Computing Technologies and Practical Utility

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Computing Technologies and Practical Utility ISSN 2319-8885 Vol.04,Issue.37, September-2015, Pages:8054-8066 www.ijsetr.com Computing Technologies and Practical Utility K. J. SARMA SMIEEE, Professor, Dept of Humanities and Sciences and Academic Cell, Malla Reddy Engineering College (Autonomous), Secunderabad, TS, India, E-mail:[email protected]. Abstract: Several computing technologies have emerged by the end of 20 th century due to the high speed Broadband internet with each having a number of applications in science, engineering, business, social systems, and governments. These technologies brought revolutionary changes in communications and computational processes. In- fact the advances in hardware, investigation of sophisticated rigorous mathematical algorithms are also responsible for greater shift in these developments. Some of these computing technologies are cluster, grid, and cloud, cloud-mobile. Multi-cloud, parallel, concurrent, distributed, DNA, mobile, high performance, utility, global cooperative, functional, ubiquitous, pervasive, exploratory, quantum, scalable computing, ubiquitous secure, computational grid, etc. In this paper we review uses and practical applications. Research into enhancing the technology, utility in various application areas have been growing at fast phase. It may stimulate further study of each and the convergence of all these technologies in dealing with logistic based problems of large dimensionality. Keywords: Cluster, Grid, Cloud, Distributed, DNA, Mobile, High Performance, Ubiquitous Pervasive, Quantum. I. INTRODUCTION computing clusters. Desktop work-stations which can also Cluster computing is the topic of research among become part of a cluster when they’re not being used. academics, industry community, system designers, network Financial services firm, which probably has many high- developers, language designers, technical forums, algorithm powered workstations that sit idle overnight. Cloud instances developers. This is also finding many business applications can be created on demand and used as long as needed, then & production management, projects of graduate students and shut down with the help of public cloud platforms. research of faculties. The use of clusters as computing platform also has several scientific and engineering applications. A simple computer cluster may be just connecting two personal computers, or may be a very fast supercomputer. A basic approach to building a cluster is that of a Beowulf [1] cluster which may be built with a few personal computers to produce a cost-effective computing. The first project conceptualized consisted of 133-node Stone Super computer [2]. The developers used Linux, the Parallel Virtual Machine toolkit. Thus computer clustering consist a number of readily available computing nodes (e.g. personal computers used as servers) via a fast local area network. The activities of the computing nodes are orchestrated by "clustering middleware". Cluster computing can also be described as a fusion of the field’s parallel, high- performance, distributed, and high-availability computing. This is a group of server machines installed in an organization’s data center forming a cluster, managing a Fig.1. significant number of the servers; then trying to keep them busy to justify the investment. This makes sense only for The usage of computer technologies incurs costs of organizations with a substantial ongoing requirement which runs applications on a cluster. Today’s researchers processors as well as other computer sub-systems (such as developed many other ways. One being a cluster created motherboard, memory, hard disks, and network cards). The using Windows HPC Server 2008 R2 containing standardization of network hardware and software protocols combination of on-premises servers, as traditional has also enhanced the confidence in using cluster computing. Since the invention of computers, there has been a Copyright @ 2015 IJSETR. All rights reserved. K. J. SARMA requirement for greater processing power. In-fact solving distinguished from conventional high performance scientific problems with large dimensionality and good computing systems, such as cluster computing. In a grid, precision encouraged the need to improve processing power. computers have each node set to perform a different A cluster designed to meet these needs is termed as a ―high application. performance‖ cluster. But all problems are not amenable to cluster solutions and depend on the communication costs of the algorithm used to solve the problem. There is a wide class of problems which can be solved with low expenditure and effectively using clusters. Parametric modeling is one area, where in ―embarrassingly parallel‖ problems such as brute-force cracking of encryption keys is made use of. As we grow more dependent, the cost of failures is increasing dramatically. Combined with computer systems’ reputation for poor reliability, meaning that there is a great impetus to develop and deploy cluster computer solutions; to ensure that computer systems can be used effectively, i.e., twenty-four hours a day. A cluster designed to meet these needs is a ―high availability‖ cluster. One feature of cluster systems is a gradual degradation, whereby a system fails slowly but with prior warning, so that remedial action can be taken in time to avoid a catastrophe. Thus we can take an action by placing the nodes of the clusters in separate physical locations. This means even when Fig.2. artificial or natural disasters like power failure, earthquake, Grid computers also tend to be more heterogeneous and fire, flood and riots occur computer systems can continue to geographically dispersed than cluster computers[4]. operate. Some of the Advantages of Cluster Computing are Sometimes a single grid will be dedicated to a particular Manageability: A cluster with large number of application, depending on the nature and complexity of components will be combined to work as a single entity application. Thus Grids are often constructed with general- which means management becomes easy. purpose grid middleware software libraries. Grid sizes can be Single System Image: Here the user of the cluster gets a quite large [5]. Grids are a form of distributed computing feel that he is working with a single system though he is source station wherein a ―super virtual computer” is working with a large number of components. In other composed of many networked, loosely coupled computers words the user manages a single system image. 3. High are used; to perform large tasks. For certain applications, Availability: Even if one component fails due to some ―distributed‖ or ―grid‖ computing, can be seen as a special technical problem, then some other component takes its type of parallel computing systems that relies on complete set place and the user can continue to work with the system, of computers (with onboard CPUs, storage, power supplies, as all the components are replicas of each other. network interfaces, etc). These are connected to a computer network (private or public) by a conventional network The Disadvantages of Cluster Computing interface, such as Ethernet. This is in contrast to the Programmability Issues: This may be because the traditional notion of a supercomputer, which has many components being different in terms of software from each processors connected by a local high-speed computer bus [6]. other. There may also be issues when combining all of them The integration of grid resources and services on Internet together as a single entity. became convenient and flexible because of the combination Problem in Finding Fault: Because we are dealing with a of grid computing and web services which are used for single entity, so problems may arise when finding-out fault various grid applications. To foster state-of-the-art research that which of the components has some problem associated in the area of grid computing and applications we must focus with it. on all aspects of grid technologies. Difficult to handle by a Layman: As cluster computing We must present novel results and solutions to solve involves merging different or same components together with various applications and challenges in grid platforms. different programmability, so a non-professional person finds Scientists use grid computing for their research, which it difficult to manage [9]. also consists ―resource sharing". Some of the Advantages of Grid Computing II. GRID COMPUTING Access to Additional Resources: In addition to CPU Grid computing is a collection of computer resources from and other storage resources, a grid can also provide other multiple locations to reach a common goal. The concept of resources also. grid computing allows users to have computing on demand Resource Balancing: A grid incorporates large number according to the need. The grid can be thought of as of systems into a single system. For applications that are a distributed system with non-interactive workloads that grid enabled, grid performs we must balance by involves a large number of files. Grid computing can be International Journal of Scientific Engineering and Technology Research Volume.04, IssueNo.37, September-2015, Pages: 8054--8066 Computing Technologies and Practical Utility scheduling grid jobs on machines that are showing low utilization. Reliability: The systems in grid are cheap and geographically dispersed. For example, there is power or cooling failure at one site, then that will not affect the other site, thus high reliability will be there specially in case of real time systems [11].
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