Abstract:- By using Internet technology cloud provides virtualized IT resources as a service. CloudComputing is a combination of Grid computing and Cluster computing. By using the Internet a computer grid is created whose purpose is only utilizing shared resources such as on a pay- per-use model, computer software and hardware. The main moto of cloudcomputing is that you can access your data in any corner of the world by using internet. Cloudcomputing is a general term for delivering through the internet. Cloudcomputing is a virtualized computer power and storage delivered via platform-agnostic infrastructures of abstracted hardware and software access over internet. Cloudcomputing systems usually work on various models like public, private, hybrid, and community models.
Enterprises that move their IT to the cloud are likely to encounter challenges such as security, interoperability, and limits on their ability to tailor their ERP to their business processes. The cloud can be a revolutionary technology, especially for small start-ups, but the benefi ts wane for larger enterprises with more complex IT needs [ 10 ]. The cloud model can be truly disruptive if it can reduce the IT opera- tional expenses of enterprises. Traditional utility services provide the same resource to all consumers. Perhaps the biggest difference between the cloudcomputing ser- vice and the traditional utility service models lies in the degree to which the cloud services are uniquely and dynamically confi gured for the needs of each application and class of users [ 12 ]. Cloudcomputing services are built from a common set of building blocks, equivalent to electricity provider turbines, transformers, and distri- bution cables. Cloudcomputing does, however, differ from traditional utilities in several critical respects. Cloud providers compete aggressively with differentiated service offerings, service levels, and technologies. Because traditional ERP is installed on your servers and you actually own the software, you can do with it as you please. You may decide to customize it, integrate it to other software, etc. Although any ERP software will allow you to confi gure and set up the software the way you would like, “Software as a Service” or “SaaS” is generally less fl exible than the traditional ERP in that you can’t completely customize or rewrite the soft- ware. Conversely, since SaaS can’t be customized, it reduces some of the technical diffi culties associated with changing the software. Cloud services can be com- pletely customized to the needs of the largest commercial users. Consequently, we have often referred to cloudcomputing as an “enhanced utility” [ 12 ]. Table 9.2 [ 5 ] shows the E-skills study for information and communications technology (ICT) practitioners conducted by the Danish Technology Institute [ 5 ] that describes the
It’s also critical to avoid thinking of cloudcomputing as a drive to reduce your operational headcount or costs. Although lowering costs is a valid business goal, it’s also a way of taking a lot of the day-to-day repetitive work out of your operations through automation. Automation enables IT staff to do something that adds benefi t to the business, allowing them more time to focus on projects rather than business as usual. This may sound like a well-used truism that is trotted out by management, and it is often overused to justify technology spending. However, if you think about the way the IT industry is moving—increasingly making use of lower-cost headcount to perform operational tasks, often through offshoring or outsourcing—you should see an opportunity to implement cloudcomputing as a way of developing your career and mov- ing up the stack to stay relevant in a changing world rather than being left to compete with a cheaper workforce.
Several different surveys on cloudcomputing in the logistics sector have been conducted in the past few months and published as studies. One of them was an online survey conducted by the software provider INFORM GmbH which showed that 68.3 % of the surveyed companies are ready right now to use cloudcomputing for logistics tasks — only 12.7 % have actually done it. The reasons for this are a lack of familiarity with the topic (29.5 %) and the security concerns mentioned by almost half of the surveyed companies. The possibility of having to rely on an external service provider was a barrier to using cloud technology for 13 % of the surveyed companies. The lack of industry-speci ﬁ c solutions was an obstacle for another 5 %. There seems to be a wide range of reasons. Flexible access (38 %), reduction in operating costs (25 %), faster implementation times for business processes (18 %), platform independence (12 %), and access to IT resources that would not be possible without cloudcomputing (7 %) were identi ﬁ ed as the ben- e ﬁ ts. According to the respondents, cloudcomputing solutions can be used for the communication between vendors and customers, controlling suppliers, and man- aging supply chain events. 25
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.
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.
In 1997, Professor Ramnath Chellappa of Emory University, defined cloudcomputing for the first time while a faculty member at the University of South California, as an important new “computing paradigm where the boundaries of computing will be determined by economic rationale rather than technical limits alone.” Even though the international IT literature and media have come forward since then with a large number of definitions, models and architectures for cloudcomputing, autonomic and utility computing were the foundations of what the community commonly referred to as “cloudcomputing”. In the early 2000s, companies started rapidly adopting this concept upon the realization that cloudcomputing could benefit both the Providers as well as the Consumers of services. Businesses started delivering computing functionality via the Internet, enterprise- level applications, web-based retail services, document-sharing capabilities and fully-hosted IT platforms, to mention only a few cloudcomputing use cases of the 2000s. The latest widespread adoption of virtualization and of service- oriented architecture (SOA) promulgated cloudcomputing as a fundamental and increasingly important part of any delivery and critical-mission strategy, enabling existing and new products and services to be offered and consumed more efficiently, conveniently and securely. Not surprisingly, cloudcomputing became one of the hottest trends in the IT armory, with a unique and complementary set of properties, such as elasticity, resiliency, rapid provisioning, and multi-tenancy.
It is foreseen that cloudcomputing could become a disruptive technology for mobile multimedia applications and services . In order to meet mul- timedia’s QoS requirements in cloudcomputing for multimedia services over the Internet and mobile wireless networks, Zhu et al.  proposed a multimedia cloudcomputing framework that leverages cloudcomputing to provide multimedia applications and services over the Internet. The prin- cipal conceptual architecture is shown in Figure 1.5. Zhu et al. addressed multimedia cloudcomputing from multimedia-aware cloud (media cloud) and cloud-aware multimedia (cloud media) perspectives. The media cloud (Figure 1.5a) focuses on how a cloud can perform distributed multimedia processing and storage and QoS provisioning for multimedia services. In a media cloud, the storage, CPU, and GPU are presented at the edge (i.e., MEC) to provide distributed parallel processing and QoS adaptation for various types of devices. The MEC stores, processes, and transmits media data at the edge, thus achieving a shorter delay. In this way, the media cloud, composed of MECs, can be managed in a centralized or peer-to-peer (P2P) manner. The cloud media (Figure 1.5b) focuses on how multimedia ser- vices and applications, such as storage and sharing, authoring and mashup, adaptation and delivery, and rendering and retrieval, can optimally utilize cloudcomputing resources to achieve better quality of experience (QoE). As depicted in Figure 1.5b, the media cloud provides raw resources, such as hard disk, CPU, and GPU, rented by the media service providers (MSPs) to serve users. MSPs use media cloud resources to develop their multime- dia applications and services, for example, storage, editing, streaming, and delivery.
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.
The main aim of this work is to present a difference between Grid Computing and CloudComputing. Cloudcomputing has many advantages over Grid Computing, clouds will not replace grids, as grids have not replaced capability HPC, over the last 10 years as some have predicated. All three technologies have their place, what we will see over the next couple of years is that these different computing nodes will more and more grow together with the WWW and the Internet, until all these resources become one global infrastructure for information, Knowledge, computation and communication, the WWW. We think it is more likely that grids will be re-branded or merge into cloudcomputing, Grid Computing helped create a certain technology reality which made clouds possible. And when it comes to IaaS (infrastructure as s service), We think in five years something like 80 to 90 percent of the computation are doing could be cloud-based. In a word, the concept of CloudComputing is becoming more and more popular. Now, CloudComputing is in the beginning stage. All kinds of companies are providing all kinds of Cloudcomputing service, from software application to net storage and mail filter. We believe cloudcomputing will become main technology in our information life. Cloud has owned all conditions. Now the dream of Grid Computing will be realized by CloudComputing. It will be a great event in the IT history .
Virtualization has been used successfully since the late 1950s. A virtual memory based on paging was first implemented on the Atlas computer at the University of Manchester in the United Kingdom in 1959. In a cloudcomputing environment a VMM runs on the physical hardware and exports hardware- level abstractions to one or more guest operating systems. A guest OS interacts with the virtual hardware in the same way it would interact with the physical hardware, but under the watchful eye of the VMM which traps all privileged operations and mediates the interactions of the guest OS with the hardware. For example, a VMM can control I/O operations to two virtual disks implemented as two different sets of tracks on a physical disk. New services can be added without the need to modify an operating system. User convenience is a necessary condition for the success of the utility computing paradigms. One of the multiple facets of user convenience is the ability to run remotely using the system software and libraries required by the application. User convenience is a major advantage of a VM architecture over a traditional operating system. For example, a user of the Amazon Web Services (AWS) could submit an Amazon Machine Image (AMI) containing the applications, libraries, data, and associated configuration settings. The user could choose the operating system for the application, then start, terminate, and monitor as many instances of the AMI as needed, using the Web Service APIs and the performance monitoring and management tools provided by the AWS.
of resources within Clouds. They have also presented a vision for the creation of global Cloud exchange for trading services. They have discussed some representative platforms for Cloudcomputing covering the state-of-the-art. In particular, authors have presented various Cloud efforts in practice from the market oriented perspective to reveal its emerging potential for the creation of third-party services to enable the successful adoption of Cloudcomputing, such as meta-negotiation infrastructure for global Cloud exchanges and provide high performance content delivery via `Storage Clouds'.
Being able to keep important data secure has always been a priority in IT, but with a technology that takes information outside of the virtual secure walls most corporations have up will raise red flags. The usage of thin clients could possibly be high-jacked if people are carless with data. Also, SLAs will need to have provisioning within them that directly specifies how cloudcomputing providers plan on protecting data. This could become a lawsuit-threatening issue someday soon if companies are not careful. With reports coming out all the time about data being lost or stolen and the rise in identity theft as the result of stolen data this could be a huge deal breaker for some companies hoping to utilize cloud technology. The idea of “private clouds” is a term that had been coined to help ease people’s concerns. But until vendors are able to easily classify what that means many technologists are going to remain concerned about the feasibility of cloudcomputing to secure data which is private and should not be out in the open.
The cloud is not only an enabler for enterprises but it is a great enabler for cyber-criminals as well for two reasons. First, cloudcomputing is still very immature and lacking standards at this time. There are not a lot of engineers with years of hands-on experience securing applications in the cloud. The end result is that many cloud services are being deployed by today’s corporations without the necessary security and controls and are very vulnerable to all kinds of attacks and breaches. The second reason why the cloud is an enabler for cyber-criminals is that the cloud vendors are a huge target because they house compute resources and data for a large number of companies. The cloud providers typically provide high levels of perimeter security, but it is up to the companies deploying their services to build the appropriate level of application security. For example, an Infrastructure as a Service (IaaS) cloud provider like Amazon Web Services (AWS) has world-class secure data centers, white papers on how to build highly secure services on its platform, and provides a suite of application programming interfaces (APIs), making it easier to design for security. However, it is up to the architects building the software on AWS to encrypt the data, manage the keys, implement good password policies, and so forth.
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.
One of the critical questions for channel companies to answer is whether or not cloud makes sense from an ROI perspective and if so, in what capacity and in which customer scenarios. This basic “economics of the cloud” discussion has been front-‐and-‐center in the channel for the better part of the last three to five years. The conversation is complicated, due in large part to the wide variety of cloud business model options and potential revenue structures to explore as well as differing customer needs. And yet, we are seeing solution providers move more decisively. Nearly 6 in 10 said they proactively pursued multiple segments of the various cloud business models in an attempt to quickly and comprehensively enter the cloud market, with medium and larger firms more likely to have gone this route than the smallest channel player (see Section 3 of this report for a detailed discussion of business models). As a result, a segment of companies have assembled quantifiable tracking metrics on revenue and profit margin, which can serve as a guidepost for channel companies moving more slowly into cloud.
There’s growing sentiment among many cloud experts that ultimately hybrid adoption will be most ad- vantageous for many organizations. Warrilow says “for some time Gartner has advised that hybrid is the most likely scenario for most organiza- tions.” Staten agrees with the notion for two reasons. First, “some appli- cations and data sets simply aren’t a good fit with the cloud,” he says. This might be due to application architec- ture, degree of business risk (real or perceived), and cost, he says. Second, rather than making a cloud-or-no- cloud decision, “it’s more practical and effective to leverage the cloud for what makes the most sense and other deployment options where they make the most sense,” he says. In terms of strategy, Staten recommends regularly analyzing deployment decisions. “As cloud services mature, their applica- bility increases,” he says.
Thus providing Infrastructure as a Service essentially means that the cloud provider assembles the building blocks for providing these services, including the computing resources hardware, networking hardware and storage hardware. These resources are exposed to the consumers through a request management system which in turn is integrated with an automated provisioning layer. The cloud system also needs to meter and bill the customer on various chargeback models. The concept of virtualization enables the provider to leverage and pool resources in a multi-tenant model. Thus, the features provided by virtualization resource pooling, combined with modern clustering infrastructure, enable efficient use IT resources to provide high availability and scalability, increase agility, optimize utilization, and provide a multi-tenancy model.
Cloudcomputing evolved out of grid computing , which is a collection of dis- tributed computers intended to provide computing power and storage on demand [ 1 ]. Grid computing clubbed with virtualisation techniques help to achieve dynam- ically scalable computing power, storage, platforms and services. In such an envi- ronment, a distributed operating system that produces a single system appearance for resources that exist and is available is solicited most [ 2 ]. In other words, one can say that cloudcomputing is a specialised distributed computing paradigm. Cloud differs with its on-demand abilities like scalable computing power – up or down, service levels and dynamic confi guration of services (via approaches like virtualisation ). It offers resources and services in an abstract fashion that are charged like any other utility, thus bringing in a utility business model for comput- ing. Though virtualisation is not mandatory for cloud, its features like partitioning, isolation and encapsulation [ 3 ] and benefi ts like reduced cost, relatively easy administration, manageability and faster development [ 4 ] have made it an essential technique for resource sharing. Virtualisation helps to abstract underlying raw resources like computation, storage and network as one, or encapsulating multiple application environments on one single set or multiple sets of raw resources. Resources being both physical and virtual, distributed computing calls for dynamic load balancing of resources for better utilisation and optimisation [ 5 ]. Like any other traditional computing environment, a virtualised environment must be secure and backed up for it to be a cost saving technique [ 3 ]. Cloudcomputing is a trans- formation of computing by way of service orientation, distributed manageability and economies of scale from virtualisation [ 3 ].