Cloudcomputing is a revolutionary computing paradigm, which enables flexible, on-demand, and low-cost usage of computing resources, but the data is outsourced to some cloud servers, and various privacy concerns emerge from it. Various schemes based on the attribute- based encryption have been proposed to secure the cloud storage. However, most work focuses on the data contents privacy and the access control, while less attention is paid to the privilege control and the identity privacy. In this paper, we present a semi anonymous privilege control scheme AnonyControl to address not only the data privacy, but also the user identity privacy in existing access control schemes. AnonyControl decentralizes the central authority to limit the identity leakage and thus achieves semi anonymity.Besides, it also generalizes the file access control to the privilege control, by which privileges of all operations on the cloud data can be managed in a fine-grained manner. Subsequently, we
Abstract - The cloudcomputing can be simply stated as delivery of computing environment where different resources are delivered as a service to the customer or multiple tenants over the internet. The task scheduling mainly focuses on enhancing the efficient utilization of resources and hence reduction in task completion time. Task scheduling is used to allocate certain tasks to particular resources at a particular time instance. Many different techniques have been presented to solve the problems of scheduling of numerous tasks. Task scheduling improves the efficient utilization of resource and yields less response time so that the execution of submitted tasks takes place within a possible minimum time. This paper discusses the analysis of priority, length and deadline based task scheduling algorithms used in cloudcomputing.
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.
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.
may be legally problematic and dangerous. Uncerti ﬁ ed code provided by the cus- tomer could harm the system health of the Cloud infrastructure or contain vul- nerabilities of any kind. Thus a Business Process designed and used in the Cloud may only consist of prede ﬁ ned and by the Cloud operator certi ﬁ ed building blocks. Designing a Business Process goes through some development steps, well known from software development techniques (cf. Fig. 1). In the beginning a Process is typically designed in a graphical notation. This step is iteratively per- formed and can be accompanied by internal simulation phases, which help the designer evaluating his newly created or altered Process model. The design phase is usually followed by a test phase. During the test phase a Process model can be executed. If necessary the Process model and depending artifacts ﬁ rst have to be deployed. In most Execution environments the deployment of new Process models is simply based on XML ﬁ les. More complex is the preparation of all services referenced by a Process model. If a service hasn ’ t been used by a Process model before, it must be installed and con ﬁ gured before usage. Often the Process model has to be updated to re fl ect the current IP or URL of the newly deployed service. An execution of a Process in the test phase must be clearly marked preventing misinterpretations of Process data and incoming or outgoing signals to external systems. Any error found while testing may lead to another design phase. After a successful test phase the Process changes to the productive phase and can be used as desired. Any errors found in the productive phase will also lead to a new design phase. At the end of its lifetime a Process model is undeployed, saving computing resources and preventing the creation of new Process instances. 10 The physical Fig. 1 Business Process
At this stage you’re in the early days, so you’ll have to use ball-park estimates. Don’t fl ash up a bill of materials part-code breakdown; keep it neat and concise. We suggest the high-line items shown in Table 2.2, but be prepared to break them out, depending on your audience (see our discussion on backup slides in a moment). If you have been to a supplier and got ball-park pricing, great; otherwise, use your judgment. But be careful—there is massive scope for dis- counting on infrastructure purchases from suppliers. At this stage you should keep it to list pricing but clearly state that and mention in the delivery that you expect discounts of X% (based on your typical purchases). Don’t be tempted to low-ball the pricing at this stage. If your com- mercial business case is marginal, a slight fl uctuation in pricing could cause the whole project to fail. There is always a deal to be done with a supplier when you get to a fi nal level of detail and specifi cation if you have a commercial constraint that makes or breaks a project—although, of course, be realistic in your expectations.
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.
As the cloud service providers are proliferating, it may be diffi cult for the service consumer to keep track of the latest cloud services offered and to fi nd the most suit- able cloud service providers based on their criteria. In such cases, the service broker performs the cost calculation of the service(s), thus performing the analysis on behalf of the consumer and providing the most competitive service to the consumer from the palette of available services. This may lead to consumption of the service from a new service provider providing the service at better conditions (based on matching criteria like SLA, costs, fi t, security, energy consumption). Thus, the service broker may be able to move system components from one cloud to another based on user- defi ned criteria such as cost, availability, performance, or quality of service. Cloud service brokers will be able to automatically route data, applications, and infrastruc- ture needs based on key criteria such as price, location (including many legislative and regulatory jurisdictional data storage location requirements), latency needs, SLA level, supported operating systems, scalability, backup/disaster recovery capabilities, and regulatory requirements. There are a number of frameworks and solutions that provide examples of this functionality. Some of them are the RESERVOIR [ 27 ], a framework that allows effi cient migration of resources across geographies and administrative domains, maximizing resource exploitation and minimizing their uti- lization costs; the Intercloud [ 28 ] environment which supports scaling of applica- tions among multiple vendor clouds; and Just in Time [ 29 ] broker which adds value by offering cloudcomputing without needing to take care of capacity planning but simply discovering, recovering, and reselling resources already amortized and idle. Another approach is provided by FCM [ 30 ], a meta- brokering component providing transparent service execution for the users by allowing the system to interconnect the various cloud broker solutions, based on the number and the location of the utilized virtual machines for the received service requests.
The past decades have witnessed the success of centralized comput- ing infrastructures in many application domains. Then, the emergence of the Internet brought numerous users of remote applications based on the technologies of distributed computing. Research in distributed computing gave birth to the development of grid computing. Though grid is based on distributed computing, the conceptual basis for grid is somewhat different. Computing with grid enabled researchers to do computationally intensive tasks by using limited infrastructure that was available with them and with the support of high processing power that could be provided by any third party, and thus allowing the researchers to use grid computing, which was one of the first attempts to provide computing resources to users on payment basis. This technology indeed became popular and is being used even now. An associated problem with grid technology was that it could only be used by a certain group of people and it was not open to the public. Cloud com- puting in simple terms is further extension and variation of grid computing, in which a market-oriented aspect is added. Though there are several other important technical differences, this is one of the major differences between grid and cloud. Thus came cloudcomputing, which is now being used as a public utility computing software and is accessible by almost every person through the Internet. Apart from this, there are several other properties that make cloud popular and unique. In cloud, the resources are metered, and a user pays according to the usage. Cloud can also support a continuously varying user demands without affecting the performance, and it is always available for use without any restrictions. The users can access cloud from any device, thus reaching a wider range of people.
Mobility is one of the main issues of MCC, as mobile devices are present here. One par- ticular position may be suitable for a device but, due to change of location, services should not be interrupted. Mobility is one of the reasons for disconnection. In mobility man- agement, localization is very important and it can be achieved using two techniques: infrastructure-based and peer-based. Infrastructure-based techniques use GSM, Wi-Fi, ultra sound RF, GPS, and RFID, which are less suitable for the needs of mobile cloud devices. On the other hand, peer-based techniques are more suited to manage mobility, considering that relative location is adequate and can be implemented with low-range pro- tocols such as Bluetooth. Escort  represents a peer-based technique to localize without using GPS or Wi-Fi, which are power-consuming applications. Here, social encounters between users are monitored by audio signaling and the walking traits of individuals by phone compasses and accelerometers. Here, various routes are created by various encoun- ters. For example, if X wants to locate Y and if X had met Z recently and Z had met Y, the route is first calculated to the point where X met Z, and then to the place where Z met Y. There will be many possible paths but the optimal one is chosen. Thus a mobile device can be localized when it is in a mobile cloud.
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.
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.
Nameservices and storage of metadata about ﬁles including record format infor- mation in the Thor DFS are maintained in a special server called the Dali server (named for the developer’s pet Chinchilla), which is analogous to the Namenode in HDFS. Thor users have complete control over distribution of data in a Thor cluster, and can re-distribute the data as needed in an ECL job by speciﬁc keys, ﬁelds, or combinations of ﬁelds to facilitate the locality characteristics of parallel processing. The Dali nameserver uses a dynamic datastore for ﬁlesystem metadata organized in a hierarchical structure corresponding to the scope of ﬁles in the system. The Thor DFS utilizes the local Linux ﬁlesystem for physical ﬁle storage, and ﬁle scopes are created using ﬁle directory structures of the local ﬁle system. Parts of a distributed ﬁle are named according to the node number in a cluster, such that a ﬁle in a 400- node cluster will always have 400 parts regardless of the ﬁle size. The Hadoop ﬁxed block size can end up splitting logical records between nodes which means a node may need to read some data from another node during Map task processing. With the Thor DFS, logical record integrity is maintained, and processing I/O is com- pletely localized to the processing node for local processing operations. In addition, if the ﬁle size in Hadoop is less than some multiple of the block size times the num- ber of nodes in the cluster, Hadoop processing will be less evenly distributed and node to node disk accesses will be needed. If input splits assigned to Map tasks in Hadoop are not allocated in whole block sizes, additional node to node I/O will result. The ability to easily redistribute the data evenly to nodes based on process- ing requirements and the characteristics of the data during a Thor job can provide a signiﬁcant performance improvement over the Hadoop approach. The Thor DFS also supports the concept of “superﬁles” which are processed as a single logical ﬁle when accessed, but consist of multiple Thor DFS ﬁles. Each ﬁle which makes up a superﬁle must have the same record structure. New ﬁles can be added and old ﬁles deleted from a superﬁle dynamically facilitating update processes without the need to rewrite a new ﬁle. Thor clusters are fault resilient and a minimum of one replica of each ﬁle part in a Thor DFS ﬁle is stored on a different node within the cluster.
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.
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.
as its stack, it might not have been able to achieve the scalability that it achieved on AWS. This by no means is a knock on Google or a declaration that AWS is any better than Google. Simply put, for scaling requirements like Instagram’s, an IaaS provider is a better choice than a PaaS. PaaS providers have thresholds that they enforce within the layers of their architecture to ensure that one customer does not consume so many resources that it impacts the overall platform, resulting in performance degradation for other customers. With IaaS, there are fewer limitations, and much higher levels of scalability can be achieved with the proper architecture. We will revisit this use case in Chapter 5. Architects must not let their loyalty to their favorite vendor get in the way of making the best possible business decision. A hammer may be the favorite tool of a home builder, but when he needs to turn screws he should use a screwdriver. Recommendation: Understand the differences between the three cloud service models: SaaS, PaaS, and IaaS. Know what business cases are best suited for each service model. Don’t choose cloud vendors based solely on the software stack that the developers use or based on the vendor that the company has been buying hardware from for years.
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.
A. Software as a Service (SaaS) - Web-based email, office software, online games, customer relationship management systems and communication tools are all examples of SaaS. As a definition of cloud, it says that Cloud is concept of delivering technology to the user and it is done by the method Software-as-a-service (SaaS). SaaS is the most common form used by small businesses, and includes the use of host software on a remote server. It runs applications through your web browser and stores, retrieves or shares store files outside of our business.
Abstract- Cloudcomputing allows business customers to scale up and down their resource usage based on needs. Due to the nature of widely distributed service providers in clouds, cloud service Migration solves several problems but The problems that follow the migration of services are cloud providers are using own platforms or APIs so services have to be adapted to them and services cannot be migrated without an interruption. This paper proposes the dynamic framework, a new form of platform as a service provider that addresses these issues. This framework abstracts existing clouds, finds the optimal cloud for a service according to its requirements and provides transparency about the underlying clouds. The cloud model is expected to give decision making ability to customers where they don’t have to spend a lot of time comparing different offerings and adapt their applications.