conduct an access control. More likely, users want to control the privileges of data manipulation over other users or cloud servers. This is because when sensitive information or computation is outsourced to the cloud servers or another user, which is out of users‟ control in most cases, privacy risks would rise dramatically because the servers might illegally inspect users‟ data and access sensitive information, or other users might be able to infer sensitive information from the outsourced computation. Therefore, not only the access but also the operation should be controlled. Secondly, personal information (defined by each user‟s attributes set) is at risk because one‟s identity is authenticated based on his information for the purpose of access control (or privilege control in this paper). As people are becoming more concerned about their identity privacy these days, the identity privacy also needs to be protected before the cloud enters our life. Preferably, any authority or server alone should not know any client‟s personal information. Last but not least, the cloudcomputing system should be resilient in the case of security breach in which some part of the system is compromised by attackers.
Task scheduling is the process of allocating the resources to the particular job in specific time. The objective of scheduling is to maximize the resource utilization. Minimizing the waiting time is the goal of scheduling. A betterscheduling algorithm yields better system performance. In the cloud there are numerous and distinct resources available. The cost of performing tasks in cloud depends on which resources are being used so the scheduling in a cloud environment is different from the traditional scheduling. In a cloudcomputing environment task scheduling is a biggest and challenging issue. Task scheduling problem is an NP-Complete problem. A lot of heuristic scheduling algorithms hasbeen introduced, but more improvement is needed to make the system faster and more responsive. The traditional scheduling algorithms like FCFS, SJF, RR, Min-Min, and Max-Min algorithms are not the much better solution to scheduling problems with cloudcomputing. So we need the better solution to this heuristic problem.
• 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.
Two of the main customer objections actually pose potential opportunities for channel firms if they are handled well. Integration concerns about tying cloud into existing infrastructure and worries about data portability need not be deal breakers – instead they provide a chance for the solution provider to flex their value, knowledge and skill set. Being able to explain to a potential customer in detail which party ultimately “owns” data placed in the cloud, particularly in a situation where a cloud provider might go out of business or the customer falls behind on payments, demonstrates the channel firm’s knowledge of cloud-‐based models. Data portability moves from a sales obstacle to overcome to a value-‐added service to sell. Likewise with integration. For channel firms selling cloud today, the greatest source of revenue after the sale lies in integration work – cloud to on-‐premise and cloud to cloud. A proven track record here with existing customers can serve as a blueprint or proof point to persuade more reluctant customer prospects, much like case studies are used.
Cloud multimedia rendering as a service  is a promising category that has the potential of significantly enhancing the user multimedia experience. Despite the growing capacities of mobile devices, there is a broadening gap with the increasing requirements for 3D and multiview rendering tech- niques. Cloud multimedia rendering can bridge this gap by conducting rendering in the cloud instead of on the mobile device. Therefore, it poten- tially allows mobile users to experience multimedia with the same qual- ity available to high-end PC users . To address the challenges of low cloud cost and network bandwidth and high scalability, Wang et al.  pro- posed a rendering adaptation technique, which can dynamically vary the richness and complexity of graphic rendering depending on the network and server constraints, thereby impacting both the bit rate of the rendered video that needs to be streamed back from the cloud server to the mobile device and the computation load on the cloud servers. Zhu et al.  empha- sized that the cloud equipped with GPU can perform rendering due to its strong computing capability. They categorized two types of cloud-based rendering: (1) to conduct all the rendering in the cloud and (2) to conduct only computation-intensive part of the rendering in the cloud while the rest would be performed on the client. More specifically, an MEC with a proxy can serve mobile clients with high QoE since rendering (e.g., view interpolation) can be done in the proxy. Research challenges include how to efficiently and dynamically allocate the rendering resources and design a proxy for assisting mobile phones on rendering computation.
Several new AWS services were introduced in 2012; some of them are in a beta stage at the time of this writing. Among the new services we note: Route 53, a low-latency DNS service used to manage user’s DNS public records; Elastic MapReduce (EMR), a service supporting processing of large amounts of data using a hosted Hadoop running on EC2 and based on the MapReduce paradigm discussed in Section 4.6; Simple Workﬂow Service (SWF), which supports workflow management (see Section 4.4) and allows scheduling, management of dependencies, and coordination of multiple EC2 instances; ElastiCache, a service enabling Web applications to retrieve data from a managed in-memory caching system rather than a much slower disk-based database; DynamoDB, a scalable and low-latency fully managed NoSQL database service; CloudFront, a Web service for content delivery; and Elastic Load Balancer, a cloud service to automatically distribute the incoming requests across multiple instances of the application. Two new services, the Elastic Beanstalk and the CloudFormation, are discussed next.
Starting in 1958 the agency, then known as ARPA, was responsible for carrying out research and development on projects at the cutting edge of science and technology. While these typically dealt with national security–related matters, the agency never felt bound by military projects alone. One outcome of this view was significant work on general information technology and computer systems, starting with pioneering research on what was called time-sharing. The first computers worked on a one user–one system principle, but because individuals use computers intermittently, this wasted resources. Research on batch processing helped to make computers more efficient because it permitted jobs to queue up over time and thereby shrunk nonusage time. Time-sharing expanded this by enabling multiple users to work on the same system at the same time. DARPA kick-started time-sharing with a grant to fund an MIT-based project that, under the leadership of J. C. R. Licklider, brought together people from Bell Labs, General Electric, and MIT (Waldrop 2002). With time-sharing was born the principle of one system serving multiple users, one of the foundations of cloudcomputing. The thirty or so companies that sold access to time-sharing computers, including such big names as IBM and General Electric, thrived in the 1960s and 1970s. The primary operating system for time-sharing was Multics (for Multiplexed Information and Computing Service), which was designed to operate as a computer utility modeled after telephone and electrical utilities. Specifically, hardware and software were organized in modules so that the system could grow by adding more of each required resource, such as core memory and disk storage. This model for what we now call scalability would return in a far more sophisticated form with the birth of the cloud- computing concept in the 1990s, and then with the arrival of cloud systems in the next decade. One of the key similarities, albeit at a more primitive level, between time-sharing systems and cloudcomputing is that they both offer complete operating environments to users. Time-sharing systems typically included several programming-language processors, software packages, bulk printing, and storage for files on- and offline. Users typically rented terminals and paid fees for connect time, for CPU (central processing unit) time, and for disk storage. The growth of the microprocessor and then the personal computer led to the end of time-sharing as a profitable business because these devices increasingly substituted, far more conveniently, for the work performed by companies that sold access to mainframe computers.
SDN has two main advantages over traditional networks in regards to detection and response to attacks: (1) the (logically) centralized management model of SDN allows administrators to quickly isolate or block attack traffic patterns without the need to access and reconfigure several heterogeneous hardware (switches, routers, firewalls, and intrusion detection systems); (2) detection of attacks can be made a distributed task among switches (SDN controllers can define rules on switches to generate events when flows considered malicious are detected), rather than depending on expensive intrusion detection systems. SDN can also be used to control how traffic is directed to network monitoring devices (e.g., intrusion detection systems) as proposed in . Quick response is particularly important in highly dynamic cloud environments. Traditional intrusion detection systems (IDS) mainly focus on detecting suspicious activities and are limited to simple actions such as disabling a switch port or notifying (sending email) to a system administrator. SDN opens the possibility of taking complex actions such as changing the path of suspicious activities in order to isolate them from known trusted communication. Research will focus on how to recast existing IDS mechanisms and algorithms in SDN contexts, and development of new algorithms to take full advantage of multiple points of action. For example, as each switch can be used to detect and act on attacks,  has shown the improvement of different traffic anomaly detection algorithms (Threshold Random Walk with Credit Based rate limiting, Maximum Entropy, network traffic anomaly detection based on packet bytes, and rate limiting) using Openflow and NOX by placing detectors closer to the edge of the network (home or small business networks instead of the ISP) while maintaining the line rate performance.
In simple language, mobile commerce is the mobile version of e-commerce. Each and every utility of e-commerce is possible through mobile devices using the computa- tion and storage in the cloud. According to Wu and Wang , mobile commerce is “the delivery of electronic commerce capabilities directly into the consumer’s hand, anywhere, via wireless technology.” There are plenty of examples of mobile com- merce, such as mobile transaction and payment, mobile messaging and ticketing, mobile advertising and shopping, and so on. Wu and Wang  further report that 29% of mobile users have purchased through their mobiles 40% of Walmart products in 2013, and $67.1 billion purchases will be made from mobile device in the United States and Europe in 2015. This statistics proves the massive growth of m-commerce. In m- commerce, the user’s privacy and data integrity are vital issues. Hackers are always trying to get secure information such as credit card details, bank account details, and so on. To protect the users from these threats, public key infrastructure (PKI) can be used. In PKI, an encryption-based access control and an over-encryption are used to secure the privacy of user’s access to the outsourced data. To enhance the customer sat- isfaction level, customer intimacy, and cost competitiveness in a secure environment, an MCC-based 4PL-AVE trading platform is proposed in Dinh et al. .
Pipe-and-Filter Style. The pipe-and-filter style is a variation of the previous style for expres- sing the activity of a software system as sequence of data transformations. Each component of the processing chain is called a filter, and the connection between one filter and the next is represented by a data stream. With respect to the batch sequential style, data is processed incrementally and each filter processes the data as soon as it is available on the input stream. As soon as one filter produces a consumable amount of data, the next filter can start its processing. Filters generally do not have state, know the identity of neither the previous nor the next filter, and they are connected with in-memory data structures such as first-in/first-out (FIFO) buffers or other structures. This par- ticular sequencing is called pipelining and introduces concurrency in the execution of the filters. A classic example of this architecture is the microprocessor pipeline, whereby multiple instructions are executed at the same time by completing a different phase of each of them. We can identify the phases of the instructions as the filters, whereas the data streams are represented by the registries that are shared within the processors. Another example are the Unix shell pipes (i.e., cat , file- name .j grep , pattern .j wc l), where the filters are the single shell programs composed together and the connections are their input and output streams that are chained together. Applications of this architecture can also be found in the compiler design (e.g., the lex/yacc model is based on a pipe of the following phases: scanning j parsing j semantic analysis j code generation), image and signal processing, and voice and video streaming.
Desktops are another computing resource that can be virtualized. Desktop virtualization is enabled by several architectures that allow remote desktop use, including the X Window System and Microsoft Remote Desktop Services. The X Window System, also known as X Windows, X, and X11, is an architecture commonly used on Linux, UNIX, and Mac OS X that abstracts graph- ical devices to allow device independence and remote use of a graphical user interface, including display, keyboard, and mouse. X does not include a windowing system—that is delegated to a window manager, such as KDE or Gnome. X is based on an MIT project and is now managed by the X.Org Foundation. It is available as open source software based on the MIT license. X client applications exist on Linux, UNIX, Mac OS X, and Windows. The X server is a native part of most Linux and UNIX systems and Mac OS X and can be added to Windows with the Cygwin platform. The X system was designed to separate server and client using the X protocol and lends itself well to cloudcomputing. X Windows is complex and can involve some troubleshooting, but because it supports many varied scenarios for its use, it has enjoyed a long life since it was first developed in 1984.
The next layer within ITaaS is Platform as a Service, or PaaS. At the PaaS level, what the service providers offer is packaged IT capability, or some logical resources, such as databases, ﬁle systems, and application operating environment. Currently, actual cases in the industry include Rational Developer Cloud of IBM, Azure of Microsoft and AppEngine of Google. At this level, two core technolo- gies are involved. The ﬁrst is software development, testing and running based on cloud. PaaS service is software developer-oriented. It used to be a huge difﬁculty for developers to write programs via network in a distributed computing environ- ment, and now due to the improvement of network bandwidth, two technologies can solve this problem: the ﬁrst is online development tools. Developers can directly complete remote development and application through browser and remote console (development tools run in the console) technologies without local installation of development tools. Another is integration technology of local development tools and cloudcomputing, which means to deploy the developed application directly into cloudcomputing environment through local development tools. The second core technology is large-scale distributed application operating environment. It refers to scalable application middleware, database and ﬁle system built with a large amount of servers. This application operating environment enables appli- cation to make full use of abundant computing and storage resource in cloudcomputing center to achieve full extension, go beyond the resource limitation of single physical hardware, and meet the access requirements of millions of Internet users.
The term Web 3.0, also known as the semantic web, describes sites wherein the computers will be generating raw data on their own without direct user inter- action. Web 3.0 is considered as the next logical step in the evolution of the Internet and web technologies. For Web 1.0 and Web 2.0, the Internet is con- fined within the physical walls of the computer, but as more and more devices such as smartphones, cars, and other household appliances become connected to the web, the Internet will be omnipresent and could be utilized in the most efficient manner. In this case, various devices will be able to exchange data among one another and they will even generate new information from raw data (e.g., a music site, Last.fm, will be able to anticipate the type of music the user likes depending on his previous song selections). Hence, the Internet will be able to perform the user tasks in a faster and more efficient way, such as the case of search engines being able to search for the actual interests of the indi- vidual users and not just based on the keyword typed into the search engines. Web 3.0 embeds intelligence in the entire web domain. It deploys web robots that are smart enough of taking decisions in the absence of any user interference. If Web 2.0 can be called a read/write web, Web 3.0 will surely be called a read/write/execute web. The two major components forming the basis of Web 3.0 are the following:
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.
Data transfer performance is a critical factor when considering the deployment of a data-intensive processing pipeline on a distributed topology. In fact, an important part of the DESDynI pre-mission studies consisted in using the deployed array of cloud servers to evaluate available data transfer technologies, both in terms of speed and easiness of installation and confi guration options. Several data transfer toolkits were compared: FTP (most popular, used as performance baseline), SCP (ubiquitous, built- in SSH security, potential encryption overhead), GridFTP (parallelized TCP/IP, strong security, but complex installation and confi guration), bbFTP (parallelized TCP/IP, easy installation, standalone client/server), and UDT (reliable UDP-based bursting technology). Benchmarking was accomplished by transferring NetCDF fi les (a highly compressed format commonly used in Earth sciences) of two representative sizes (1 and 10 GB) between JPL, two cloud servers in the AWS-West region, and one cloud server in the AWS-East region. The result was that UDT and GridFTP offered the overall best performance across transfer routes and fi le sizes: UDT was slightly faster and easier to confi gure than GridFTP, but it lacked the security features (authen- tication, encryption, confi dentiality, and data integrity) offered by GridFTP. It was also noticed that the measured transferred times varied considerably when repeated for the same fi le size and transfer end points, most likely because of concurrent use of network and hardware resources by other projects hosted on the same cloud. Additionally, using the Amazon internal network, when transferring data between servers on the same AWS-West region consistently, yielded much better performance than when using the publicly available network between the same servers.
Cloud i.e. the internet and computing i.e. use of computer technology. Then, Cloudcomputing is a internet basedcomputing. Using cloudcomputing any user can have an access over the internet to database resources and different services as long as one needs without worrying about the maintenance and storage of data. Moreover, an important aim of cloudcomputing has been delivering computing as a utility. Utility computing describes a business model for on-demand delivery of computing power; consumers pay Providers based on usage.
Zumerle says most enterprise users are comfortable with the mobile com- munication security they perceive, though recent events have “caused a slight surge in Gartner inquiries for so- lutions that provide voice and texting privacy.” Casper believes the common user doesn’t differentiate various threats. “Mobile communications are created by a whole ecosystem consisting of domestic and international carriers, device manufacturers with open or closed technical systems, operating sys- tems and applications, but also wireless hotspots, home networks, and Web- based servers from banks, e-commerce shops, and others,” he says. Every party is interested in protecting some of users’ information (primarily for reputation and legal reasons) but also in exploiting some to finance products and services, he says.
Enterprises often don’t have the required expertise to build cloud-based solutions. The average medium-to-large company that has been in business for more than a few years typically has a collection of applications and services spanning multiple eras of application architecture from mainframe to client-server to commercial-off the-shelf and more. The majority of the skills internally are specialized around these different architectures. Often the system administrators and security experts have spent a lifetime working on physical hardware or on-premises virtualization. Cloud architectures are loosely coupled and stateless, which is not how most legacy applications have been built over the years. Many cloud initiatives require integrating with multiple cloud-based solutions from other vendors, partners, and customers. The methods used to test and deploy cloud-based solutions may be radically different and more agile than what companies are accustomed to in their legacy environments. Companies making a move to the cloud should realize that there is more to it than simply deploying or paying for software from a cloud vendor. There are significant changes from an architectural, business process, and people perspective. Often, the skills required to do it right do not exist within the enterprise.
The CloudComputing has emerged as a latest domain in terms of technology as well as research interests. Cloudcomputing is also known as fifth utility (along with water, electricity, gas and telephone) which is available as per the demand of the user. CloudComputing is based on pay as per the use model. In this, a cloudcomputing model provides online computing service on demand as required by the user. Cloudcomputing is a fifth generation computing truly based on service provisioning based on virtualization. The cloudcomputing model believes in providing various benefits like fast deployment, pay-for-use, lower costs, scalability, rapid provisioning, rapid elasticity, ubiquitous network access, greater resiliency. It also provides hypervisor protection against network attacks, disaster recovery in minimal cost and various solutions to data storage, on- demand security controls, and real time detection of system tampering and rapid re-constitution of services.
The Requesting Platforms are based on Standards such as SQL, SOA and WEB. This research study focuses on the use of SOA as a component for the convergence of EA, SOA and CloudComputing. SOA is an architecture that allows for the structuring of an application that goes through identification of a number of functional units which can be reused many times for the delivery of an application service. Web Services provide developers methods of integrating. With the emergence of cloudcomputing, researchers are finding a place for SOA in the transition of existing applications to cloud services. This no doubt will be leveraged by the protocols that emerged as part of the web 2.0 technologies, especially those that had been based on SOAP (simple object access protocol). SOAP is an XML (Extensible Mark-up Language) based on open source message transport protocol). SOA is also a business-driven IT architectural approach that supports integrating a business as linked, repeatable tasks or services.