and is

attracting massive global investment. Although the

benefits of cloud computing are clear, so is the need to

develop proper security for cloud implementations.

Cloud security is becoming a key differentiator and

competitive edge between cloud providers.

The cloud is a next generation platform that provides

dynamic resource pools, virtualization, and high

availability. Today, we have the ability to utilize

scalable, distributed computing environments within

the confines of the Internet, a practice known as cloud

computing. Cloud computing is the Concept

Implemented to decipher the Daily ComputingProblems, likes of Hardware Software and Resource

Availability unhurried by Computer users. The cloud

Computing provides an undemanding and Non

ineffectual Solution for Daily Computing. The

prevalent Problem Associated with Cloud Computing

is the Cloud security and the appropriate

Implementation of Cloud over the Network.

Cloud computing is evolving as a key computing

platform for sharing resources that include

infrastructures, software, applications, and business

processes. Virtualization is a core technology for

enabling cloud resource sharing. However, mostexisting cloud computing platforms have not formally






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adopted the service-oriented architecture (SOA) that

would make them more flexible, extensible, and

reusable. As an emerging technology and business

paradigm, Cloud Computing has taken commercial

computing by storm. Cloud computing platforms

provide easy access to a company's high-performance

computing and storage infrastructure through web

services. With cloud computing, the aim is to hide thecomplexity of IT infrastructure management from its

users. At the same time, cloud computing platforms

provide massive scalability, 99.999% reliability, high

performance, and specifiable configurability. These

capabilities are provided at relatively low costs

compared to dedicated infrastructures.

In the cloud computing system, the schedule of

computing resources is a critical portion of cloud

computing study. An effective load balancing strategy

is able to markedly improve the task throughput of

cloud computing. Virtual machines are selected as a

fundamental processing unit of cloud computing. Theresources in cloud computing will increase sharply and

vary dynamically due to the utilization of

virtualization technology. Current era of Web 2.0 is

enabling new business models for using the semantic

web. One such business model is leasing out

computing platform of hardware and software over the

internet to the tenants and is dubbed as Cloud

Computing. The anticipated future trend of computing

is believed to be this cloud computing as it promises a

lot of benefits like no capital expenditure, speed of

application deployment, shorter time to market, lower

cost of operation and easier maintenance for the

tenants.

Cloud computing is one of the emerging technologies

that will lead to the next generation of Internet. It

provides optimized and efficient computing through

enhanced collaboration, agility, scalability, and

availability.

Moreover, for instance if you have a company, you

can transfer internal network of your company yourserver database - on Cloud Computing to enjoy more

speed and processing power, and also if you use

server, you will economize in budget and only paypower consumption and maintenance costs.These are just part of the great performance of newtechnology, known as Cloud computing that is namedalso as "the next big thing" [1-9].

II. CONSIDERING HIGH IMPACTS OFCLOUD COMPUTING ON DIFFERENT

INDUSTRIES ASE OF USE

In two past sections of the paper, we define some of

the basic and fundamental principles of cloud and also

we tell about some of its advantageous. Now we wantimply into, the major applications of this technology.

After that when we understand the importance of this

technology, we tell about some techniques and

algorithms which can be uses for improving the

security aspect of such network; for example, we can

used Kalman Filter for prediction and estimation the

amount of users that can be allowed to logging into

special organization account.

On the other side there is indirect denial of service.This then affects other services when an attacker

means to hack a particular service down in the direct

denial of service. These effects depend on the

computing power the hacker has access to. If he tries

to cause downtime for a particular service (which is

hosted on a server) it could cause downtime for other

services too. The servers account all their computing

power to all the requests that are being made for one

specific service, and thus this causes that there is no

rest of computing power to access other applications in

the cloud on that particular server. Though it depends

on the infrastructure of the cloud, how bad the sideeffects are. For example the cloud could export the

service to another server when it notices that a

particular server is not able anymore to cope with all

the requests. This will cause even more downtime on

other services than before. When organizations use

cloud computing they shift the control of their security

partially to their cloud provider. They also have to

obey the rules that the provider makes up. The

unknown factor for cloud users is then that they do not

exactly know who provides the security measures in

the cloud. The cloud provider could easily hire a third

party in order to provide the security for the cloud.

This third party could be a liability for the security. It

means that there is another party that has access to the

information in the cloud and this party may be kept

unknown by the cloud provider. A major advantage

which is easily overlooked but also very important is

scale benefits (Armbrust, 2010; Grossman, 2009). Any

piece of software that gets installed in different places

has lower cost per installation than if this would be a

single one. This means that you have to invest less;

however in return you get the same quality as if you

would purchase something alone. This also providesthe possibility to increase the quality of purchased

software. You are able to spend the same amount of

financial resources as before, and you can get higher

quality. Cloud computing works by this principle, as

the provider purchases software, implements this into

the cloud, and then makes it ready for use for their

clients (Buyya, 2008). A very straight forward

advantage of cloud computing is the pay- as-you go

pricing, something we already mentioned when

defining the cloud. Logically one thinks of the cost

reduction for organizations with high IT expenses.

When you think further as Grossman (2009) describes,you will see that there are more benefits thanks to this






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cost model. For starting organizations there is a low

entrance barrier. Not only for purchasing this form of

IT, also for entering the market as a new organization.

They do not need to invest large amounts of money in

advance to get operable. The next advantage of this

pay as you go system is that the IT is easily scalable

and expandable. Another advantage is the bulk of data

that a cloud can handle. Storage of data can be realizedup to several petabytes (Grossman, 2009). This huge

amount of data is not (easy) process able by

conventional IT systems yet, as they are much smaller

and therefore unable to process a lot of data. Last is the

accessibility of the cloud. Traditional IT systems are

usually more bound and limited to a certain physical

area. In cloud computing this is not the case. The cloud

can be accessed by any person that has the rights to

access, and more importantly, it can be accessed from

anywhere (Armbrust, 2009). It does depend on the

security measures you take whether or not it is

accessible from all over the world.From now on, we wants to do a behavioral comparison

of two stage Kalman filtering technique for

surveillance permeating tracking in cloud computing,

with such a technique we can predict and update our

information about the interest of our users in using

different parts of cloud resources so that we able to

predict accidental phenomenas such as hanging ourcrashing of such resources; or even when we detect

hacker existence on such networks we be able to track

and finally wipe out the surveillance actions.

III. DYNAMICAL SYSTEM MODEL:Now, after general discussions about different aspects

of Cloud Computing, we want to present dynamical

system model for cloud computing which can be used

for estimation and prediction of the presence of

hackers and spyware actions and/or we can use this

modeling for estimation crowd on different hours. The

Kalman filters are based on linear dynamic systems

discredited in the time domain. They are modeled on a

Markov chain built on linear operators perturbed by

Gaussian noise. The state of the system is representedas a vector of real numbers. At each discrete time

increment, a linear operator is applied to the state to

generate the new state, with some noise mixed in, and

optionally some information from the controls on the

system if they are known. Then, another linear

operator mixed with more noise generates the

observed outputs from the true ("hidden") state. The

Kalman filter may be regarded as analogous to the

hidden Markov model, with the key difference that the

hidden state variables take values in a continuous

space (as opposed to a discrete state space as in the

hidden Markov model). Additionally, the hiddenMarkov model can represent an arbitrary distribution

for the next value of the state variables, in contrast to

the Gaussian noise model that is used for the Kalman

filter. There is a strong duality between the equations

of the Kalman Filter and those of the hidden Markov

model. A review of this and other models is given in

Roweis and Ghahramani (1999) and Hamilton (1994),

Chapter 13.

In order to use the Kalman filter to estimate theinternal state of a process given only a sequence of

noisy observations for example in cloud platform,one must model the process in accordance with the

framework of the Kalman filter. This means specifying

the following matrices: Fk, the state-transition model;

Hk, the observation model; Qk, the covariance of the

process noise; Rk, the covariance of the observation

noise; and sometimes Bk, the control-input model, for

each time-step, k, as described below.

The Kalman filter model assumes the true state at timekis evolved from the state at (k 1) according to

Where:

Fk is the state transition model which isapplied to the previous state xk1;

Bk is the control-input model which is appliedto the control vector uk;

Wk is the process noise which is assumed tobe drawn from a zero mean multivariate

normal distribution with covariance Qk.

At time k an observation (or measurement) zk of the

true state xk is made according to

Where Hk is the observation model which maps the

true state space into the observed space and vk is the

observation noise which is assumed to be zero mean

Gaussian white noise with covariance Rk.

The initial state, and the noise vectors at each step {x0,

w1, ..., wk, v1 ... vk} are all assumed to be mutually

independent.

Many real dynamical systems do not exactly fit this

model. In fact, unmodelled dynamics can seriously

degrade the filter performance, even when it was

supposed to work with unknown stochastic signals as

inputs. The reason for this is that the effect of

unmodelled dynamics depends on the input, and,

therefore, can bring the estimation algorithm to

instability (it diverges). On the other hand,

independent white noise signals will not make the

algorithm diverge. The problem of separating between

measurement noise and unmodelled dynamics is a

difficult one and is treated in control theory under the

framework of robust control.

The state of the filter is represented by two variables:






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, the a posteriori state estimate at time kgiven observations up to and including at time

k;

, the a posteriori error covariance matrix(a measure of the estimated accuracy of the

state estimate).

The Kalman filter can be written as a single equation;however it is most often conceptualized as two distinct

phases: "Predict" and "Update". The predict phase uses

the state estimate from the previous time step to

produce an estimate of the state at the current time

step. This predicted state estimate is also known as the

a priori state estimate because, although it is an

estimate of the state at the current time step, it does not

include observation information from the current time

step. In the update phase, the current a priori

prediction is combined with current observation

information to refine the state estimate. This improved

estimate is termed the a posteriori state estimate.Typically, the two phases alternate, with the prediction

advancing the state until the next scheduled

observation, and the update incorporating the

observation. However, this is not necessary; if an

observation is unavailable for some reason, the update

may be skipped and multiple prediction steps

performed. Likewise, if multiple independent

observations are available at the same time, multiple

update steps may be performed (typically with

different observation matrices Hk).

Predict:Predicted (a priori) state estimate:

Predicted (a priori) estimate covariance:

Update:

Innovation or measurement

residual:

Innovation (or residual) covariance

Optimal Kalman gain

Updated (a posteriori) state

Updated (a posteriori) estimate

covariance

The formula for the updated estimate and covariance

above is only valid for the optimal Kalman gain.

Usage of other gain values require a more complex

formula found in the derivations section.

Invariants:

If the model is accurate, and the values for and

accurately reflect the distribution of the initial

state values, then the following invariants are

preserved: (all estimates have a mean error of zero)

Where is the expected value of , and

covariance matrices accurately reflect the covariance

of estimates

IV. CONCLUSIONIn this article, we introduce Cloud Computing and

perusal about influences of it on the processes of these

days. As Cloud Computing begins to move beyond the

pure hype stage and into the beginning of mainstream

adoption, adopting cloud-based services or moving

application services to the cloud brings a number of

new risks, including: Cloud availability, Cloud

security, Erosion of data integrity, and so on.However, for enterprise which require visibility, trust

and control over cloud-based services. To maximize

the value of cloud computing, meanwhile, to avoid the

risk associated with their cloud-based

implementations, enterprises need an approach,

processes, procedures, and technology to manage and

control thousands of data, services and process

elements in the Cloud environment. In a word, Cloud

computing needs governance.

Cloud computing' service-oriented characteristics

advance a new way of service provisioning called

utility based computing. However, toward the practicalapplication of commercialized Cloud, we encounter

two challenges: i) there is no well-defined job

scheduling algorithm for the Cloud that considers the

system state in the future, particularly under

overloading circumstances; ii) the existing job

scheduling algorithms under utility computing

paradigm do not take hardware/software failure and

recovery in the Cloud into account.

Although, there is some worry about security in cloud

computing, but the number of persons that save their

personal information in servers of third company for

example Google, is increasing. We presented some

solutions for improving its security. With regard to lots






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of cloud computing advantages, specially, costs

reduction of implementation in large scale, investing

capital is increasing in this filed. Cloud Computing is

advancing with fast rate and also it will be complete

with little deficiencies rather than other technologies.

It is predict that Cloud computing is the basic platform

for IT in next 20 year [16].

REFERENCES:[1] David C. Wyld; the cloudy future ofgovernment

IT: cloud computing and the public sector aroundthe world, IJWesT, Vol. 1, Num. 1, Jan. 2010.

[2] Jean-Daniel Cryans, Alain April, Alain Abran;criteria to compare cloud computing with currentdatabase technology, R. Dumke et al. (Eds.):IWSM / MetriKon / Mensura 2008, LNCS 5338,pp. 114-126, 2008.

[3] Anil Madhavapeddy, Richard Mortier, JonCrowcroft, Steven Hand; multiscale notmulticore: efficient heterogeneous cloud

computing, published by the British InformaticsSociety Ltd. Proceedings of ACM-BCS Visions ofComputer Science 2010.

[4] Harold C. Lim, Shivnath Babu, Jeffrey S. Chase,Sujay S. Parekh; automated control in cloudcomputing: challenges and opportunities,ACDC09, June 19, Barcelona, Spain.

[5] N. Sainath, S. Muralikrishna, P.V.S. Srinivas; aframework of cloud computing in the real world;Advances in Computational Sciences andTechnology, ISSN 0973-6107, Vol. 3, Num. 2,(2010), pp. 175-190.

[6] Kyle Chard, Simon Caton, Omer Rana, KrisBubendorfer; social cloud: cloud computing insocial networks

[7] G. Bruce Berriman, Eva Deelman, Paul Groth,Gideon Juve; the application of cloud computingto the creation of image mosaics and managementof their provenance,

[8] Roy Campbell, Indranil Gupta, Michael Heath,Steven Y. Ko, Michael Kozuch, Marcel Kunze,Thomas Kwan, Kevin Lai, Hing Yan Lee, MarthaLyons, Dejan Milojicic, David OHallaron, YengChai Soh; open cirrus TM cloud computingtestbed: federated data centers for open sourcesystems and services research

[9] Rajkumar Buyya, Chee Shin Yeo, SrikumarVenugopal, James Broberg, Ivona Brandic; cloudcomputing and Emerging IT platforms: Vision,Hype, and Reality for delivering computing as the5th utility

[10]Lamia Youseff, Maria Butrico, Dilma Da Silva;toward a unified ontology of cloud computing

[11] Daniel A. Menasce, Paul Ngo; understandingcloud computing: experimentation and capacityplanning; Proc. 2009, Computer MeasurementGroup Conf. Dallas, TX. Dec. 2009.

[12] Won Kim; cloud computing: today andtomorrow; JOT, Vol. 8, No. 1, Jan-Feb 2009.

[13] Richard Chow, philippe Golle, Markus Jakobsson,Elaine Shi, Jessica Staddon, Ryusuke Masuoka,Jesus Molina; controlling data in the cloud:outsourcing computation without outsourcingcontrol; CCSW09, Nov. 13, 2009, Chicago,Illinois, USA.

[14] Bo Peng, Bin Cui, Xiaoming Li; implementationissues of a cloud computing platform; Bulletin ofthe IEEE computer society technical committee ondata engineering.

[15] Daniel Nurmi, Rich Wolski, Chris Grzegorczyk,Graziano Obertelli, Sunil Soman, Lamia Youseff,Dmitrii Zagorodnov; the eucalyptus open-sourcecloud computing system.

[16] Mehdi Darbandi, Hoda Purhosein; Perusal aboutinfluences of Cloud Computing on the processesof these days and presenting new ideas about itssecurity, Int. IEEE Conf. Azerbaijan, Bakku.

Mehdi Darbandi Mohammad

Abedi

Mohsen Panahi Ali Hamzenejad





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