The process involved in writing this paper included studying areas in which I felt limited in my knowledge of cloudcomputing and where I wanted to know more. An outline was created as a guide for the PowerPoint slide presentation and conducting research for composing this paper. Scholarly journals and professional information technology articles, trade journals, and popular websites were scanned for information, including a white paper offered by IBM. To add a little pizzazz my presentation, images of clouds were included in the graphics. Searching for these images and selected just the right one was a lot of fun, adding enjoyment to a somewhat dry, yet informative topic! Citations and a bibliography were collected as I went along in the process and read articles. Costs of Could Computing seemed particularly interesting to me, so it was decided to share screen shots of some of Amazons costs and results of a study found comparing costs.
This book comprehensively debates on the emergence of mobile cloudcomputing from cloudcomputing models. Various technological and architectural advancements in mobile and cloudcomputing have been reported. It has meticulously explored the design and architecture of computational offloading solutions in cloud and mobile cloudcomputing domains to enrich mobile user experience. Furthermore, to optimize mobile power consumption, existing solutions and policies toward green mobile computing, green cloudcomputing, green mobile networking, and green mobile cloudcomputing are briefly discussed. The book also presents numerous cloud and mobile resource allo- cation and management schemes to efficiently manage existing resources (hardware and software). Recently, integrated networks (e.g., WSN, VANET, MANET) have sig- nificantly helped mobile users to enjoy a suite of services. The book discusses existing architecture, opportunities, and challenges, while integrating mobile cloud comput- ing with existing network technologies such as sensor and vehicular networks. It also briefly expounds on various security and privacy concerns, such as application security, authentication security, data security, and intrusion detection, in the mobile cloud com- puting domain. The business aspects of mobile cloudcomputing models in terms of resource pricing models, cooperation models, and revenue sharing among cloud pro- viders are also presented in the book. To highlight the standings of mobile cloud comput- ing, various well-known, real-world applications supported by mobile cloudcomputing models are discussed. For example, the demands and issues while deploying resource- intensive applications, including face recognition, route tracking, traffic management, and mobile learning, are discussed. This book concludes with various future research directions in the mobile cloudcomputing domain to improve the strength of mobile cloudcomputing and to enrich mobile user experience.
Mobile CloudComputing is a new paradigm since 2009 and it is still in nascent stage. The security and privacy issues in mobile cloudcomputing are inherited from cloudcomputing, however, it is difficult to resolve these issues because of resource constraint in mobile devices like energy, storage, processing etc. However, traditional ABE is not suitable for mobile cloud because it is computationally intensive and mobile devices has limited resources. In this paper, we propose LDSS to address this issue. It introduces a novel LDSS-CPABE algorithm to migrate major computation overhead from mobile devices onto proxy servers, thus it can help in solving the secure data sharing problem in mobile cloud. The experimental results show states that LDSS can ensures data privacy in mobile cloud and reduce the overload on users’ side in mobile cloud. In future work, we will design the new approaches to ensure data integrity. To further tap the potential of mobile cloud, and also ensure how to do cipher text retrieval over existing data sharing schemes. To address the security and privacy issues, we will have to develop efficient security and privacy framework with the objective of lesser resource requirement in mobile device and minimize the communication cost and network latency while ensuring privacy, authenticity and integrity of user’s data in cloud.
An integrated view of service-based activities isprovided by the concept of a workflow. An IT assistedworkflow represents a series of structured activities and computations that arise in Information- assisted problem solving. Workflowshave been drawing enormous attention inthe database and information systems researchand development communities [16, 20]. Similarly,the scientific community has developed anumber of problem solving environments, mostof them as integrated solutions . Scientificworkflows merge advances in these two areasto automate support for sophisticated scientificproblem solving [28, 42].A workflow can be represented by a directedgraph of data flows that connect loosely andtightly coupled (and often asynchronous) processingcomponents. One such graph is shown in Figure 1. It illustrates a Kepler-based implementation of a part of a fusion simulation workflow [2,8]. In the context of “cloudcomputing”, the key questions should be whether the under lying infrastructure is supportive of the workflow orientedview of the world. Thisincludes on demandand advance-reservation-based accessto individual and aggregated computational andother resources, autonomics, ability to groupresources from potentially different “clouds” todeliver workflow results, appropriate level ofsecurity and privacy, etc.
In other cases, the loss of control of where your virtual IT infrastructure resides could open the way to other problematic situations. More precisely, the geographical location of a datacenter gen- erally determines the regulations that are applied to management of digital information. As a result, according to the specific location of data, some sensitive information can be made accessible to government agencies or even considered outside the law if processed with specific cryptographic techniques. For example, the USA PATRIOT Act 5 provides its government and other agencies with virtually limitless powers to access information, including that belonging to any company that stores information in the U.S. territory. Finally, existing enterprises that have large computing infra- structures or large installed bases of software do not simply want to switch to public clouds, but they use the existing IT resources and optimize their revenue. All these aspects make the use of a public computing infrastructure not always possible. Yet the general idea supported by the cloudcomputing vision can still be attractive. More specifically, having an infrastructure able to deliver IT services on demand can still be a winning solution, even when implemented within the private premises of an institution. This idea led to the diffusion of private clouds, which are similar to pub- lic clouds, but their resource-provisioning model is limited within the boundaries of an organization.
This paper presents a treatise aboutcloudcomputing security risk and attack, we explained the definition of cloudcomputing and the P2P network in order to understand the infrastructure and how Denial of Service attacks consumes and confuses the network infrastructure, the two type of Denial of Service attack DoS and DDoS aimed to flood the network storage and to exhaust the network resource. Among the available solutions is SSL certificates but we found It's not enough to solve and to prevent DDoS/DoS attacks, that due to the existence of SSL handshake attack. The ideal solution for Denial of Service attacks is by using private certificate protected from third party or to use multi-type and multi-level of private certificate keys. For strengthening our work we took two study cases Prolexic cloud and Parse cloud.
8) Insufficient Due Diligence : Cloudcomputing has brought with it a gold rush of sorts, with many organizations rushing into the promise of cost reductions, operational efficiencies and improved security. While these can be realistic goals for organizations that have the resources to adopt cloud technologies properly, too many enterprises jump into the cloud without understanding the full scope of the undertaking . Without a complete understanding of the CSP environment, applications or services being pushed to the cloud, and operational responsibilities such as incident response, encryption, and security monitoring, organizations are taking on unknown levels of risk in ways they may not even comprehend, but that are a far departure from their current risks.
Milos et. Al.  discusses the Biometric programs together withfingerprint identity, face, or iris scanning. Theseapplications simply paintings in a laboratory setting wherein thepatron laptop has unlimited access to the throughput andcomputational assets of the network. The problem basedright here is on the battery energy of the tool and the throughputof the conversation channel of the client node to the cloud.The paper explains the mobile cloudcomputing method forbiometric programs inclusive of fingerprint identification, facereputation and iris popularity. Debessay et. Al.  analyzesand research the effect of cloudlets in interactive mobile cloudapplications. In order to observe the effect, cloudlet communityand carrier structure is proposed. This structure focuseson file modifying, video streaming, and collaborative chatting.The overall performance profits with the use of clouds are shown by means ofsimulation consequences.
Overview of cloudcomputing: Meaning of the terms cloud and cloudcomputing-cloud based service offerings-grid computing vs cloudcomputing-benefits of cloud model-limitations- legal issues-key characteristics of cloudcomputing-challenges for the cloud-The evolution of cloudcomputing.
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.
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.
John McCarthy was a visionary in computer science; in the early 1960s he formulated the idea that computation may be organized as a public utility, like water and electricity. In 1992 Gordon Bell was invited to and delivered an address at a conference on parallel computations with the provocative title Massively parallel computers: why not parallel computers for the masses? ; he argued that one-of-a- kind systems are not only expensive to build, but the cost of rewriting applications for them is prohibitive. Google Inc. was founded by Page and Brin, two graduate students in computer science at Stanford University; in 1998 the company was incorporated in California after receiving a contribution of $100, 000 from the co-founder and chief hardware designer of Sun Microsystems, Andy Bechtolsheim. Amazon EC2 was initially released as a limited public beta cloudcomputing service on August 25, 2006. The system was developed by a team from Cape Town, South Africa. In October 2008 Microsoft announced the Windows Azure platform; in June 2010 the platform became commercially available. iCloud, a cloud storage and cloudcomputing service from Apple Inc., stores content such as music, photos, calendars, and documents and allows users to access it from Apple devices. The system was announced on June 6, 2011. In 2012 the Oracle Cloud was announced (see www.oracle.com/us/ corporate/features/oracle-cloud/index.html )
Cloudcomputing has evolved from many decades of computing. Cloudcomputing is the biggest technological shift since the birth of the personal computer and the broad adoption of the Internet. Cloudcomputing is still in its infancy. Early adopters were mainly start-ups, small businesses, and risk-taking enterprises. As 2012 closed out and the year 2013 began, cloudcomputing has become widely accepted and enterprise budgets for cloudcomputing initiatives are growing at enormous rates. As with anything new and immature, cloudcomputing is still lacking in standards and best practices. The cloud vendors have occasional outages but their overall performance has improved over the years as their products and services mature. Incredible success stories like Netflix and Instagram are becoming more common each year. Enterprises are shifting dollars away from commercial software licenses and hardware investments in favor of a variety of cloud services across all three service models. The secret to success for enterprises will be picking the right cloud solutions to solve the right business problems. Understanding the three cloud service models—SaaS, PaaS, and IaaS—is crucial for enterprises to make the right investments in the cloud.
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.
Hadoop MapReduce and the LexisNexis HPCC platform are both scalable archi- tectures directed towards data-intensive computing solutions. Each of these system platforms has strengths and weaknesses and their overall effectiveness for any appli- cation problem or domain is subjective in nature and can only be determined through careful evaluation of application requirements versus the capabilities of the solution. Hadoop is an open source platform which increases its ﬂexibility and adaptability to many problem domains since new capabilities can be readily added by users adopt- ing this technology. However, as with other open source platforms, reliability and support can become issues when many different users are contributing new code and changes to the system. Hadoop has found favor with many large Web-oriented companies including Yahoo!, Facebook, and others where data-intensive computing capabilities are critical to the success of their business. Amazon has implemented new cloudcomputing services using Hadoop as part of its EC2 called Amazon Elastic MapReduce. A company called Cloudera was recently formed to provide training, support and consulting services to the Hadoop user community and to pro- vide packaged and tested releases which can be used in the Amazon environment. Although many different application tools have been built on top of the Hadoop platform like Pig, HBase, Hive, etc., these tools tend not to be well-integrated offer- ing different command shells, languages, and operating characteristics that make it more difﬁcult to combine capabilities in an effective manner.
ABSTRACT----- we take customer satisfaction into consideration to address the problem how to configure their cloud service platforms to obtain the maximum profit becomes increasingly the focus that they pay attention to. Customer satisfaction affects the profit of cloud service providers in two ways. On one hand, the cloud configuration affects the quality of service which is an important factor affecting customer satisfaction. On the other hand, the customer satisfaction affects the request arrival rate of a cloud service provider. This paper adopts the thought in Business Administration, and firstly defines the customer satisfaction level of cloudcomputing. Based on the definition of customer satisfaction, a profit maximization model is build in which the effect of customer satisfaction on quality of service (QoS) and price of service (PoS) is considered.
accordingly cloud services can be selected. If it is for personal home use, then there will be different cloud type. Factors like security, costs are also taken in the court. Also some large sized organizations do pragmatic case study on all the cloud types for any accessibility. Many organizations look for SSL certificate for security purpose which they consider the solution of hour when security is their constraint. As cloudcomputing is constantly changing the IT landscape, several other key issues like access benefits, regulatory compliance, data segregation, monitoring, continuity, data recovery are looked. Each provider does what user expects from it, it can make user reap its benefits only when it is chosen according to needs.
Cloudcomputing provide applications as service by software and hardware via the internet , server provide the service for user base on demand and also user pay the cost base on use of service (kushida, 2010). User can use the different application, storage, resources. Cloudcomputing service mode includes three types: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (Iaas) (SPI Model). Security, availability and reliability are challenges in cloudcomputing technology, but security between the other challenges is very important, because any technology without security system can’t adopt by the users. User when will not concern about the security and reliability that, they know about how is the operation being performed. Cloudcomputing try that user can trust to cloud service, because cloud service concern about user, and try to provide security system in cloudcomputing (Frank Gens, 2009). Fujistu researched about concerning in using the cloudcomputing by users, and you can see the result, that most of the users concern about security in cloudcomputing, and after the security, stable operation and support system have highest rate.
Cloudcomputing is a concept which provide a facility to the user to delivering technology though the Internet servers. It is basically for processing and data storage. Without any use of traditional media Cloudcomputing allows vendors to convey services over the Internet. This method is called Software as a Service, or SaaS. Cloudcomputing help user to communicate more than one server at the same time and exchange information among them. Cloudcomputing can increase profitability by improving resource utilization. By improving resource utilization Cloudcomputing can increase profitability .
Despite the tremendous business and technical advantages, what we shall always keep in mind is that cloudcomputing would not be our wonderland until users’ outsourced sensitive data could hide from the prying eyes. Privacy concern is one of the primary hurdles that prevent the widespread adoption of the cloud by potential users, especially if the private data of users used to reside in the local storage are to be outsourced to and computed in the cloud. Imagine that CSPs host the services looking into your personal emails, financial and medical records, and social network profiles. Although these sensitive data could be protected by deploying intrusion detection systems, firewalls, or even segmenting data in a virtualized environment, CSP possesses full control of the cloud infrastructure including the system hardware and lower levels of software stack. Privacy breach is still likely to occur owing to the existence of disgruntled, profiteered or curious employees from CSP [25, 37]. Encrypting-then-outsourcing [28,48] provides us strong guarantee that no one could mine any useful information from the ciphertext of users’ data. Many people argue that sensitive data has to be encrypted before outsourcing in order to provide user data privacy against the cloud service providers. However, encrypted data makes data utilization a very challenging task. One example is keyword search functions on the documents stored in the cloud. Without those usable data services, the cloud will become merely a remote storage which provides limited value to all parties.