21-04-2011, 03:13 PM
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ABSTRACT
Grid computing can mean different things to different individuals. The grand vision is often presented as an analogy to power grids where users (or electrical appliances) get access to electricity through wall sockets with no care or consideration for where or how the electricity is actually generated. In this view of grid computing, computing becomes pervasive and individual users (or client applications) gain access to computing resources (processors, storage, data, applications, and so on) as needed with little or no knowledge of where those resources are located or what the underlying technologies, hardware, operating system, and so on are. Though this vision of grid computing can capture one’s imagination and may indeed someday become a reality, there are many technical, business, political, and social issues that need to be addressed. If we consider this vision as an ultimate goal, there are many smaller steps that need to be taken to achieve it. These smaller steps each have benefits of their own. Therefore, grid computing can be seen as a journey along a path of integrating various technologies and solutions that move us closer to the final goal. Its key values are in the underlying distributed computing infrastructure technologies that are evolving in support of cross-organizational application and resource sharing—in a word, virtualization—virtualization across technologies, platforms, and organizations. This kind of virtualization is only achievable through the use of open standards. Open standards help ensure that applications can transparently take advantage of whatever appropriate resources can be made available to them. An environment that provides the ability to share and transparently access resources across a distributed and heterogeneous environment not only requires the technology to virtualize certain resources, but also technologies and standards in the areas of scheduling, security, accounting, systems management, and so on.
CHAPTER 1
What grid Computing is
Grid computing can mean different things to different individuals. The grand vision is often presented as an analogy to power grids where users (or electrical appliances) get access to electricity through wall sockets with no care or consideration for where or how the electricity is actually generated. In this view of grid computing, computing becomes pervasive and individual users (or client applications) gain access to computing resources (processors, storage, data, applications, and so on) as needed with little or no knowledge of where those resources are located or what the underlying technologies, hardware, operating system, and so on are. Though this vision of grid computing can capture one’s imagination and may indeed someday become a reality, there are many technical, business, political, and social issues that need to be addressed. If we consider this vision as an ultimate goal, there are many smaller steps that need to be taken to achieve it. These smaller steps each have benefits of their own. Therefore, grid computing can be seen as a journey along a path of integrating various technologies and solutions that move us closer to the final goal. Its key values are in the underlying distributed computing infrastructure technologies that are evolving in support of cross-organizational application and resource sharing—in a word, virtualization—virtualization across technologies, platforms, and organizations. This kind of virtualization is only achievable through the use of open standards. Open standards help ensure that applications can transparently take advantage of whatever appropriate resources can be made available to them. An environment that provides the ability to share and transparently access resources across a distributed and heterogeneous environment not only requires the technology to virtualize certain resources, but also technologies and standards in the areas of scheduling, security, accounting, systems management, and so on.
Grid computing could be defined as any of a variety of levels of virtualization along a continuum. Exactly where along that continuum one might say that a particular solution is an implementation of grid computing versus a relatively simple implementation using virtual resources is a matter of opinion. But even at the simplest levels of virtualization, one could say that grid-enabling technologies are being utilized. This continuum is illustrated in Figure 1-1 on page 5. Starting in the lower left you see single system partitioning. Virtualization starts with being able to carve up a machine into virtual machines. As you move up this spectrum you start to be able to virtualize similar or homogeneous resources. Virtualization applies not only to servers and CPUs, but to storage, networks, and even applications. As you move up this spectrum you start to virtualize unlike resources. The next step is virtualizing the enterprise, not just in a data center or within a department but across a distributed organization, and then, finally, virtualizing outside the enterprise, across the Internet, where you might actually access resources from a set of OEMs and their suppliers or you might integrate information across a network of collaborators. Early implementations of grid computing have tended to be internal to a particular company or organization. However, cross-organizational grids are also being implemented and will be an important part of computing and business optimization in the future. The distinctions between interorganizational grids and interorganizational grids are not based in technological differences. Instead, they are based on configuration choices given: Security domains, degrees of isolation desired, type of policies and their scope, and contractual obligations between users and providers of the infrastructures. These issues are not fundamentally architectural in nature. It is in the industry’s best interest to ensure that there is not an artificial split of distributed computing paradigms and models across organizational boundaries and internal IT infrastructures. Grid computing involves an evolving set of open standards for Web services and interfaces that make services, or computing resources, available over the Internet. Very often grid technologies are used on homogeneous clusters, and they can add value on those clusters by assisting, for example, with scheduling or provisioning of the resources in the cluster. The term grid, and its related technologies, applies across this entire spectrum. If we focus our attention on distributed computing solutions, then we could consider one definition of grid computing to be distributed computing across virtualized resources. The goal is to create the illusion of a simple yet large and powerful virtual computer out of a collection of connected (and possibly heterogeneous) systems sharing various combinations of resources.
CHAPTER 2
Benefits of grid computing
When you deploy a grid, it will be to meet a set of business requirements. To better match grid computing capabilities to those requirements, it is useful to keep in mind some common motivations for using grid computing.
2.1 Exploiting underutilized resources:
The potential for massive parallel CPU capacity is one of the most common visions and attractive features of a grid. In addition to pure scientific needs, such computing power is driving a new evolution in industries such as the bio-medical field, financial modeling, oil exploration, motion picture animation, and many others.
The common attribute among such uses is that the applications have been written to use algorithms that can be partitioned into independently running parts. A CPU-intensive grid application can be thought of as many smaller subjobs, each executing on a different machine in the grid. To the extent that these subjobs do not need to communicate with each other, the more scalable the application becomes. A perfectly scalable application will, for example, finish in one tenth of the time if it uses ten times the number of processors. Barriers often exist to perfect scalability. The first barrier depends on the algorithms used for splitting the application among many CPUs. If the algorithm can only be split into a limited number of independently running parts, then that forms a scalability barrier. The second barrier appears if the parts are not completely independent; this can cause contention, which can limit scalability. For example, if all of the sub jobs need to read and write from one common file or database, the access limits of that file or database will become the limiting factor in the application’s scalability. Other sources of inter-job contention in a parallel grid application include message communications latencies among the jobs, network communication capacities, synchronization protocols, input-output bandwidth to stoage or other devices, and other delays interfering with real-time requirements.
There are many factors to consider in grid-enabling an application. One must understand that not all applications can be transformed to run in parallel on a grid and achieve scalability. Furthermore, there are no practical tools for transforming arbitrary applications to exploit the parallel capabilities of a grid. There are some practical tools that skilled application designers can use to write a parallel grid application. However, automatic transformation of applications is a science in its infancy. This can be a difficult job and often requires mathematics and programming talents, if it is even possible in a given situation. New computation-intensive applications written today are being designed for parallel execution, and these will be easily grid-enabled, if they do not already follow emerging grid protocols and standards.
2.2 Parallel CPU Capacity:
The potential for massive parallel CPU capacity is one of the most common visions and attractive features of a grid. In addition to pure scientific needs, such computing power is driving a new evolution in industries such as the bio-medical field, financial modeling, oil exploration, motion picture animation, and many others.
The common attribute among such uses is that the applications have been written to use algorithms that can be partitioned into independently running parts. A CPU-intensive grid application can be thought of as many smaller subjobs, each executing on a different machine in the grid. To the extent that these subjobs do not need to communicate with each other, the more scalable the application becomes. A perfectly scalable application will, for example, finish in one tenth of the time if it uses ten times the number of processors. Barriers often exist to perfect scalability. The first barrier depends on the algorithms used for splitting the application among many CPUs. If the algorithm can only be split into a limited number of independently running parts, then that forms a scalability barrier. The second barrier appears if the parts are not completely independent; this can cause contention, which can limit scalability. For example, if all of the subjobs need to read and write from one common file or database, the access limits of that file or database will become the limiting factor in the application’s scalability. Other sources of inter-job contention in a parallel grid application include message communications latencies among the jobs, network communication capacities, synchronization protocols, input-output bandwidth to storage or other devices, and other delays interfering with real-time requirements. There are many factors to consider in grid-enabling an application. One must understand that not all applications can be transformed to run in parallel on a grid and achieve scalability. Furthermore, there are no practical tools for transforming arbitrary applications to exploit the parallel capabilities of a grid. There are some practical tools that skilled application designers can use to write a parallel grid application. However, automatic transformation of applications is a science in its infancy. This can be a difficult job and often requires mathematics and programming talents, if it is even possible in a given situation. New computation-intensive applications written today are being designed for parallel execution, and these will be easily grid-enabled, if they do not already follow emerging grid protocols and standards
2.3 Virtual resources and virtual organizations for Collaboration
Another capability enabled by grid computing is to provide an environment for collaboration among a wider audience. In the past, distributed computing promised this collaboration and achieved it to some extent. Grid computing can take these capabilities to an even wider audience, while offering important standards that enable very heterogeneous systems to work together to form the image of a large virtual computing system offering a variety of resources, as illustrated in Figure 2-1 on page 11. The users of the grid can be organized dynamically into a number of virtual organizations, each with different policy requirements. These virtual organizations can share their resources collectively as a larger grid. Sharing starts with data in the form of files or databases. A data grid can expand data capabilities in several ways. First, files or databases can span many systems and thus have larger capacities than on any single system. Such spanning can improve data transfer rates through the use of striping techniques.
Data can be duplicated throughout the grid to serve as a backup and can be hosted on or near the machines most likely to need the data, in conjunction with advanced scheduling techniques.
Sharing is not limited to files, but also includes other resources, such as specialized devices, software, services, licenses, and so on. These resourcesare virtualized to give them a more uniform interoperability among heterogeneous grid participants. The participants and users of the grid can be members of several real and virtual organizations. The grid can help in enforcing security rules among them and implement policies, which can resolve priorities for both resources and users.
ABSTRACT
Grid computing can mean different things to different individuals. The grand vision is often presented as an analogy to power grids where users (or electrical appliances) get access to electricity through wall sockets with no care or consideration for where or how the electricity is actually generated. In this view of grid computing, computing becomes pervasive and individual users (or client applications) gain access to computing resources (processors, storage, data, applications, and so on) as needed with little or no knowledge of where those resources are located or what the underlying technologies, hardware, operating system, and so on are. Though this vision of grid computing can capture one’s imagination and may indeed someday become a reality, there are many technical, business, political, and social issues that need to be addressed. If we consider this vision as an ultimate goal, there are many smaller steps that need to be taken to achieve it. These smaller steps each have benefits of their own. Therefore, grid computing can be seen as a journey along a path of integrating various technologies and solutions that move us closer to the final goal. Its key values are in the underlying distributed computing infrastructure technologies that are evolving in support of cross-organizational application and resource sharing—in a word, virtualization—virtualization across technologies, platforms, and organizations. This kind of virtualization is only achievable through the use of open standards. Open standards help ensure that applications can transparently take advantage of whatever appropriate resources can be made available to them. An environment that provides the ability to share and transparently access resources across a distributed and heterogeneous environment not only requires the technology to virtualize certain resources, but also technologies and standards in the areas of scheduling, security, accounting, systems management, and so on.
CHAPTER 1
What grid Computing is
Grid computing can mean different things to different individuals. The grand vision is often presented as an analogy to power grids where users (or electrical appliances) get access to electricity through wall sockets with no care or consideration for where or how the electricity is actually generated. In this view of grid computing, computing becomes pervasive and individual users (or client applications) gain access to computing resources (processors, storage, data, applications, and so on) as needed with little or no knowledge of where those resources are located or what the underlying technologies, hardware, operating system, and so on are. Though this vision of grid computing can capture one’s imagination and may indeed someday become a reality, there are many technical, business, political, and social issues that need to be addressed. If we consider this vision as an ultimate goal, there are many smaller steps that need to be taken to achieve it. These smaller steps each have benefits of their own. Therefore, grid computing can be seen as a journey along a path of integrating various technologies and solutions that move us closer to the final goal. Its key values are in the underlying distributed computing infrastructure technologies that are evolving in support of cross-organizational application and resource sharing—in a word, virtualization—virtualization across technologies, platforms, and organizations. This kind of virtualization is only achievable through the use of open standards. Open standards help ensure that applications can transparently take advantage of whatever appropriate resources can be made available to them. An environment that provides the ability to share and transparently access resources across a distributed and heterogeneous environment not only requires the technology to virtualize certain resources, but also technologies and standards in the areas of scheduling, security, accounting, systems management, and so on.
Grid computing could be defined as any of a variety of levels of virtualization along a continuum. Exactly where along that continuum one might say that a particular solution is an implementation of grid computing versus a relatively simple implementation using virtual resources is a matter of opinion. But even at the simplest levels of virtualization, one could say that grid-enabling technologies are being utilized. This continuum is illustrated in Figure 1-1 on page 5. Starting in the lower left you see single system partitioning. Virtualization starts with being able to carve up a machine into virtual machines. As you move up this spectrum you start to be able to virtualize similar or homogeneous resources. Virtualization applies not only to servers and CPUs, but to storage, networks, and even applications. As you move up this spectrum you start to virtualize unlike resources. The next step is virtualizing the enterprise, not just in a data center or within a department but across a distributed organization, and then, finally, virtualizing outside the enterprise, across the Internet, where you might actually access resources from a set of OEMs and their suppliers or you might integrate information across a network of collaborators. Early implementations of grid computing have tended to be internal to a particular company or organization. However, cross-organizational grids are also being implemented and will be an important part of computing and business optimization in the future. The distinctions between interorganizational grids and interorganizational grids are not based in technological differences. Instead, they are based on configuration choices given: Security domains, degrees of isolation desired, type of policies and their scope, and contractual obligations between users and providers of the infrastructures. These issues are not fundamentally architectural in nature. It is in the industry’s best interest to ensure that there is not an artificial split of distributed computing paradigms and models across organizational boundaries and internal IT infrastructures. Grid computing involves an evolving set of open standards for Web services and interfaces that make services, or computing resources, available over the Internet. Very often grid technologies are used on homogeneous clusters, and they can add value on those clusters by assisting, for example, with scheduling or provisioning of the resources in the cluster. The term grid, and its related technologies, applies across this entire spectrum. If we focus our attention on distributed computing solutions, then we could consider one definition of grid computing to be distributed computing across virtualized resources. The goal is to create the illusion of a simple yet large and powerful virtual computer out of a collection of connected (and possibly heterogeneous) systems sharing various combinations of resources.
CHAPTER 2
Benefits of grid computing
When you deploy a grid, it will be to meet a set of business requirements. To better match grid computing capabilities to those requirements, it is useful to keep in mind some common motivations for using grid computing.
2.1 Exploiting underutilized resources:
The potential for massive parallel CPU capacity is one of the most common visions and attractive features of a grid. In addition to pure scientific needs, such computing power is driving a new evolution in industries such as the bio-medical field, financial modeling, oil exploration, motion picture animation, and many others.
The common attribute among such uses is that the applications have been written to use algorithms that can be partitioned into independently running parts. A CPU-intensive grid application can be thought of as many smaller subjobs, each executing on a different machine in the grid. To the extent that these subjobs do not need to communicate with each other, the more scalable the application becomes. A perfectly scalable application will, for example, finish in one tenth of the time if it uses ten times the number of processors. Barriers often exist to perfect scalability. The first barrier depends on the algorithms used for splitting the application among many CPUs. If the algorithm can only be split into a limited number of independently running parts, then that forms a scalability barrier. The second barrier appears if the parts are not completely independent; this can cause contention, which can limit scalability. For example, if all of the sub jobs need to read and write from one common file or database, the access limits of that file or database will become the limiting factor in the application’s scalability. Other sources of inter-job contention in a parallel grid application include message communications latencies among the jobs, network communication capacities, synchronization protocols, input-output bandwidth to stoage or other devices, and other delays interfering with real-time requirements.
There are many factors to consider in grid-enabling an application. One must understand that not all applications can be transformed to run in parallel on a grid and achieve scalability. Furthermore, there are no practical tools for transforming arbitrary applications to exploit the parallel capabilities of a grid. There are some practical tools that skilled application designers can use to write a parallel grid application. However, automatic transformation of applications is a science in its infancy. This can be a difficult job and often requires mathematics and programming talents, if it is even possible in a given situation. New computation-intensive applications written today are being designed for parallel execution, and these will be easily grid-enabled, if they do not already follow emerging grid protocols and standards.
2.2 Parallel CPU Capacity:
The potential for massive parallel CPU capacity is one of the most common visions and attractive features of a grid. In addition to pure scientific needs, such computing power is driving a new evolution in industries such as the bio-medical field, financial modeling, oil exploration, motion picture animation, and many others.
The common attribute among such uses is that the applications have been written to use algorithms that can be partitioned into independently running parts. A CPU-intensive grid application can be thought of as many smaller subjobs, each executing on a different machine in the grid. To the extent that these subjobs do not need to communicate with each other, the more scalable the application becomes. A perfectly scalable application will, for example, finish in one tenth of the time if it uses ten times the number of processors. Barriers often exist to perfect scalability. The first barrier depends on the algorithms used for splitting the application among many CPUs. If the algorithm can only be split into a limited number of independently running parts, then that forms a scalability barrier. The second barrier appears if the parts are not completely independent; this can cause contention, which can limit scalability. For example, if all of the subjobs need to read and write from one common file or database, the access limits of that file or database will become the limiting factor in the application’s scalability. Other sources of inter-job contention in a parallel grid application include message communications latencies among the jobs, network communication capacities, synchronization protocols, input-output bandwidth to storage or other devices, and other delays interfering with real-time requirements. There are many factors to consider in grid-enabling an application. One must understand that not all applications can be transformed to run in parallel on a grid and achieve scalability. Furthermore, there are no practical tools for transforming arbitrary applications to exploit the parallel capabilities of a grid. There are some practical tools that skilled application designers can use to write a parallel grid application. However, automatic transformation of applications is a science in its infancy. This can be a difficult job and often requires mathematics and programming talents, if it is even possible in a given situation. New computation-intensive applications written today are being designed for parallel execution, and these will be easily grid-enabled, if they do not already follow emerging grid protocols and standards
2.3 Virtual resources and virtual organizations for Collaboration
Another capability enabled by grid computing is to provide an environment for collaboration among a wider audience. In the past, distributed computing promised this collaboration and achieved it to some extent. Grid computing can take these capabilities to an even wider audience, while offering important standards that enable very heterogeneous systems to work together to form the image of a large virtual computing system offering a variety of resources, as illustrated in Figure 2-1 on page 11. The users of the grid can be organized dynamically into a number of virtual organizations, each with different policy requirements. These virtual organizations can share their resources collectively as a larger grid. Sharing starts with data in the form of files or databases. A data grid can expand data capabilities in several ways. First, files or databases can span many systems and thus have larger capacities than on any single system. Such spanning can improve data transfer rates through the use of striping techniques.
Data can be duplicated throughout the grid to serve as a backup and can be hosted on or near the machines most likely to need the data, in conjunction with advanced scheduling techniques.
Sharing is not limited to files, but also includes other resources, such as specialized devices, software, services, licenses, and so on. These resourcesare virtualized to give them a more uniform interoperability among heterogeneous grid participants. The participants and users of the grid can be members of several real and virtual organizations. The grid can help in enforcing security rules among them and implement policies, which can resolve priorities for both resources and users.
