Abstract: Cyberinfrastructure has closely tracked commercial best practices for over a decade. However, we believe there is still much to learn about correct strategies for building distributed systems to support collaborating scientists and related communities. In this perspectives paper, we review the current state of Cyberinfrastructure and illustrate opportunities that we see if CloudComputing strategies are adopted. In summary, CloudComputing is the use of Web Services to control the life cycle of virtual machines and virtual data stores to create a flexible, user-controlled infrastructure. Huge commercial investments in Cloud infrastructure make it likely that these systems will dominate large-scale computing hardware and software in the next decade. Furthermore, open source Cloud software is making it possible for universities and research laboratories to build open-architecture clouds for scientific computing and other uses. We illustrate the applicability and potential advantages of CloudComputing to Spatial Cyberinfrastructure through two case studies (flood modeling and radar imageprocessing), mapping these projects’ requirements to both infrastructure and runtime capabilities typically provided by Clouds. Our preliminary conclusion from this review is that Spatial Cyberinfrastructure’s requirements are a good match for many common capabilities of Clouds, warranting a larger scale investigation.
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.
Bednarz et al.  proposed Cloud Based Image Analysis and Processing Toolbox being carried out by CSIRO, is to run on the Australian National research Collaboration Tools and Resources (NeCTAR) cloud infrastructure and is designed to give access to biomedical imageprocessing and analysis services to Australian researchers via remotely accessible user interfaces. The toolbox is based on software packages and libraries developed over the last 10-15 years by CSIRO scientists and software engineers: (a) HCA-Vision: developed for automating the process of quantifying cell features in microscopy images; (b) MILXView: a 3D medical imaging analysis and visualization platform increasingly popular with researchers and medical specialists working with MRI, PET and other types of medical images and (c) X- TRACT: developed for advanced X-ray image analysis and Computed Tomography. By providing user-friendly access to cloudcomputing resources and new workflow-based interfaces, our solution will enable the researchers to carry out various challenging image analysis and reconstruction tasks that are currently impossible or impractical due to the limitations of the existing interfaces and the local computer hardware. Several case studies will be presented at the conference.
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
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.
Apart from the vendor-specific migration methodologies and guidelines, there are also proposals independent from a specific cloud provider. Reddy and Kumar proposed a methodology for data migration that consists of the following phases: design, extraction, cleansing, import, and verification. Moreover, they categorized data migration into storage migration, database migration, application migration, business process migration, and digital data retention (Reddy and Kumar, 2011). In our proposal, we focus on the storage and database migration as we address the database layer. Morris specifies four golden rules of data migration with the conclusion that the IT staff does not often know about the semantics of the data to be migrated, which causes a lot of overhead effort (Morris, 2012). With our proposal of a step-by-step methodology, we provide detailed guidance and recom- mendations on both data migration and required application refactoring to minimize this overhead. Tran et al. adapted the function point method to estimate the costs of cloud migration projects and classified the applications potentially migrated to the cloud (Tran et al., 2011). As our assumption is that the decision to migrate to the cloud has already been taken, we do not con- sider aspects such as costs. We abstract from the classification of applications to define the cloud data migration scenarios and reuse distinctions, such as complete or partial migration to refine a chosen migration scenario.
Operating a web site that requires database access, supports considerable traffic, and possibly connects to enterprise systems requires complete control of one or more servers, to guarantee responsiveness to user requests. Servers supporting the web site must be hosted in a data center with access from the public Internet. Traditionally, this has been achieved by renting space for physical servers in a hosting center operated by a network provider far from the enterprise’s inter- nal systems. With cloudcomputing, this can now be done by renting a virtual machine in a cloud hosting center. The web site can make use of open source software, such as Apache HTTP Server, MySQL, and PHP; the so-called LAMP stack; or a Java™ stack, all of which is readily available. Alternatively, enterprises might prefer to use commercially supported software, such as Web- Sphere ® Application Server and DB2 ® , on either Linux ® or Windows operating systems. All
A cloud OS should provide the APIs that enable data and services interoper- ability across distributed cloud environments. Mature OSs provide a rich set of services to the applications so that each application does not have to invent important functions such as VM monitoring, scheduling, security, power management, and memory management. In addition, if APIs are built on open standards, it will help organizations avoid vendor lock-in and thereby creating a more flexible environment. For example, linkages will be required to bridge traditional DCs and public or private cloud environments. The flex- ibility of movement of data or information across these systems demands the OS to provide a secure and consistent foundation to reap the real advan- tages offered by the cloudcomputing environments. Also, the OS needs to make sure the right resources are allocated to the requesting applications. This requirement is even more important in hybrid cloud environments. Therefore, any well-designed cloud environment must have well-defined APIs that allow an application or a service to be plugged into the cloud eas- ily. These interfaces need to be based on open standards to protect customers from being locked into one vendor’s cloud environment.
Expedia, Travelocity, Orbitz, Kayak, CheapoAir, Bestfares, MakeMyTrip.com, Cleartrip, and Carlson Wagonlit Travel are a few of the many travel and hospitality portals that offer elegant services via an interactive portal with a rich web experience for all travel and hospitality needs, breaking physical boundaries. We no longer need to visit any physical ofﬁces to accomplish these jobs—we can do so with the use of smart devices from anywhere at any time. MakeMyTrip.com is an example in the travel and hospitality industry to establish rapid growth over a decade, crossing the limit of geographical boundaries and leveraging these major technologies. The company was founded in the year 2000 with the aim of empow- ering Indian travelers with instant booking and comprehensive travel and hospitality packages in one web environment. It aimed to offer a range of best-value products and services based on leading technol- ogies for interactive customer engagement supported by round-the- clock support staff. With greater customer engagement based on cloud deployment, the company expanded its reach to global cus- tomers, breaking all geographical boundaries, and today it is extremely successful among Asian diasporas globally, including those in the United States, Australia, Europe, the Middle East, and Africa. Software Development Leverages
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. .
Nearly equal in significance level are the rest of the challenges cited by channel firms making the move to cloud, with most of those hurdles centered on financial decisions. Initial start up costs, for example, can be minimal or quite large, depending on whether or not they involved building a data center to provide cloud services. Interestingly, the largest channel firms cited this as a major challenge, though they are most likely to have the deeper pockets needed to outfit a new data center if they don’t already have one in existence. Meantime, cash flow and other financial considerations ranked highest among channel firms (63%) involved in all four types of cloud business models outlined in this study. This suggests that the level of commitment they have made to cloud has complicated financial fundamentals; one example would be the effects of a decreased reliance on legacy streams of revenue, which in the short-‐term could create cash flow concerns as they ramp cloud sales.
Because the vCloud Director architecture makes it easy to create multiple private and shared networks to isolate tenant platforms, you will fi nd that you are deploying signifi cantly more VLANs than would typically be required for a traditional virtualization platform. Most switching infrastructure has a technical limit of 4095 VLANs per switch. This can be a seri- ous issue for service providers that have many enterprise customers looking to consume a public cloud service, so this ability to scale is important. Most enterprises are not likely to reach this level in normal operation, but there is a signifi cant benefi t in provisioning vCloud Director within your enterprise network using VCNI. You can request a single VLAN once from the networking team within your business and from then on you have nothing more to do with them as you scale out the platform.
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 ].
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.
Commonly, agility, delivery speed, and cost savings entice companies to public clouds. Public cloud, for example, can free a company from having to invest in consolidating, expanding, or building a new data center when it outgrows a current facility, Kavis says. IT really doesn’t “want to go back to the well and ask management for another several mil- lion dollars,” thus it dives into the public cloud, he says. Stadtmueller says the public cloud is the least ex- pensive way to access compute and storage capacity. Plus, it’s budget- friendly because up-front infra- structure capital investments aren’t required. Businesses can instead align expenses with their revenue and grow capacity as needed. This is one reason why numerous startups choose all- public-cloud approaches.
A common option for reducing the operating costs of only sporadically used IT infra- structure, such as in the case of the “warm standby” , is CloudComputing. As defined by NIST , CloudComputing provides the user with a simple, direct access to a pool of configurable, elastic computing resources (e.g. networks, servers, storage, applications, and other services, with a pay-per-use pricing model). More specifically, this means that resources can be quickly (de-)provisioned by the user with minimal provider interaction and are also billed on the basis of actual consumption. This pric- ing model makes CloudComputing a well-suited platform for hosting a replication site offering high availability at a reasonable price. Such a warm standby system with infrastructure resources (virtual machines, images, etc.) being located and updated in the Cloud is herein referred to as a “Cloud-Standby-System”. The relevance and po- tential of this cloud-based option for hosting replication systems gets even more ob- vious in the light of the current situation in the market. Only fifty percent of small and medium enterprises currently practice BCM with regard to their IT-services while downtime costs sum up to $12,500-23,000 per day for them .
We have experience a tremendous change in computing from older times till today. Previously, large computers were kept behind the glass walls and only the professional are allowed to operate them . Later, came the concept of grid computing which allows the users to have computing on demand according to need . After that, we got such computing which makes resource provisioning easier and on demand of user . Then, finally we got the concept of cloudcomputing which concentrates on the provisioning and de provisioning of computation, storage, data services to and from the user without user being not aware of the fact that from where he is getting those resources . With the large scale use of internet all over the globe, everything can be delivered over internet using the concept of cloudcomputing as a utility like gas, water, and electricity etc. . The rest of the paper is organized as follows: Section II describes the cluster computing including its advantages and disadvantages. Section III describes grid computing including its advantages and disadvantages. Section IV describes cloudcomputing including its advantages and disadvantages. Section V represents comparison between cluster, grid, and cloudcomputing. In the last section, conclusion is presented.
Cloudcomputing and big data are conjoined. Big data provides users the ability to use commodity computing to process distributed queries across multiple datasets and return resultant sets in a timely manner. Cloudcomputing provides the underlying engine through the use of Hadoop, a class of distributed data-processing platforms. Large data sources from the cloud and Web are stored in a distributed fault-tolerant database and processed through a programming model for large data sets with a parallel distributed algorithm in a cluster. Big data utilizes distributed storage technology based on cloudcomputing rather than local storage attached to a computer or electronic device. Big data evaluation is driven by fast-growing cloud-based applications developed using virtualized technologies. Therefore, cloudcomputing not only provides facilities for the computation and processing of big data but also serves as a service model. The use of cloudcomputing in big data is shown in the following figure. The main purpose of data visualization is to view analytical results presented visually through different graphs for decision making. Therefore, cloudcomputing not only provides facilities for the computation and processing of big data but also serves as a service model. Cloudcomputing is correlated with a new pattern for the provision of computing infrastructure and big data processing method for all types of resources available in the cloud through data analysis.
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 .
In Mobile CloudComputing, all small portable devices are wirelessly connected with the cloud server. During wireless communication, the cloud server generates master keys for every mobile device based on their unique identity like MAC address and IMEI etc. After mutual authentication and registration, the mobile devices and cloud server communicated to each other by encrypting information with the help of cloud server, the generated master keys for specific mobile devices. The main limitation of this paper is that mobile devices having geographical area constraint and lack of central standard authenticator. If devices move from one area to another, then it has to be authenticating with a new server and may face problems of compatibility in term of hardware configuration and software.