Unabridged collation about multifarious computing methods and outreaching cloud computing based on innovative procedure

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Unabridged collation about multifarious computing methods and outreaching cloud computing based on innovative procedure
Mehdi Darbandi1, Mohammad Abedi1, Meysam Panahi2, Ali Hamzenejad 2, Mohsen Kariman Khorasani3
Department of Electrical Engineering and Computer Science at Iran University of Science and Technology (IUST), Tehran, Iran 2 Department of Management System and Productivity, Faculty of Industrial Engineering, Tehran South Branch, Islamic Azad University (IAU), Tehran, Iran 3 Department of Communication Engineering, Islamic Azad University, Gonabad Branch, Gonabad, Iran
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Abstract— Cloud computing is one of today's most exciting 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 find dynamical 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. KeywordsControl model, estimation and prediction. Kalman estimator,

I. INTRODUCTION Cloud 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, portability and 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 transmission and 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 the electric power enterprise informatization construction will be the private cloud computing, which will

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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 the requirement. 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 monitoring are 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 the network 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 the common 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 management is 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 of computing 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, reluctant to 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. Consumers purchase 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 all others. 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 the requirements 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 accounting systems 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. The representation 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 documentsharing 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) shortterm 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 multiple different 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 nonprofit 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 paradigm which 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 be no 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 new technology 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 important features 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 is closely 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 computing services 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 quit challenging 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 host installed 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 context of 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 most influential 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 attributebased 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 its secure 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 computingas-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. Cloud computing'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 development and 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 academic and industrial research associations. While cloudbursting 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 work autonomous (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-peer systems. This is because there is a whole cloud of users which are both “client” and “servers” (Stoica 2002). 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 to become 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, "interclouds" 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 cloud computing 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 computing platforms 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 Computing Problems, 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, most existing 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 the complexity 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. The resources 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 –your server database - on Cloud Computing to enjoy more speed and processing power, and also if you use server, you will economize in budget and only pay power consumption and maintenance costs. These are just part of the great performance of new technology, known as Cloud computing that is named also as "the next big thing" [1-9].
II.

CONSIDERING HIGH IMPACTS OF CLOUD 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 want imply 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 side effects 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 provides the 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 realized up 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 phenomena’s such as hanging our crashing 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.

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 the internal 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 time k is evolved from the state at (k − 1) according to Where:
  

Fk is the state transition model which is applied to the previous state xk−1; Bk is the control-input model which is applied to the control vector uk; Wk is the process noise which is assumed to be 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:

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 represented as 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 hidden Markov model can represent an arbitrary distribution

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

, the a posteriori state estimate at time k given 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:



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.

CONCLUSION

Update: Innovation residual:

or

measurement

Innovation (or residual) covariance Optimal Kalman gain

Updated (a posteriori) state

Updated (a posteriori) estimate covariance

In 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 practical application 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

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

JOURNAL OF COMPUTING, VOLUME 4, ISSUE 9, SEPTEMBER 2012, ISSN (Online) 2151-9617 https://sites.google.com/site/journalofcomputing WWW.JOURNALOFCOMPUTING.ORG

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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].

[15]

[16]

Daniel Nurmi, Rich Wolski, Chris Grzegorczyk, Graziano Obertelli, Sunil Soman, Lamia Youseff, Dmitrii Zagorodnov; “the eucalyptus open-source cloud computing system. Mehdi Darbandi, Hoda Purhosein; “Perusal about influences of Cloud Computing on the processes of these days and presenting new ideas about its security”, Int. IEEE Conf. Azerbaijan, Bakku.

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Mehdi Darbandi

Mohammad Abedi

Mohsen Panahi

Ali Hamzenejad

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