Comparing of Cloud Computing and Grid Computing

Along with the development of grid computing and cloud computing, finite computing resources supplied by standalone server usually become the bottleneck in the process of system implementation of online trading platform. Moreover, It is difficult to reuse algorithm module and the system also becomes more complicated to achieve. Traditional online trading framework can not preferably solve the problem listed above. Thus, we propose a framework of online trading platform based on cloud computing. It is feasible to establish a high-complexity online trading platform using infinite virtual computing capability provided by cloud computing. Simultaneously, this framework can realize the sharing of data sets after the integration of heterogeneous and distributed data based on layered thoughtway, and supply theoretical foundation and framework support for setting up a unified, open, distributed, and parallel online trading platform.

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.

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.

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.

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.

Grid Computing and Cloud Computing are two different technologies that have emerged to validate the long-held dream of computing as utilities which led to an important revolution in IT industry. These technologies came with several challenges in terms of middleware, programming model, resources management and business models. These challenges are seriously considered by Distributed System research. Resources allocation is a key challenge in both technologies as it causes the possible resource wastage and service degradation. This paper is addressing a comprehensive study of the resources allocation processes in both technologies. It provides the researchers with an in-depth understanding of all resources allocation related aspects and associative challenges, including: load balancing, performance, energy consumption, scheduling algorithms, resources consolidation and migration. The comparison also contributes an informal definition of the Cloud resource allocation process. Resources in the Cloud are being shared by all users in a time and space sharing manner, in contrast to dedicated resources that governed by a queuing system in Grid resource management. Cloud Resource allocation suffers from extra challenges abbreviated by achieving good load balancing and making right consolidation decision.

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 have wide-ranging implications in several areas of computing, notably massive knowledge, cloud computing has simply up to the highest of any list of technology subjects. Furthermore, it is one of the most important chop-chop developing technologies due to the availability of a wide range of computer infrastructure and services. The expansion of cloud computing has included mainframe computing, cluster and grid computing, distributed and parallel computing technologies, and so on. Cloud computing has mostly supplanted grid, cluster, and mainframe computing. This refers to the delivery of numerous services over the web differently. Such as databases, infrastructure, run time platforms, knowledge storage, servers, networking, and code area unit samples of these resources. Price savings, bigger productivity, speed, efficiency, high performance, high computing power, and security are simply many of the explanations why consumers and businesses embrace the on demand computing. This article goes through the definition of cloud computing as well as its properties, various cloud services, deployment models, and security concerns.

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.

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

Educational institutions throughout the World have become highly dependent on information technology for their teaching-learning, service delivery and business requirements. The term cloud computing is completely new trend and technology which is known as third party revolution after computer and internet, in distributed computing, parallel computing, grid computing and in the case of distributed database improves the power of digital library. Cloud computing is making it possible to separate the process of building an infrastructure for service provisioning from the library of providing end user services. Cloud computing provides people the way to share distributed resources and services that belong to different organizations or sites. Cloud computing share distributed resources via the network in the open environment. The digital libraries have to face with some technique and service problems along with many advantages relative to the traditional libraries, and all these produce the practical needs of cloud computing. Cloud computing has influenced information resources, information users, information personnel and information facilities of digital libraries with its unique advantages and then promoted the development of digital libraries. Developed country like Ethiopia Libraries may soon be building and managing their own data centers. Different models show that libraries maintain more control over the applications and data stores that contain sensitive, private information about patrons. Provisioning and maintenance of infrastructure for Web based digital library present several challenges. In this paper we discuss problems faced with digital library and development efforts to overcome that problem. Infrastructure virtualization and cloud computing are particularly attractive choices which is challenged by both growth in the size of the indexed document collection, new features and most prominently usage. With the overview of Cloud Computing to university library, services of libraries will have a new leap in the near future. Services provided by libraries will become more user centric, more professional and more effective, etc. And we all believe that libraries will create more knowledge benefits for Ethiopia with the help of Cloud Computing.

Grid computing integrates scattered clusters, servers, storages and networks in different geographic locations to form a virtual super-computer. Along with the development of grid computing, dealing with the data distribution requires a method which is faster and more effective for parallel applications in order to reduce data exchange between clusters. In this paper, we present two methods to reduce inter-cluster communication cost based on the consideration to different kinds of communication cost and a simple logic mapping technology. Our theoretical analyses and simulation results show the proposed methods are better than the methods without reordering processor and considering the communication cost. The performance evaluation shows that the proposed methods not only reduce communication cost successfully but also achieve a great improvement.

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.

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

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