08-03-2011, 10:42 AM
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1. ABSTRACT:
The last decade has seen a substantial increase in computer and network performance, mainly as a result of faster hardware and more sophisticated software. Nevertheless, there are still problems, in the fields of science, engineering, and business, which cannot be effectively dealt with using the current generation of supercomputers. In fact, due to their size and complexity, these problems are often very data intensive and consequently require a variety of heterogeneous resources that are not available on a single machine. A number of teams have conducted experimental studies on the cooperative use of geographically distributed resources unified to act as a single powerful computer. This new approach is known by several names, such as metacomputing, scalable computing, global computing, Internet computing, and more recently peer-to-peer or Grid computing. Grid computing provides key infrastructure for distributed problem solving in dynamic virtual environments. It has been adopted by many scientific projects, and industrial interest is rising rapidly. However, Grids are still the domain of a few highly trained programmers with expertise in networking, high-performance computing, and operating systems.
The early efforts in Grid computing started as a project to page link supercomputing sites, but have now grown far beyond their original intent. In fact, many applications can benefit from the Grid infrastructure, including collaborative engineering, data exploration, high-throughput computing, and of course distributed supercomputing. Moreover, due to the rapid growth of the Internet and Web, there has been a rising interest in Web-based distributed computing, and many projects have been started and aim to exploit the Web as an infrastructure for running distributed and parallel applications. In this context, the Web has the capability to be a platform for parallel and collaborative work as well as a key technology to create a pervasive and ubiquitous Grid-based infrastructure.
2. INTRODUCTION:
Parallel supercomputers continue to increase in power and in ability to solve very large and complex problems in computational science. For many users, however, there are a number of practical limitations associated with these machines, including their high cost, the difficulty of obtaining access to them, and the difficulty of writing or procuring software tools that execute on them. In recent years, there has been a good deal of interest in alternative computing platforms known as computational grids, which are made up of large collections of geographically dispersed CPUs, storage, and visualization devices linked by local networks and the Internet. Of particular interest to the optimization community are computational grids that are made up of workstations, PCs and PC clusters, and supercomputer nodes, and which may be owned by a number of different individuals and institutions. Grids grant access to computer cycles that would not otherwise be used by the owners of the machines of which they are composed, without interfering with the computing activities of the machine owners. A key contribution of Grid computing is the potential for seamless aggregations of and interactions among computing, data, and information resources, which is enabling a new generation of scientific and engineering applications that are self-optimizing and dynamic data driven. However, achieving this goal requires a service-oriented Grid infrastructure that leverages standardized protocols and services to access hardware, software, and information resources Usually, grids provide sophisticated interfaces to distributed resources management as well as application execution and monitoring in wide and local area networks. These networks may connect thousands of computers by high-speed of up to 40 Gigabits/sec links. The computing resources include nodes made of thousands of processors, and terabytes of storage media.
Grid resources can be used to solve grand challenge problems in areas such as biophysics, chemistry, biology, scientific instrumentation, drug design, high energy physics, data mining, financial analysis, nuclear simulations, material science, chemical engineering, environmental studies, climate modeling, weather prediction, molecular biology, neuroscience/brain activity analysis, structural analysis, mechanical CAD/CAM, and astrophysics.
3. DISTRIBUTED COMPUTING:
Distributed Computing is an environment in which a group of independent and geographically dispersed computer systems take part to solve a complex problem, each by solving a part of the problem and then combining the result from all computers. It utilizes a network of many computers, each accomplishing a portion of an overall task, to achieve a computational result much more quicker than with a single computer. Distributed Computing normally refers to managing or pooling of hundreds or thousands of computer systems which individually are limited in their memory and processing power. These systems are loosely coupled systems coordinately working with a common goal.
4. THE BASICS OF GRID COMPUTING:
The term grid computing originated in the early 1990s as a metaphor for making computer power as easy to access as an electrical power grid.
Grid computing is a form of distributed computing that involves coordinating and sharing computing, application, data, storage or network resources across dynamic and geographically dispersed organizations. However the vision of a large scale resource sharing is not yet a reality in many areas as Grid computing is an evolving area of computing, while standards and technology are still being developed to enable this new paradigm.
Grid computing offers a model for solving massive computational problems by making use of the unused resources (CPU cycles and/or disk storage) of large numbers of disparate, often desktop, computers treated as a virtual cluster embedded in a distributed telecommunications infrastructure. It is an emerging computing model that provides the ability to perform higher throughput computing by taking advantage of many networked computers to model a virtual computer architecture that is able to distribute process execution across a parallel infrastructure. Grids use the resources of many separate computers connected by a network (usually the internet) to solve large-scale computation problems. They provide the ability to perform computations on large data sets, by breaking them down into many smaller ones, or provide the ability to perform many more computations at once than would be possible on a single computer, by modeling a parallel division of labour between processes. Many use the idle time on many thousands of computers throughout the world. Such arrangements permit handling of data that would otherwise require the power of expensive super computers or would have been impossible to analyze otherwise.
Grid computing has the design goal of solving problems too big for any single supercomputer, while retaining the flexibility to work on multiple smaller problems. Thus grid computing provides a multi-user environment. Its secondary aims are: better exploitation of the available computing power, and catering for the intermittent demands of large computational exercises. Grid Computing can be seen as a super set of distributed computing.
Functionally one can classify grids into several types:
• Computational Grids: which focus primarily on computationally intensive operations.
• Data Grids: which control the sharing or management of large amount of distributed data.
• Equipment Grids: which have a primary piece of equipment e.g., a telescope, and where the surrounding grid is used to control the equipment remotely and to analyze the data produced.
Many projects using grid computing are covering tasks such as protein folding, research into drugs for cancer, mathematical problems and climate models. Most of these projects work by running as a screensaver on users' personal computers, which process small pieces of the overall data while the computer is either completely idle or lightly used. These programs generally run in the background or as a screensaver when the user does not use the entire computing power of the PC. Many such projects have made progress in fields that would have otherwise taken prohibitive investment or a delay in/on results.
1. ABSTRACT:
The last decade has seen a substantial increase in computer and network performance, mainly as a result of faster hardware and more sophisticated software. Nevertheless, there are still problems, in the fields of science, engineering, and business, which cannot be effectively dealt with using the current generation of supercomputers. In fact, due to their size and complexity, these problems are often very data intensive and consequently require a variety of heterogeneous resources that are not available on a single machine. A number of teams have conducted experimental studies on the cooperative use of geographically distributed resources unified to act as a single powerful computer. This new approach is known by several names, such as metacomputing, scalable computing, global computing, Internet computing, and more recently peer-to-peer or Grid computing. Grid computing provides key infrastructure for distributed problem solving in dynamic virtual environments. It has been adopted by many scientific projects, and industrial interest is rising rapidly. However, Grids are still the domain of a few highly trained programmers with expertise in networking, high-performance computing, and operating systems.
The early efforts in Grid computing started as a project to page link supercomputing sites, but have now grown far beyond their original intent. In fact, many applications can benefit from the Grid infrastructure, including collaborative engineering, data exploration, high-throughput computing, and of course distributed supercomputing. Moreover, due to the rapid growth of the Internet and Web, there has been a rising interest in Web-based distributed computing, and many projects have been started and aim to exploit the Web as an infrastructure for running distributed and parallel applications. In this context, the Web has the capability to be a platform for parallel and collaborative work as well as a key technology to create a pervasive and ubiquitous Grid-based infrastructure.
2. INTRODUCTION:
Parallel supercomputers continue to increase in power and in ability to solve very large and complex problems in computational science. For many users, however, there are a number of practical limitations associated with these machines, including their high cost, the difficulty of obtaining access to them, and the difficulty of writing or procuring software tools that execute on them. In recent years, there has been a good deal of interest in alternative computing platforms known as computational grids, which are made up of large collections of geographically dispersed CPUs, storage, and visualization devices linked by local networks and the Internet. Of particular interest to the optimization community are computational grids that are made up of workstations, PCs and PC clusters, and supercomputer nodes, and which may be owned by a number of different individuals and institutions. Grids grant access to computer cycles that would not otherwise be used by the owners of the machines of which they are composed, without interfering with the computing activities of the machine owners. A key contribution of Grid computing is the potential for seamless aggregations of and interactions among computing, data, and information resources, which is enabling a new generation of scientific and engineering applications that are self-optimizing and dynamic data driven. However, achieving this goal requires a service-oriented Grid infrastructure that leverages standardized protocols and services to access hardware, software, and information resources Usually, grids provide sophisticated interfaces to distributed resources management as well as application execution and monitoring in wide and local area networks. These networks may connect thousands of computers by high-speed of up to 40 Gigabits/sec links. The computing resources include nodes made of thousands of processors, and terabytes of storage media.
Grid resources can be used to solve grand challenge problems in areas such as biophysics, chemistry, biology, scientific instrumentation, drug design, high energy physics, data mining, financial analysis, nuclear simulations, material science, chemical engineering, environmental studies, climate modeling, weather prediction, molecular biology, neuroscience/brain activity analysis, structural analysis, mechanical CAD/CAM, and astrophysics.
3. DISTRIBUTED COMPUTING:
Distributed Computing is an environment in which a group of independent and geographically dispersed computer systems take part to solve a complex problem, each by solving a part of the problem and then combining the result from all computers. It utilizes a network of many computers, each accomplishing a portion of an overall task, to achieve a computational result much more quicker than with a single computer. Distributed Computing normally refers to managing or pooling of hundreds or thousands of computer systems which individually are limited in their memory and processing power. These systems are loosely coupled systems coordinately working with a common goal.
4. THE BASICS OF GRID COMPUTING:
The term grid computing originated in the early 1990s as a metaphor for making computer power as easy to access as an electrical power grid.
Grid computing is a form of distributed computing that involves coordinating and sharing computing, application, data, storage or network resources across dynamic and geographically dispersed organizations. However the vision of a large scale resource sharing is not yet a reality in many areas as Grid computing is an evolving area of computing, while standards and technology are still being developed to enable this new paradigm.
Grid computing offers a model for solving massive computational problems by making use of the unused resources (CPU cycles and/or disk storage) of large numbers of disparate, often desktop, computers treated as a virtual cluster embedded in a distributed telecommunications infrastructure. It is an emerging computing model that provides the ability to perform higher throughput computing by taking advantage of many networked computers to model a virtual computer architecture that is able to distribute process execution across a parallel infrastructure. Grids use the resources of many separate computers connected by a network (usually the internet) to solve large-scale computation problems. They provide the ability to perform computations on large data sets, by breaking them down into many smaller ones, or provide the ability to perform many more computations at once than would be possible on a single computer, by modeling a parallel division of labour between processes. Many use the idle time on many thousands of computers throughout the world. Such arrangements permit handling of data that would otherwise require the power of expensive super computers or would have been impossible to analyze otherwise.
Grid computing has the design goal of solving problems too big for any single supercomputer, while retaining the flexibility to work on multiple smaller problems. Thus grid computing provides a multi-user environment. Its secondary aims are: better exploitation of the available computing power, and catering for the intermittent demands of large computational exercises. Grid Computing can be seen as a super set of distributed computing.
Functionally one can classify grids into several types:
• Computational Grids: which focus primarily on computationally intensive operations.
• Data Grids: which control the sharing or management of large amount of distributed data.
• Equipment Grids: which have a primary piece of equipment e.g., a telescope, and where the surrounding grid is used to control the equipment remotely and to analyze the data produced.
Many projects using grid computing are covering tasks such as protein folding, research into drugs for cancer, mathematical problems and climate models. Most of these projects work by running as a screensaver on users' personal computers, which process small pieces of the overall data while the computer is either completely idle or lightly used. These programs generally run in the background or as a screensaver when the user does not use the entire computing power of the PC. Many such projects have made progress in fields that would have otherwise taken prohibitive investment or a delay in/on results.
