CloudComputing is a new IT consumer delivery model that aims to provide high availability and easy access [Li et al. 2009; Marta et al. 2011; Sheng-Yuan et al. 2010]. As referred to in the National Institute of Standards and Technology (NIST) definition of CloudComputing [NIST 2011], this model is composed of five essential characteristics: ―(On-demand self-service, Broad network access, Resource pooling, Rapid elasticity, Measured Service); three service models (Cloud Software as a Service (SaaS), Cloud Platform as a Service (PaaS), Cloud Infrastructure as a Service (IaaS)); and, four deployment models (Private cloud, Community cloud, Public cloud, Hybrid cloud). Key enabling technologies include: (1) fast wide-area networks, (2) powerful, inexpensive server computers, and (3) high-performance virtualization for commodity hardware .
Abstract—SMEs operate in competitive markets for business growth and sustainability. Information and Communication Technology (ICT) solutions have the potential to increase SMEs competitiveness thus contributes towards business sustainability. However SMEs mainly use the traditional ICT solutions to stay competitive. This leads to several challenges that they face in implementing ICT solutions, such as lack of capital, skilled staff, and so forth. The eminent challenge is that ICTs are expensive to procure and maintain. Considering these challenges, there is a need for cost-effective alternative ICT solutions that when implemented, can improve SMEs competitiveness. This paper investigates SMEs challenges regarding the adoption and use of traditional (on-premise) ICT solutions, and the potential of cloudcomputing as an alternative technology. The study adopted qualitative research approach, where case study research designs was used consisting of three SMEs. The data was collected through interviews, expert reviews, literature and questionnaires, where a total of 19 participants were used. The findings reveals that cloudcomputing happens to be a cost-effective alternative solution compared to traditional (on-premise) ICT solutions.
In 2011, Low et al. proposed a TOE framework for cloudcomputing adoption, and they defined their framework in three main contexts and eight factors. The organizational context includes Relative advantage, Complexity, Compatibility, Top management support, Firm size and Technology readiness, and the environmental context which can be integrated with Competitive pressure and Trading partner pressure .They investigated their framework for high-tech industry in Taiwan, and they showed that all specified factors had a positive impact on cloud adoption in high-tech industries, except complexity. However, unfortunately, this framework has been investigated in high-tech industry only, therefore maybe their framework may not show the same results on SMEs. Low et al. did not cover each factor exactly, with sufficient elements. Therefore organization which requires to adopt cloudcomputing, will not be able to apply this framework as a complete reference, due to this fact that this framework does not cover all the aspects which are necessary for deciding cloud adoption.
Besides that, many SMEs face a dilemma on whether to discard their current budget for investment or to opt for the cloudcomputing (Bhattacharjee, 2009). They believed that to migrate to the cloud services, they need to prepare to incur not only for cost of migration but also the cost of restructuring their company to fit for this new system. With the limited of budget, most of this SMEs prefer to choose to expanding their business rather than switching for cloud services. (Zhang, 2010). As a result of the limited financial resources available to most SMEs, it may be additionally more difficult to obtain necessary external expertise or additional training from vendors or IT providers (Dodgson, 2011).
During the last two decades, the use of Internet has been changing every domain of technology. It has also led to the tremendous development and implementation of cloudcomputing from the last few years. But the shared nature of data in the cloud makes it prone to security attacks. Different security techniques should be implemented to prevent security breaches. Authentication is one such technique which plays a major role in CloudComputing security. Various possible security attacks on the Cloud Service Providers (CSP) are prevented by applying different authentication mechanisms, which verifies a user’s identity when a user demands services from cloud servers. There are multiple authentication technologies for verifying the identity of a user before granting access to resources. In this paper we have discussed services provided by cloud and its brief analysis. Our mainly focus is on SaaS service which includes security issues and its solutions. We have mentioned various security prevention techniques which need to be considered when we want to implement SaaS.
3. Interoperability: In this CloudComputing adoption procedure by SMEs, it's critical to exist associate applicable level of capability between public and private clouds. Associate oversize styles of companies have created remarkably growth toward regulating their systems, processes and data by means of realization of ERPs. Standardization desires associate extensible infrastructure succeeding to a totally incorporated association among occurrences. Software as a Service (SaaS) applications delivered via the cloud acquire a low-capital, fast-deployment selection. Relying upon the applying, it is very important to integrate with existing applications which will be resident in an extremely separate cloud or on ancient technology. Customary is as associate enabler or associate obstacle for ability that every of them yield connect sincere maintenance of information and process‘s incorporation. , .
and its relationship to organisational performance. Coupled with the highly competitive business environment in Ghana, SMES are currently finding various ways to operate efficiently so as to cut cost and maximise profit and the researchers think cloudcomputing is the best option. Thus, in this new paradigm of computing, cloudcomputing has emerged to change the old ways of computing so as to bring about an increase in organisational performance. Cloudcomputing has emerged as one of the enabling technologies that allow the Information Technology world to use computer resource effectively and more efficiently. What this means is that users of the cloud have the luxury of unlimited computing power at the right time within the organisation to maximise profit. The authors Lebans and Euske (2006) defined organisational performance asindicators which provide information on the degree of achievement of organisational set objectives. This adoption of ICT concepts has influenced strategic management, (Nolan and McFarlan, 2005) increasing competitive advantage of the SMEs if applied by business aspects. Idea of strategic implementation of ICT principles including the adoption of cloudcomputing is based on the assumption that organizational performance is directly associated with the management’s competence to create strategic feedback between the market position of the organization and the design of the adequate support for achieving goals (Čudanov, Krivokapić and Krunić, 2011). The real benefit from the strategic implementation of any ICT concept, in this case, cloudcomputing paradigm, is achieved by the organisation's ability to make a long-term use of the ICT-based functionalities (Čudanov, 2011). In addition to major cloud infrastructure providers, (for example Amazon, Google
This is the age of information technology where accessing information is the key to success for all businesses. Information is not vital to large enterprises alone. The Small and Medium Industries (SMEs) will also have to be go-getters in accessing information. While accessing information is essential on one side, it is also important to store them and access them whenever required. Understanding this trend, the big and giant web based companies like Google, Amazon, Salesforce.com came with a model named "CloudComputing” the sharing of web infrastructure to deal with the internet data storage, scalability and computation (Kambi1, 2009). Cloudcomputing is a very useful tool for SMEs in India which constitute about 50% of the total industries and make a contribution of 70% to the GDP. In India 95% of the industrial units are SMEs which give over 50% of the industrial output (Popli and Rao, 2009). Thus SMEs form the backbone of the Indian economy Before we proceed to identify the opportunity and challenges faced by SMEs let us define and understand the concept of cloudcomputing. SMEs are said to be the lifeblood of any vibrant economy. They are known to be the silent drivers of a nation's economy. SMEs are leading the way for entering new global markets and for innovations in the emerging economic order. In India 95% of the industrial units are SMEs which give over 50% of the industrial output (Popli and Rao, 2009). Thus SMEs form the backbone of the Indian economy. SMEs of India are one of the most aggressive adopters of ERP Packages. Online services are better suited for small industries whereas large enterprises face more problems in implementation because of their complex functionalities and data security concerns (Dubey and Wagle, 2007).
Abstract — Cloudcomputing provides people the way to share distributed resources and services that belong to different organizations or sites. Since cloudcomputing share distributed resources via the network in the open environment, thus it makes security problems important for us to develop the cloudcomputing application. On one hand, an individual has full control on data and processes in his/her computer. On the other hand, we have the cloudcomputing wherein, the service and data maintenance is provided by some vendor which leaves the client/customer unaware of where the processes are running or where the data is stored. So the client has no control over it. The cloudcomputing uses the internet as the communication media. The vendor has to provide some assurance for security of data in the cloudcomputing. Organizations use cloudcomputing as a service infrastructure, critically like to examine the security and confidentiality issues for their business critical insensitive applications. Yet, guaranteeing the security of corporate data in the "cloud" is difficult, if not impossible, as they provide different services like Software as a service (SaaS), Platform as a service (PaaS), and Infrastructure as a service (IaaS). Each service has their own security issues. Data Protection Application Security Privacy is important security issues that have to be included in cloudcomputing. We propose a model system in which cloudcomputing system is combined with Cluster Load balancing, ssl over aes and secure session In this model, some important security services, including authentication, confidentiality and integrity, are provided in cloudcomputing system.
When IT systems are introduced into any new field, not just Archaeology, frequent bug fixes and upgrades are needed. In cloudcomputing, instead of an engineer having to visit an office to do this work, the maintenance work for hundreds of thousands or even millions of users can be done simply system in the Cloud center; this could solve many of the problems associated with maintenance. Moreover, in Cloudcomputing, there are no disparities in the software versions being used by different users, which leads to improved usability in addition to reduced maintenance problems.
Cloudcomputing is not a total new concept; it is originated from the earlier large-scale distributed computing technology. However, it will be a subversion technology and cloudcomputing will be the third revolution in the IT industry, which represent the development trend of the IT industry from hardware to software, software to services, distributed service to centralized service. Cloudcomputing is also a new mode of business computing, it will be widely used in the near future. Many companies are currently introducing their SaaS software service platforms to the market. In this paper, The overall objective of the project is to implement SaaS multitenancy in CloudComputing at single instance, create Scheduled Task and providing the services according to different layers. Customers will get the best software services by paying and getting license.
SMEs need not to think about experts and network, middleware and he abstraction layers to integrate which are rather demanding skills and proper knowledge in this area. Nevertheless this type of services helps to reduce cost for SMEs and helps consume their products instantly. Just couple of mouse click they are on line and pay just the services they consumes. How SMEs perceive the service models provided by the provider we raised this question about the preferable models that are suitable for SMEs application and data. Figure 7 shows the analyzed responses provided by the surveyed respondents.
• Front-Loading: In general, for each service to be used in a process model, its functionality and the BOs of its input and output parameters need to be pre- speci ﬁ ed, and which IT system shall offer this service. Using the LPD, a business process modeler may then use this information. Different systems may store different attributes for BOs, i.e. those they need. To foster front-loading, the Logistics Mall MMP  supports describing offered apps in business view that is pre-linked to the technical descriptions needed for service execution, and service governance is de ﬁ ned. Technical descriptions like WSDLs and XSD data type speci ﬁ cations are used, but they are not comprehensible to business process designers having no or only limited IT skills and are therefore hidden from them. • Look-Ahead: Usually, service descriptions and service operations are published in a repository (SOA repository or LDAP in our case). To enable look-ahead, a service should be described in a language (or graphical notation) easily understandable to business process designers , as described above. Fur- thermore, it must be easy to search for needed services and to access and understand related descriptions. Based on this, business process designers can ﬁ nd appropriate services and use them in corresponding process steps. Existing technical service speci ﬁ cations are pre-linked with these business level speci- ﬁ cations to avoid unnecessary implementation steps. Reuse of existing services reduces efforts and costs for service implementation or service renting. As a disadvantage, adjustments of the de ﬁ ned process logic to the available service set might become necessary.
Both cloudcomputing and SOA share some core principles. First, both rely on the service concept to achieve the objectives. Service is a functionality or a fea- ture offered by one entity and used by another. For example, a service could be retrieving the details of the online bank account of a user. SOA and cloudcomputing use service delegation in that the required task is delegated either to service provider (in the case of cloudcomputing) or to other application or business components in the enterprise (in the case of SOA). Service delega- tion helps the people to use the services without being concerned about the implementation and maintenance details. Services could be shared by multi- ple applications and users, thereby achieving optimized resource utilization. Second, both cloudcomputing and SOA promote loose coupling among the components or services, which ensures the minimum dependencies among different parts of the system. This feature reduces the impact that any single change on one part of the system makes on the performance of the overall system. Loose coupling helps the implemented services to be separated and unaware of the underlying technology, topology, life cycle, and organiza- tion. The various formats and protocols used in distributed computing, such as XML, WSDL, Interface Description Language (IDL), and Common Data Representation (CDR), help to achieve the encapsulation of technology dif- ferences and heterogeneity among the various components used for combin- ing a business solution for solving the computing problems. Various services should be location and technology independent in cloudcomputing, and SOA can be used for achieving this transparency in the cloud domain.
adopted cloudcomputing because of this factor. Data storage facilities are flexible on clouds, as SMEs have to pay only for that volume of space which they consume. In this way, they can increase or decrease storage space whenever required to meet the business requirement. Therefore, cloudcomputing provides a relative advantage to IT business which requires the availability of resources on demand to experience rapid growth. In the case of any maintenance requirement, SMEs do not have to pay any expenses because it is a responsibility of the vendors to update, upgrade and maintain a cloud. Moreover, it enables them to compete in the market by offering a cost reduction structure and advance IT solutions. Most of the SMEs saved a significant amount of their IT expenses after the adoption of cloudcomputing in very less time. Major cloud providers in the industry offer guarantee for their clouds and risk of data unavailability is also minimized because of cloudcomputing. In this way, the SMEs have more trust on cloud providers which allows them to use their storage.
Routing policy method. In terms of predicate index and physical opera- tors, eddy  is an efficient routing policy to continuously reorder the application of pipelined operators in a query plan. Given a set of input streams, the approach routes tuples of each stream to operators and the operators run as independent threads to return tuples to the eddy. Once the tuples have been handled by all the operators, the eddy will send the result to the output. During query processing, three properties of run-time fluctuations, respectively, are the costs of operators, their selectivities, and the rates at which the tuples arrive from the inputs. The implementation used two ideas for routing: the first approach, called Backpressure, limits the size of the input queues of operators, capping the rate at which the eddy can route tuples to slow operators. This causes more tuples to be routed to fast operators early in query execution. The second approach augments backpressure with a ticket scheme, whereby the eddy gives a ticket to an operator whenever it consumes a tuple and takes a ticket away whenever it sends a tuple back to the eddy. In this way, higher selectivity operators accumulate more tickets. The priority scheme learning the varied selectiv- ity is implemented via lottery scheduling, which is a novel randomized resource allocation mechanism and probabilistically fair. Each time the eddy gives a tuple to an operator, it credits the operator one “ticket.” A sin- gle physical ticket may represent any number of logical tickets. This is sim- ilar to monetary notes, which may be issued in different denominations. Lottery tickets encapsulate resource rights that are abstract, relative, and uniform. When an eddy plans to send a tuple to be processed, it “holds a lottery” among the operators eligible for receiving the tuple. The num- ber of lotteries won by a client has a binomial distribution. The chance of winning a lottery and receiving the tuple for an operator corresponds to the owned count of tickets. Meanwhile, by characterizing the moments of symmetry and the synchronization barriers, the eddy tracks an order- ing of the operators that improves the overall efficiency using the lottery scheme.
Abstract The surging demand for inexpensive and scalable IT infrastructures has led to the widespread adoption of Cloudcomputing architectures. These architec- tures have therefore reached their momentum due to inherent capacity of simplifi ca- tion in IT infrastructure building and maintenance, by making related costs easily accountable and paid on a pay-per-use basis. Cloud providers strive to host as many service providers as possible to increase their economical income and, toward that goal, exploit virtualization techniques to enable the provisioning of multiple virtual machines (VMs), possibly belonging to different service providers, on the same host. At the same time, virtualization technologies enable runtime VM migration that is very useful to dynamically manage Cloud resources. Leveraging these fea- tures, data center management infrastructures can allocate running VMs on as few hosts as possible, so to reduce total power consumption by switching off not required servers. This chapter presents and discusses management infrastructures for power- effi cient Cloud architectures. Power effi ciency relates to the amount of power required to run a particular workload on the Cloud and pushes toward greedy con- solidation of VMs. However, because Cloud providers offer Service-Level Agreements (SLAs) that need to be enforced to prevent unacceptable runtime per- formance, the design and the implementation of a management infrastructure for power-effi cient Cloud architectures are extremely complex tasks and have to deal with heterogeneous aspects, e.g., SLA representation and enforcement, runtime reconfi gurations, and workload prediction. This chapter aims at presenting the cur- rent state of the art of power-effi cient management infrastructure for Cloud, by care- fully considering main realization issues, design guidelines, and design choices. In addition, after an in-depth presentation of related works in this area, it presents some novel experimental results to better stress the complexities introduced by power-effi cient management infrastructure for Cloud.
Flexibility, Reliability and More Storage - The data store on cloud are almost unlimited bandwidth and storage space and Remote cloud servers offer it. It’s easier to makes data backup and disaster recovery because the cloud data can be store a copy of these data at multiple redundant sites on the cloud provider’s network. During these process no need to spend large money so it less expensive. Cloudcomputing has increased the storage or Cloudcomputing provides a large storage, so we will not have to worry about the end of the store at the hard drive now.
Services such as Gmail, Google Drive, Google Calendar, Picasa, and Google Groups are free of charge for individual users and available for a fee for organizations. These services are running on a cloud and can be invoked from a broad spectrum of devices, including mobile ones such as iPhones, iPads, Black-Berrys, and laptops and tablets. The data for these services is stored in data centers on the cloud. The Gmail service hosts emails on Google servers and, provides a Web interface to access them and tools for migrating from Lotus Notes and Microsoft Exchange. Google Docs is Web-based software for building text documents, spreadsheets, and presentations. It supports features such as tables, bullet points, basic fonts, and text size; it allows multiple users to edit and update the same document and view the history of document changes; and it provides a spell checker. The service allows users to import and export files in several formats, including Microsoft Office, PDF, text, and OpenOffice extensions.
As an emerging state-of-the-art technology, cloudcomputing has been applied to an extensive range of real-life situations. Health care service is one of such important application fields. We developed a ubiquitous health care system, named HCloud, after comprehensive evaluation of requirements of health care applications. It is provided based on a cloudcomputing plat- form with characteristics of loose coupling algorithm modules and powerful parallel computing capabilities that compute the details of those indicators for the purpose of preventive health care service. First, raw physiological sig- nals are collected from the body sensors by wired or wireless connections and then transmitted through a gateway to the cloud platform, where storage and analysis of the health status are performed through data-mining tech- nologies. Last, results and suggestions can be fed back to the users instantly for implementing personalized services that are delivered via a heteroge- neous network. The proposed system can support huge physiological data storage; process heterogeneous data for various health care applications, such as automated electrocardiogram (ECG) analysis; and provide an early warn- ing mechanism for chronic diseases. The architecture of the HCloud platform for physiological data storage, computing, data mining, and feature selections is described. Also, an online analysis scheme combined with a Map-Reduce parallel framework is designed to improve the platform’s capabilities. Performance evaluation based on testing and experiments under various conditions have demonstrated the effectiveness and usability of this system.