Descriptive Analysis of the Cloud Computing Services and Deployment Models

In this paper, we review and summarize some of the leading computing concepts including cluster, grid, and cloud computing. These ideas relate to bringing computers together to build a more powerful computational engine that is capable of processing multiple tasks concurrently. In cluster computing, a group of computers are located close by and directly linked with extremely high speed connections in order to perform as a single computer system. In grid computing, computer resources from multiple Internet domains are combined to reach a common computational goal. Compared with clusters, grids tend to be more loosely coupled, heterogeneous, and geographically distributed. Cloud computing is an emerging technology allowing a large number of interconnected computers, hardware and/or software to be rented as a commodity like electricity. The service types of cluster, grid, and cloud have been compared. We conclude that cloud computing has the potential to meet various computing demands in a cost effective easy way provided that some crucial issues like privacy, security, migration, and management are properly addressed.

The emergence of cloud computing has a significant impact on the new technology trends (broadband internet, fast connection and virtualisation) in the world of Information Technology and has become the backbone for the developing network technologies. Its concept is based on high-performance computing with cluster computing, distributed parallel computing and grid computing. Here, we present a comparison among all these techniques along with the application fields and development trends. This will enable us a better thinking about these concepts. In this paper, we aim to pinpoint several challenges and issues in these computing adoption perspectives. Also, we discussed on some of the projects and tools, used to design and development of these models. This study will be helpful to explore the requirements of the users in the computing scenario.

Grid computing is a major research area with strong involvement from both academia and the computing industry. The common vision is that grid computing represents the culmination of truly general distributed computing across various resources in a ubiquitous, open-ended infrastructure to support a wide range of different application areas. Although significant progress has been made in the design and deployment of grids, many challenges still remain before the goal of a user-friendly, efficient, and reliable grid can be realized. Grid research issues cover many areas of computer science to address the fundamental capabilities and services that are required in a heterogeneous environment, such as adaptability, scalability, reliability and security, and to support applications as diverse as ubiquitous local services, enterprise-scale virtual organizations, and internet-scale distributed supercomputing. Cloud computing is also emerging as an alternate platform for large-scale distributed applications where resources are typically provided by a single administrative domain in a pay per-use mode. To some, cloud computing is a natural evolution of grid computing, to others, it is a complementary and perhaps competing technology. Grid and cloud research will greatly benefit from interactions with the many related areas of computer science, making Euro-Par an excellent venue to present results and discuss issues.

Purpose– Cloud Computing (CC) technology is getting implemented rapidly in the educational sector to improve learning, research and other administrative process. As evident from the literature review, most of these implementations are happening in the western countries such as USA, UK, while the level of implementation of CC in developing countries such as India is rare. Moreover, implementing CC technology in the educational sector require various decisions to be made by the managers of the Information Technology (IT) department such as selecting suitable deployment model, vendor providing cloud service, etc. in their respective university or institute. The purpose of this paper is to attempt to address one such decision. Since, different types of CC deployment are available; selecting a suitable one plays a key role, as it might have an impact on the requirements of various stakeholders such as students, teachers, administrative staff (especially the staff members in the IT department), etc. apart from affecting the overall performance of the facilities such as a laboratory. Naturally, a proper decision by analysing multiple perspectives has to be made while carrying out such strategic initiatives by any educational institute.Design/methodology/approach– A case study methodology has been chosen as the research methodology to discuss and demonstrate the above decision problem that was faced in real time by one of the educational institutes in India, offering high-quality management education. The IT managers of this institute were planning to switch over to CC technology for the computer laboratory and they have to make a decision of choosing suitable alternative CC deployment models such as private cloud (PRC), public cloud (PUC), community cloud (COC), hybrid cloud (HYC), etc. by analysing and comparing them based on various factors and perspectives such as elasticity, availability, scalability, etc. Since, multiple factors are involved in making such a strategic decision, the most commonly used Multi-Criteria Decision Making (MCDM) model – namely, the Analytic Hierarchy Process (AHP) is used as a decision support during the decision making process.Findings– The team of decision makers, who were planning to implement CC in the case institute, found that PRC is best as they believed that it would provide adequate cost savings, apart from providing necessary security to maintain confidential information such as student's detail, grades, etc.Research limitations/implications– The results obtained are based on a single case study. Hence, they cannot be generalized for institutions across educational sector. However, the decision making situation and understanding its impact on the stakeholders of the educational institute can be common across various educational institute.Practical implications– Using a real-life case study of an educational institute, this paper presented a strategic decision making situation, which needs to be considered by the IT managers of the educational institutes when they decide to switch over to CC technology. Various criteria to be considered during the decision making process was identified from the literature review were identified and enumerated. These factors would useful for the IT managers of the different educational institute and they can suitably add or delete these decision criteria as per their requirements and situation at hand. Moreover, the algorithm of AHP, which was used as a decision support, was presented in a step-by-step manner, which should be beneficial for the practitioners to apply the same for similar decision making situations.Originality/value– It is believed that this paper would be the first to report on a strategic decision of choosing the deployment model for CC technology especially in the educational sector. Similarly, this paper would also contribute to the field of CC, as it lists out the decision criteria that are to be considered for making the above decision, which has not got adequate importance. Lastly, this paper is also unique in the realm of AHP because application for a decision problem in the field of CC especially in the educational sector is least reported.

Computing has changed the way humans solve problems. It has changed the world more than any other innovation in the recent past. Over the last couple of years, there has been an increased interest in utilizing computing processor powers to its maximum. Cloud, cluster and grid computing are the most common types of computing. All these share some common features toward achieving utility although marginal differences exist in their processes and approaches. The paper presents the relationship and the differences of grid computing from cloud computing in terms of their architecture, applications, security and tools.

Grid and Cluster Computing are becoming the two main approaches for building large-scale and cost-effective computing infrastructures. Cluster computing relies on the gathering of just a few, or up to thousands, of inexpensive personal computers combined into a single machine, i.e., a cluster. However, despite that cluster computing is a more economically viable approach for building a supercomputing platform, the effective programming of such a computing infrastructure remains difficult. Grid computing is pushing this idea further by clustering a large variety of geographically distributed resources like computer, storage, data and other services, at a wider scale. Grids rely heavily on the Internet to connect all these resources together and offer them to the users as transparently as possible. Although much progress has been made in the deployment of grid infrastructures, many challenges still lie ahead of us before the ultimate goal of the Grid can be realized. This topic reports recent advances in Grid models, services, application development, data storage and management.

Cloud Computing and Inter-Clouds – Types, Topologies and Research Issues

Dew Computing (DC) is a comparatively modern field with a wide range of applications. By examining how technological advances such as fog, edge and Dew computing, and distributed intelligence force us to reconsider traditional Cloud Computing (CC) to serve the Internet of Things. A new dew estimation theory is presented in this article. The revised definition is as follows: DC is a software and hardware cloud-based company. On-premises servers provide autonomy and collaborate with cloud networks. Dew Calculation aims to enhance the capabilities of on-premises and cloud-based applications. These categories can result in the development of new applications. In the world, there has been rapid growth in Information and Communication Technology (ICT), starting with Grid Computing (GC), CC, Fog Computing (FC), and the latest Edge Computing (EC) technology. DC technologies, infrastructure, and applications are described. We'll go through the newest developments in fog networking, QoE, cloud at the edge, platforms, security, and privacy. The dew-cloud architecture is an option concerning the current client-server architecture, where two servers are located at opposite ends. In the absence of an Internet connection, a dew server helps users browse and track their details. Data are primarily stored as a local copy on the dew server that starts the Internet and is synchronized with the cloud master copy. The local dew pages, a local online version of the current website, can be browsed, read, written, or added to the users. Mapping between different Local Dew sites has been made possible using the dew domain name scheme and dew domain redirection.

The Internet of Things (IoT) is defined as interconnected digital and mechanical devices with intelligent and interactive data transmission features over a defined network. The ability of the IoT to collect, analyze and mine data into information and knowledge motivates the integration of IoT with grid and cloud computing. New job scheduling techniques are crucial for the effective integration and management of IoT with grid computing as they provide optimal computational solutions. The computational grid is a modern technology that enables distributed computing to take advantage of a organization’s resources in order to handle complex computational problems. However, the scheduling process is considered an NP-hard problem due to the heterogeneity of resources and management systems in the IoT grid. This paper proposed a Greedy Firefly Algorithm (GFA) for jobs scheduling in the grid environment. In the proposed greedy firefly algorithm, a greedy method is utilized as a local search mechanism to enhance the rate of convergence and efficiency of schedules produced by the standard firefly algorithm. Several experiments were conducted using the GridSim toolkit to evaluate the proposed greedy firefly algorithm’s performance. The study measured several sizes of real grid computing workload traces, starting with lightweight traces with only 500 jobs, then typical with 3000 to 7000 jobs, and finally heavy load containing 8000 to 10,000 jobs. The experiment results revealed that the greedy firefly algorithm could insignificantly reduce the makespan makespan and execution times of the IoT grid scheduling process as compared to other evaluated scheduling methods. Furthermore, the proposed greedy firefly algorithm converges on large search spacefaster , making it suitable for large-scale IoT grid environments.

Cloud computing is a new computing model which uses virtualization technology, distributed computing, parallel computing and other existing technologies to achieve cloud service virtualization and economies of scale, whilst increasingly overwhelming cloud security issues has brought great challenges and concerns to the cloud services providers and cloud users, especially trust and privacy issues with regard to cloud computing and cloud shared storage associated security issues. In the paper, we expound the basic concepts of cloud computing, deployment models, service models and key features, analyze and outline the currently highlighted cloud security issues, report the status quo of cloud computing security, investigate the prevalent and typical cloud computing security problem key solving techniques, and thus render a comprehensive cloud computing security technical reference model, which is composed of associated cloud security solving techniques that result from inevitably multi-faceted cloud security issues. The model is expected to alleviate prominent cloud security issues. This paper generalizes cloud security technology research directions and further development space of cloud security technology and standardization.KeywordsCloud computingSecurity issuesTrusted cloud computing solving techniquesShared storage security solving techniquesCloud security technical reference model

Cloud computing has become biggest buzz in the computer era these days. It runs entire operating systems on the cloud and do everything on cloud to store data off-site. Cloud computing is primarily based on grid computing, but it’s a new computational model. Cloud computing has emerged into a new opportunity to further enhance way of hosting data centre and provide services. The primary substance of cloud computing is to deal the computing power, storage, different sort of stages and services which assigned to the external users on demand through the internet. Task scheduling in cloud computing is vital role optimisation and effective dynamic resource allocation for load balancing. In cloud, the issue focused is under utilisation and over utilisation of the resources to distribute workload of multiple network links for example, when cloud clients try to access and send request to the same cloud server while the other cloud server remain idle at that moment, leads to the unbalanced of workload on cloud data centers. Thus, load balancing is to assign tasks to the individual cloud data centers of the shared system so that no single cloud data centers is overloaded or under loaded. A Hybrid approach of Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm is combined in order to get effective response time. The proposed hybrid algorithm has been experimented by using CloudSim simulator. The result shows that the hybrid load balancing algorithm improves the cloud system performance by reducing the response time compared to the Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm. Â

Distributed computing, grid computing, and virtualization have all resulted in the growth of cloud computing.. The existing research work, introduced a strategic theory (ST) based Improved Elephant Herd Optimization (IEHO) for feasible allotment procedure where the result function confess a straight payment system to meet the users requirements. However total users is huge in cloud computing, total tasks and total data are as well vast. Because the cost of every work on cloud services varies, client task scheduling in the cloud is not like conventional scheduling approaches. In a cloud computing system, it's critical to figure out how to effectively schedule work. As a results, the research proposes an unique Task Scheduling Mechanism (TSM) was proposed, which may suit users' needs while also improving resource usage and so improving the overall performance of the cloud cloud computing. The goal of this project is to plan task groups in a cloud computing platform with varying resource costs and computational performance. The suggested cloud scheduling solution uses an enhanced cost-based scheduling scheme to efficiently map workloads to existing cloud services. The scheduling technique, that is founded on Hybrid Evolutionary (HE)algorithms, increases the computation/communication proportion by combining user jobs as per a cloud resource's processing capacity and sending the aggregated activities to the resource. The simulation experiments show that the proposed Hybrid Evolutionary based Task Scheduling Mechanism (HE-TSM) is efficiently schedule the tasks along with both costs and performance in the cloud computing environment.

Survey on Programming Models and Environments for Cluster, Cloud, and Grid Computing that Defends Big Data

The 2013 IEEE Latin America Conference on Cloud Computing and Communications (Latin- Cloud 2013) is the second edition of a conference that aims at becoming the premier forum for discussions of recent advances in cloud computing and communications, virtualization, utility computing, grid computing, service-oriented architectures, and software- defined networks for the datacenter in this region of the world. The Cloud Computing and Communications paradigm has defined unprecedented business models, promoting on-demand computing resource allocation, elastic scaling, and elimination of up-front costs. Capitalizing on paradigms such as virtualization, utility computing, grid computing, and service oriented architectures, cloud computing and communications has already changed the way we compute and communicate. To cope with technology challenges for the support of diverse services and applications, the IEEE Communications Society (ComSoc), Latin America Region, with the support of the IEEE Cloud Computing Initiative, supported the 2013 IEEE Latin America Conference on Cloud Computing and Communications (LatinCloud 2013). The conference has the goal of establishing a scientific forum for the discussion of the rather relevant topic of cloud computing and its critically associated aspects of communications in the Latin American context.

Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: A review, classifications, and open issues