13-02-2010, 04:56 PM
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INTRODUCTION
The demand for computing power continues to grow. Increased network bandwidth, more powerful computers and storage systems, and sophisticated software applications promise solutions to a lot of business and technical computing problems. But harnessing these new abilities means dealing with the challenge of growing workload demands. Organisations face many challenges as they strive to remain competitive. Reduced computing costs, greater throughput, faster time-to-market, and improved quality and innovation are all important. Investments in hardware need to be carefully justified, and organizations must find ways to accomplish more with available resources. Flexibility is key, as enterprises need to handle dynamically changing workloads and quickly provide computing power where it is needed most. Even though the demand for computing resources is great, many existing systems are underutilized. While a few individual serves may be working at capacity, the vast majority of systems are not. As a result, many computing cycles are left unused.
Grid computing enables organizations to use their distributed computing resources more efficiently and flexibly, providing more power out of existing systems and helping organizations gain a competitive business advantages. Grids enable the sharing selection, and aggregation of a wide variety of resources including supercomputer, storage systems, data sources, and specialized devices that are geographically distributed and owned by different organizations for solving large-scale computational and data intensive problems in sciences, engineering, and commerce. Thus enterprises or organizations come together to share resources and skills in order to better respond to business opportunities or large-scale application processing requirements, and whose cooperation is supported by computer networks.
WHAT IS GRID COMPUTING
Distributed Processing Control Data (for batch job servers)
Grid computing is applying the resources of many computers in a network to a single problem at the same time-usually to a scientific or technical problem that requires a great number of computer processing cycles or access to large amounts of data. Grid computing is concerned with coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations. The sharing that we are concerned with is not primarily file exchange but rather direct access to computers, software, data, and other resources, as is required by a range of collaborative problem-solving and resource brokering strategic emerging in industry, science, and engineering.
Other System Data I
¦ database and Internet session control ' Attached Objects
Data Dictionary
' DBMS-independent logical schema 1 indexing metadata
User Administration and Security Data
¦ user it), name, group, log file
¦ batch / interactive modes
¦ full and lightweight users
- associated configurations
- security groups and rules
A Grid is a collection of computing resources connected through network that perform tasks. It appears to users as a large system, providing a single point of access to powerful distributed resources. Grid middleware support a common set aggregates these resources and provides transparent, remote, and secure access to computing power wherever and whenever it is needed i.e., grid computing aggregates resources and delivers computing power to every user in the network. Users treat the Grid as a single computational resource. Users can submit thousands of jobs at a time without being concerned about where they run. Grid computing is currently used in technical computing environments, to provide more resources for compute-intensive tasks.
A computational grid is a hardware and software infrastructure that provides :-:-;endable, consistent, pervasive and inexpensive access to high-end computational capabilities. Grid computing requires the use of software that can divide and farm out Pieces of a program to as many as several thousand computers. Grid computing can be thought of as distributed and large-scale cluster computing and as a form of network dfetributed processing. In short a grid is a system that co-ordinates resources that are not subject to centralized control using standard, open, general-purpose protocols and Interfaces to deliver nontrivial qualities of service.
ORIGIN OF GRID COMPUTING
In the 1980s, the National Science Foundation created the NSFnet: a communications network intended to give scientific researchers easy access to its new supercomputer centers. Very quickly, one smaller network after another linked in-and the result was the internet. The popularity of the internet as well as the availability of powerful computers and high-speed network technologies as low-cost commodity components is changing the way we use computers today. These technology opportunities have led to the possibility of using distributed computers as a single, unified computing resource, leading to what is popularly known as Grid computing. The term "the Grid" was coined in the mid 1990s to denote a proposed distributed computing infrastructure for advanced science and engineering.
Considerable progress has since been made on the construction of such an infrastructure, but the term "Grid" has also been conflated, at least in popular perception, to embrace everything from advanced networking to artificial intelligence.
DIFFERENT TYPES OF GRID
No two grids are alike. Several distinct types of grids are commonly used in industry today. Three of the most common are compute grids, data grids and access grids:
Compute grid - distributed compute resources consisting of desktop, server, and High Performance Computing (HPC) systems. Computing resources that are managed and collectively made available to meet an organizations computing needs, computational grids enable sharing, selection, and aggregation of geographically distributed resources, such as computers, data sources, and scientific instruments, for solving large-scale problems in science, engineering and commerce.
Access
National/
international
Grids
Access grid - distributed audio-visual equipment, such as cameras, microphones, speakers, and video screens, set up to provide a virtual collective presentation room.
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Data grid - distributed storage devices including disk and tape devices along with the necessary software to migrate data as needed.
LEVELS OF DEPLOYMENT
Grid computing can be divided into three logical levels of deployment: cluster grids, campus grid and global grids as shown in figure.
Cluster Grids are the simplest, consisting of one or more systems interconnected through a network working together to provide a single point of access to users in a single department. Cluster Grids may contain distributed workstations and servers, as well as centralized resources in a datacenter environment. Typically owned and used by a single project or department, Cluster Grids support both high throughput and high performance jobs. Common examples of the Cluster Grid architecture include compute farms, groups of multi-processor HPC systems etc.
Campus Grids: As capacity needs increase, multiple cluster grids can be combined into a campus grid which enables multiple departments within an organization to share computing resources. Organizations can use campus grids to handle a wide variety of tasks. Campus grids typically contain resources from multiple administrative domains, but are located in the same geographic location.
Global grids are a collection of campus grids that cross organizational boundaries to create very large virtual systems. Users have access to compute power that far exceeds the resources available within their own organization. Computing resources may be geographically dispersed, connecting sites around the globe. Designed to support and address the needs of multiple sites and organizations sharing resources, global provide the power of distributed resources to users anywhere in the world.
THE GRID ARCHITECTURE
> Three tier system architecture
> Layered architecture
THE THREE TIER SYSTEM ARCHITECTURE
It is the architecture for a typical cluster grid. Cluster grids employ standard three-tier system architecture, as shown in figure. The architecture includes front-end access nodes, middle-tier management nodes, and back-end compute nodes. Nodes in the access tier are used for submit, control and monitor jobs, compute tier nodes are used to execute jobs and the management tier nodes run the software needed to implement the cluster grid.
Access Tier
The access tier provides access and authentication services to the cluster grid access. Alternatively, web-based services can be provided to permit easy-or tightly-controlled-access to the facility.
Protocols can also be implemented to allow external services like accounting or analysis programs. Any access method should, of course, be able to integrate with common authentication schemes such as NIS, LDAP, and Kerberos. Furthermore, facilities for mapping external user identities to a local identity can be considered.
Management Tier
This middle tier include one or more servers which run the server elements of client-server software such as Distributed Resource Management (DRM), hardware diagnosis software, and system performance monitors. Additional duties of servers in this tier may also include:
> File server - provide NFS service to other nodes in the Cluster grid.
> License key server - manage software license keys for the cluster grid.
> Software provisioning server-manage operating system and application, software versioning and patch application on other nodes in the cluster grid.
The size and number of servers in this tier will vary depending on the type and level of services to be provided. For small implementations with limited functionality, a single services to be chosen to host all management services for ease of administration. Alternatively, these functions may be provided by multiple servers for greater scalability and flexibility.
Compute Tier
This tier supplies the compute power for the cluster grid. Jobs submitted through tier. Nodes in this tier run the client-side of the DRM software, the daemons associated with message-passing environments, and any agents for system health monitoring. The compute tier communicates with the management tier, receiving jobs to run and reporting job completion status and accounting details.
THE LAYERED ARCHITECTURE
Our goal in describing our grid architecture is to identify requirements for general classes of component. The grid architecture identifies fundamental system components, specifies the purpose and function of these components, and indicates how these components interest with one another. In defining a grid architecture, interoperability is the central issue to be addressed. In a networked environment, interoperability means common protocols. A protocol definition specifies how distributed system elements
Dept of C S E -8-
interest with one another in order to achieve a specified behavior, and the structure of the information exchanged during this interaction.
The layered grid architecture is a protocol architecture, with protocols defining the basic mechanisms by which users and resources establish, manage, and exploit sharing relationships. A standards-based open architecture facilities extensibility, interoperability, portability, and code sharing; standard protocols make it easy to define standard services that provide enhanced capabilities. Standard abstractions, APIs, and SDKs can accelerate code development, enable code sharing, and enhance application portability. APIs and SDKs are an adjunct to, not an alternative to, protocols and services, first; and APIs and SDKs, second.
Our architecture organizes components into layers, as shown in Figure. Components within each layer share common characteristics but can build on capabilities and behaviors provided by any lower layer. The architecture consists of resource and connectivity protocols, which facilities the sharing of individual resources.
THE FABRIC LAYER
Protocols defined at the Fabric layer can be used to construct a wide range of global services and application-specific behaviors at the collective layer. The grid fabric layer provides the resources to which shared access is mediated by grid protocols. Fabric components implement the local, resource-specific operations that occur on specific resources as a result of sharing operating at higher levels. There is thus a tight interdependence between the function implemented at the Fabric level and the sharing operating supported.
THE CONNECTIVITY LAYER
The connectivity layer defines core communication and authentication protocols required for grid-specific network transactions. Communication protocols enable the exchange of data between fabric layer resources. Authentication protocols provide cryptographically secure mechanisms for verifying the identity of users and resources. Communication requirements include transport, routing, and naming. With respect to security aspects of the connectivity layer, we observe that the complexity of the security problem makes it important that any solutions be based on existing standards whenever possible. Users must be able to "long on" (authenticate) just once and then have access
to multiple grid resources defined in the fabric layer, without further user intervention. The internet protocols (the internet (IP and ICMP), transport (TCP, UDP), and application (DNS, OSPF, RSVP, etc) are used for communication. Grid security infrastructure (GSI) protocols are used for authentication, communication protection, and authorization. GSI builds on and extends the Transport layer security (TLS) protocols (29) to address most of the issues listed above:
THE RESOURCE LAYER
The resource layer builds on connectivity layer communication and authentication protocols to define protocols for the secure negotiation, initiation, monitoring, control, accounting, and payment of sharing operations on individual resources. Resource layer implementations of these protocols call Fabric layer functions to access and control local resources. Resource layer protocols are concerned entirely with individual resources and hence ignore issues of global state and atomic actions across distributed collections. Two primary classes of resource layer protocols can be distinguished:
Information protocols are used to negotiate access to a shared resource.
Management protocols are used to negotiate access to a shared resource.
These protocols must be chosen so as to capture the fundamental mechanisms of sharing across many different resource types.
> A grid resource information protocol and associated information model.
> An associated soft-state resource registration protocol, the grid resource registration servers.
> The grid resource access and management (GRAM) protocol is used for allocation of computational resources and for monitoring and control of computation on those resources.
> An extended version of the file transfer protocol, grid FTP, is a management protocol for data access.
> LDAP is used as a catalog access protocol.
THE COLLECTIVE LAYER
The collective layer is global in nature and contains protocols and services that are not associated with any one specific resource n capture interactions across collections of resources. The collective components build on the narrow resource and connectivity layer can implement a wide variety of sharing behaviors without placing new requirements on the resources being shared.
Directory services allow participants to discover the existence and/or properties of resources. Resource-level GRRP and GRIP protocols are used to construct directories, Co-allocation, scheduling, and brokering services allow participants to request the allocation of one or more resources for a specific purpose and the scheduling of tasks on the appropriate resources.
Monitoring and diagnostics services support the monitoring of resources for failure, adversarial attack, overload, and so forth.
Data replication services support the management of storage resources to maximize data access performance with respect to metrics such as response time, reliability, and cost.
Grid-enabled programming systems enable familiar programming models to be used in grid environments, using various grid services to address resource discovery, security, resource allocation, and other concerns.
Workload management systems and collaboration frameworks provide for the description, use, and management of multi-step, asynchronous, multi-component workflows.
Software discovery services discover and select the best software implementation and execution platform based on the parameters of the problem being solved. Community authorization servers enforce community policies governing resource access, generating capabilities that community members can use to access community resources.
Community accounting and payment services gather resource usage information for the purpose of accounting, payment, and/or limiting of resource usage by community members,
Collaborating service support the coordinated exchange of information within potentially large user communities, whether synchronously or asynchronously.
DeptofC S E
-11-
THE APPLICATION LAYER
The application layer comprises the user applications that operate within an organization environment. Applications are constructed in terms of services defined at any layer. At each layer, we have well-defined protocols that provide access to some useful service: resource management, data access, resource discovery, and so forth. At each layer, APIs may also be defined whose implementation (ideally provided by third-party SDKs) exchange protocol messages with the appropriate service(s) to perform desired actions.
The layered grid architecture and its relationship to the internet protocol architecture. Because the internet protocol architecture extends from network to application, there is a mapping from grid layers into internet layers.
GRID & WEB
The grid is a next-generation internet. "The grid" is not an alternative to "the internet": it is rather a set of additional protocols and sen/ices that build on internet protocols and services to support the creation and use of computation or it is a layer of software and services that "sits on top of operating systems and links different systems together, allowing them to share resources - and data-enriched environments. Any resource that is "on the grid" is also, by definition, "on the Net".
The Web is not (yet) a grid: its open, general-purpose protocols support access to distributed resources but not the coordinated use of those resources to deliver interesting qualities of service. Grid computing has been proclaimed as the successor to the Web. Current internet technologies address communication and information exchange among computers but do not provide integrated approaches to the coordinated use of resource at multiple sites for computation. By adding the ability to extensively share computing power, applications and storage to the Web's current ability to share text and multimedia files, problems that require a lot of computing resources can be resolved, devices can work past their own limits and collaboration can become more intense. Let's investigate each of these more closely. The Web has z'ovided a good test bed for grid computing, both through its successes and its Shortcomings. For grid computing to prosper, it will need to solve problems of standards, property rights, access and authorization and modularization and dispatching.
> Standards: there are no clear standards for elements such as security and quality of service.
> Property rights - most software used today is proprietary, and a significant amount of code being written for grid computing is owned by specific businesses. Beyond fair and legal return for the property, proprietary software may stunt the growth and development of grid computing or even determine its future direction.
> Access and authorization - Are all resources available to everyone Most people would not mind if others used spare cycles on their systems in exchange for similar consideration, but content and personal intellectual property (such as term papers and proprietary bidding software) might be more problematic. In addition, security and property rights must be protected.
> Dispatching and modularization - How are resources shared What is the priority Where are assignments dispatched How does one deal with latency These are all nontrivial concerns, and the answers are not clear. A more difficult problem is modularizing code to distribute processing.
The path to the future for grid computing will vary depending upon how these problems are solved. Internet traffic could grow eight times more than forecast over the next decade because of commercial adoption of grid computing.
BENEFITS OF GRID COMPUTING
Grid computing is a model for allowing companies to use a large number of computing resources on demand, no matter where they are located. Grid computing can provide many benefits not available with traditional computing models:
> Better utilization of resources - grid computing uses distributed resources more efficiently and delivers more usable computing power. This can decrease time-to-market, allow for innovation, or enable additional testing and simulation for improved product quality. By employing existing resources, grid computing helps protect IT investments, containing costs while providing more capacity.
> Increased user productivity - by providing transparent access to resources, work can be completed more quickly. Users gain additional productivity as they can focus on design and development rather than wasting valuable time hunting for resources and manually scheduling and managing large numbers of jobs.
> Scalability - Grid can grow seamlessly over time, allowing many thousands of processors to be integrated into one cluster. Components can be updated independently and additional resources can be added as needed, reducing large one-time expenses.
> Flexibility - Grid computing provides computing power where it is needed most, helping to better meet dynamically changing work loads. Grids can contain heterogeneous compute nodes, allowing resources to be added and removed as needs dictate.
> Uniformity - All resources and data should appear to come from the same source even when they don't. Beckhardt refers back to his electrical utility analogy, saying, "When you plug into the wall, you don't know if the power is coming from PG & E or Con Edison."
> Transparency - Researchers should be able to manipulate all of the data available on the grid regardless of the source. In other words, data in different formats and file types should be integrated into a virtual database.
> Reliability - The grid should almost always be available. Think fault tolerance and redundancy. Achieving this requires robust storage, multiple power sources,
and sophisticated networking.
> Pervasiveness - Resources connected to the grid should be available to as many people as possible - a tremendous challenge given the range of platforms and operating systems employed by intended BioGrid users.
> Security - If the databases contain intellectual property, they must be protected. Beckhardt says that a tendency among early grid efforts to neglect data security "has to be resolved before (grid computing) becomes a commercial reality".
APPLICATIONS
The concepts of grid computing started as a project to page link geographically dispersed supercomputers, but now it has grown far beyond its original intent. A grid platform couid be used for many different types of applications. The applications are categorized into four main classes: including collaborative engineering, data exploration, high throughput computing, and distributed supercomputing.
Distributed supercomputing
Distributed supercomputing applications have large computational requirements that can be met only by simultaneous, execution across multiple supercomputers. Distributed computing is a science which solves a large problem by giving small parts of the problem to many computers to solve and then combining the solutions for the parts into a solution for the problem.
Recent distributed computing projects have been designed to use the computers of hundreds of thousands of volunteers all over the world, via the internet. These projects are so large, and require so much computing power to solve, the they would be impossible for any one computer or person to solve in a reasonable amount of time.
Teleimmersive applications
Teleimmersive applications combine simulation, virtual reality, and collaborative environments to provide a shared, virtual design space. Teleimmersive applications can be extremely demanding, requiring large amounts of computation and stringent and diverse network support.
Smart instruments
Computational grids can enhance the power of scientific instruments by providing access to data archives and on-line processing capabilities.
Data intensive applications
Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. This encompasses a number of different finical approaches, such as clustering, data summarizations, learning classification t5 finding dependency net works, analyzing changes, and detecting anomalies. Data analysis tends to work from the data up and the best techniques are those
developed with an orientation towards large volumes of data, making use of as much of the collected data as possible to arrive at reliable conclusions and decisions. The analysis process starts with a set of data, uses a methodology to develop an optimal representation of the structure of the data during which time knowledge is acquired. One knowledge has been acquired this can be extended to larger sets of data working on the assumption that the larger data set has a structure similar to the sample data.
Collaborative design
When collaboration is conducted in a distributed and indirect way, information such as documented results of design decisions and ideas can be shared easily among participants of a working team.
In design behavior, conflicts and argument are regular. Hence designers need to dynamically exchange their opinions both formally and informally using multi media integrated into the system. Collaborative software can help to manage and record information of every process of the product design. Computer based design environment integrates the various knowledge and experience together. Every team member is able to contribute ideas, and the team can jointly explore design concepts early in the project.
CONCLUSION
Applying the resources of many computers in a network to a single problem at the same time - usually a scientific or technical problem that requires a great number of computer processing cycles or access to large amounts of data. Grid computing uses software to divide and farm out pieces of a program to as many as several thousand computers. A number of corporations, professional groups and university consortia have developed frameworks and software for managing grid computing projects. Grid computing provides clustering of remotely distributed computing. The principal focus of grid computing to date has been on maximizing the use of available processor resources for compute-intensive applications. Grid computing along with storage virtualization and ser CONTENTS
1. Introduction '
2. What is grid computing 2
3. Origin of grid computing 3
4. Different type of grid 4
5. Levels of developments 6
6. The Grid Architecture 7
7. The Three Tire System Architecture 7
8. The Layered Architecture 8
9. Grid And Web 12
10. Benefits of Grid Computing 14
I I. Applications *»
12. Conclusion '°
er virtua
INTRODUCTION
The demand for computing power continues to grow. Increased network bandwidth, more powerful computers and storage systems, and sophisticated software applications promise solutions to a lot of business and technical computing problems. But harnessing these new abilities means dealing with the challenge of growing workload demands. Organisations face many challenges as they strive to remain competitive. Reduced computing costs, greater throughput, faster time-to-market, and improved quality and innovation are all important. Investments in hardware need to be carefully justified, and organizations must find ways to accomplish more with available resources. Flexibility is key, as enterprises need to handle dynamically changing workloads and quickly provide computing power where it is needed most. Even though the demand for computing resources is great, many existing systems are underutilized. While a few individual serves may be working at capacity, the vast majority of systems are not. As a result, many computing cycles are left unused.
Grid computing enables organizations to use their distributed computing resources more efficiently and flexibly, providing more power out of existing systems and helping organizations gain a competitive business advantages. Grids enable the sharing selection, and aggregation of a wide variety of resources including supercomputer, storage systems, data sources, and specialized devices that are geographically distributed and owned by different organizations for solving large-scale computational and data intensive problems in sciences, engineering, and commerce. Thus enterprises or organizations come together to share resources and skills in order to better respond to business opportunities or large-scale application processing requirements, and whose cooperation is supported by computer networks.
WHAT IS GRID COMPUTING
Distributed Processing Control Data (for batch job servers)
Grid computing is applying the resources of many computers in a network to a single problem at the same time-usually to a scientific or technical problem that requires a great number of computer processing cycles or access to large amounts of data. Grid computing is concerned with coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations. The sharing that we are concerned with is not primarily file exchange but rather direct access to computers, software, data, and other resources, as is required by a range of collaborative problem-solving and resource brokering strategic emerging in industry, science, and engineering.
Other System Data I
¦ database and Internet session control ' Attached Objects
Data Dictionary
' DBMS-independent logical schema 1 indexing metadata
User Administration and Security Data
¦ user it), name, group, log file
¦ batch / interactive modes
¦ full and lightweight users
- associated configurations
- security groups and rules
A Grid is a collection of computing resources connected through network that perform tasks. It appears to users as a large system, providing a single point of access to powerful distributed resources. Grid middleware support a common set aggregates these resources and provides transparent, remote, and secure access to computing power wherever and whenever it is needed i.e., grid computing aggregates resources and delivers computing power to every user in the network. Users treat the Grid as a single computational resource. Users can submit thousands of jobs at a time without being concerned about where they run. Grid computing is currently used in technical computing environments, to provide more resources for compute-intensive tasks.
A computational grid is a hardware and software infrastructure that provides :-:-;endable, consistent, pervasive and inexpensive access to high-end computational capabilities. Grid computing requires the use of software that can divide and farm out Pieces of a program to as many as several thousand computers. Grid computing can be thought of as distributed and large-scale cluster computing and as a form of network dfetributed processing. In short a grid is a system that co-ordinates resources that are not subject to centralized control using standard, open, general-purpose protocols and Interfaces to deliver nontrivial qualities of service.
ORIGIN OF GRID COMPUTING
In the 1980s, the National Science Foundation created the NSFnet: a communications network intended to give scientific researchers easy access to its new supercomputer centers. Very quickly, one smaller network after another linked in-and the result was the internet. The popularity of the internet as well as the availability of powerful computers and high-speed network technologies as low-cost commodity components is changing the way we use computers today. These technology opportunities have led to the possibility of using distributed computers as a single, unified computing resource, leading to what is popularly known as Grid computing. The term "the Grid" was coined in the mid 1990s to denote a proposed distributed computing infrastructure for advanced science and engineering.
Considerable progress has since been made on the construction of such an infrastructure, but the term "Grid" has also been conflated, at least in popular perception, to embrace everything from advanced networking to artificial intelligence.
DIFFERENT TYPES OF GRID
No two grids are alike. Several distinct types of grids are commonly used in industry today. Three of the most common are compute grids, data grids and access grids:
Compute grid - distributed compute resources consisting of desktop, server, and High Performance Computing (HPC) systems. Computing resources that are managed and collectively made available to meet an organizations computing needs, computational grids enable sharing, selection, and aggregation of geographically distributed resources, such as computers, data sources, and scientific instruments, for solving large-scale problems in science, engineering and commerce.
Access
National/
international
Grids
Access grid - distributed audio-visual equipment, such as cameras, microphones, speakers, and video screens, set up to provide a virtual collective presentation room.
204.121.50 Subnet Network Flowchart
agvid*o2.«U*<il.go»
20«.l 21 .$0.3,0 iixl<s>t»j 2 .» cU*svl.0fe v
J04.W1.S0.22
DiiHtSwtw) PowerPoint 60 IC-jntiofl8 5001 f««»nMufiKi.il UDP S«46*
Ihih TCP 9997 NTPTCPVDP UJ SSH TCP7UDP 22
MUDTCP7777 KTPTCP.UDP 12 J \
RAT. UDP, PORT mutt b#*v«n and :¦ S0000 B»«on Mtrit kdii UOP 56464
Iflrtu^ Vwun HTTP TCP tt> VI*C TCP SS00, S90Q J>
Im on TCP 9997 SSHTCPWW22 X
Bwkoo TCP 9997 NIP TCP.WOP 121 SSHKP/UOP22
JUnfcwt
Data grid - distributed storage devices including disk and tape devices along with the necessary software to migrate data as needed.
LEVELS OF DEPLOYMENT
Grid computing can be divided into three logical levels of deployment: cluster grids, campus grid and global grids as shown in figure.
Cluster Grids are the simplest, consisting of one or more systems interconnected through a network working together to provide a single point of access to users in a single department. Cluster Grids may contain distributed workstations and servers, as well as centralized resources in a datacenter environment. Typically owned and used by a single project or department, Cluster Grids support both high throughput and high performance jobs. Common examples of the Cluster Grid architecture include compute farms, groups of multi-processor HPC systems etc.
Campus Grids: As capacity needs increase, multiple cluster grids can be combined into a campus grid which enables multiple departments within an organization to share computing resources. Organizations can use campus grids to handle a wide variety of tasks. Campus grids typically contain resources from multiple administrative domains, but are located in the same geographic location.
Global grids are a collection of campus grids that cross organizational boundaries to create very large virtual systems. Users have access to compute power that far exceeds the resources available within their own organization. Computing resources may be geographically dispersed, connecting sites around the globe. Designed to support and address the needs of multiple sites and organizations sharing resources, global provide the power of distributed resources to users anywhere in the world.
THE GRID ARCHITECTURE
> Three tier system architecture
> Layered architecture
THE THREE TIER SYSTEM ARCHITECTURE
It is the architecture for a typical cluster grid. Cluster grids employ standard three-tier system architecture, as shown in figure. The architecture includes front-end access nodes, middle-tier management nodes, and back-end compute nodes. Nodes in the access tier are used for submit, control and monitor jobs, compute tier nodes are used to execute jobs and the management tier nodes run the software needed to implement the cluster grid.
Access Tier
The access tier provides access and authentication services to the cluster grid access. Alternatively, web-based services can be provided to permit easy-or tightly-controlled-access to the facility.
Protocols can also be implemented to allow external services like accounting or analysis programs. Any access method should, of course, be able to integrate with common authentication schemes such as NIS, LDAP, and Kerberos. Furthermore, facilities for mapping external user identities to a local identity can be considered.
Management Tier
This middle tier include one or more servers which run the server elements of client-server software such as Distributed Resource Management (DRM), hardware diagnosis software, and system performance monitors. Additional duties of servers in this tier may also include:
> File server - provide NFS service to other nodes in the Cluster grid.
> License key server - manage software license keys for the cluster grid.
> Software provisioning server-manage operating system and application, software versioning and patch application on other nodes in the cluster grid.
The size and number of servers in this tier will vary depending on the type and level of services to be provided. For small implementations with limited functionality, a single services to be chosen to host all management services for ease of administration. Alternatively, these functions may be provided by multiple servers for greater scalability and flexibility.
Compute Tier
This tier supplies the compute power for the cluster grid. Jobs submitted through tier. Nodes in this tier run the client-side of the DRM software, the daemons associated with message-passing environments, and any agents for system health monitoring. The compute tier communicates with the management tier, receiving jobs to run and reporting job completion status and accounting details.
THE LAYERED ARCHITECTURE
Our goal in describing our grid architecture is to identify requirements for general classes of component. The grid architecture identifies fundamental system components, specifies the purpose and function of these components, and indicates how these components interest with one another. In defining a grid architecture, interoperability is the central issue to be addressed. In a networked environment, interoperability means common protocols. A protocol definition specifies how distributed system elements
Dept of C S E -8-
interest with one another in order to achieve a specified behavior, and the structure of the information exchanged during this interaction.
The layered grid architecture is a protocol architecture, with protocols defining the basic mechanisms by which users and resources establish, manage, and exploit sharing relationships. A standards-based open architecture facilities extensibility, interoperability, portability, and code sharing; standard protocols make it easy to define standard services that provide enhanced capabilities. Standard abstractions, APIs, and SDKs can accelerate code development, enable code sharing, and enhance application portability. APIs and SDKs are an adjunct to, not an alternative to, protocols and services, first; and APIs and SDKs, second.
Our architecture organizes components into layers, as shown in Figure. Components within each layer share common characteristics but can build on capabilities and behaviors provided by any lower layer. The architecture consists of resource and connectivity protocols, which facilities the sharing of individual resources.
THE FABRIC LAYER
Protocols defined at the Fabric layer can be used to construct a wide range of global services and application-specific behaviors at the collective layer. The grid fabric layer provides the resources to which shared access is mediated by grid protocols. Fabric components implement the local, resource-specific operations that occur on specific resources as a result of sharing operating at higher levels. There is thus a tight interdependence between the function implemented at the Fabric level and the sharing operating supported.
THE CONNECTIVITY LAYER
The connectivity layer defines core communication and authentication protocols required for grid-specific network transactions. Communication protocols enable the exchange of data between fabric layer resources. Authentication protocols provide cryptographically secure mechanisms for verifying the identity of users and resources. Communication requirements include transport, routing, and naming. With respect to security aspects of the connectivity layer, we observe that the complexity of the security problem makes it important that any solutions be based on existing standards whenever possible. Users must be able to "long on" (authenticate) just once and then have access
to multiple grid resources defined in the fabric layer, without further user intervention. The internet protocols (the internet (IP and ICMP), transport (TCP, UDP), and application (DNS, OSPF, RSVP, etc) are used for communication. Grid security infrastructure (GSI) protocols are used for authentication, communication protection, and authorization. GSI builds on and extends the Transport layer security (TLS) protocols (29) to address most of the issues listed above:
THE RESOURCE LAYER
The resource layer builds on connectivity layer communication and authentication protocols to define protocols for the secure negotiation, initiation, monitoring, control, accounting, and payment of sharing operations on individual resources. Resource layer implementations of these protocols call Fabric layer functions to access and control local resources. Resource layer protocols are concerned entirely with individual resources and hence ignore issues of global state and atomic actions across distributed collections. Two primary classes of resource layer protocols can be distinguished:
Information protocols are used to negotiate access to a shared resource.
Management protocols are used to negotiate access to a shared resource.
These protocols must be chosen so as to capture the fundamental mechanisms of sharing across many different resource types.
> A grid resource information protocol and associated information model.
> An associated soft-state resource registration protocol, the grid resource registration servers.
> The grid resource access and management (GRAM) protocol is used for allocation of computational resources and for monitoring and control of computation on those resources.
> An extended version of the file transfer protocol, grid FTP, is a management protocol for data access.
> LDAP is used as a catalog access protocol.
THE COLLECTIVE LAYER
The collective layer is global in nature and contains protocols and services that are not associated with any one specific resource n capture interactions across collections of resources. The collective components build on the narrow resource and connectivity layer can implement a wide variety of sharing behaviors without placing new requirements on the resources being shared.
Directory services allow participants to discover the existence and/or properties of resources. Resource-level GRRP and GRIP protocols are used to construct directories, Co-allocation, scheduling, and brokering services allow participants to request the allocation of one or more resources for a specific purpose and the scheduling of tasks on the appropriate resources.
Monitoring and diagnostics services support the monitoring of resources for failure, adversarial attack, overload, and so forth.
Data replication services support the management of storage resources to maximize data access performance with respect to metrics such as response time, reliability, and cost.
Grid-enabled programming systems enable familiar programming models to be used in grid environments, using various grid services to address resource discovery, security, resource allocation, and other concerns.
Workload management systems and collaboration frameworks provide for the description, use, and management of multi-step, asynchronous, multi-component workflows.
Software discovery services discover and select the best software implementation and execution platform based on the parameters of the problem being solved. Community authorization servers enforce community policies governing resource access, generating capabilities that community members can use to access community resources.
Community accounting and payment services gather resource usage information for the purpose of accounting, payment, and/or limiting of resource usage by community members,
Collaborating service support the coordinated exchange of information within potentially large user communities, whether synchronously or asynchronously.
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THE APPLICATION LAYER
The application layer comprises the user applications that operate within an organization environment. Applications are constructed in terms of services defined at any layer. At each layer, we have well-defined protocols that provide access to some useful service: resource management, data access, resource discovery, and so forth. At each layer, APIs may also be defined whose implementation (ideally provided by third-party SDKs) exchange protocol messages with the appropriate service(s) to perform desired actions.
The layered grid architecture and its relationship to the internet protocol architecture. Because the internet protocol architecture extends from network to application, there is a mapping from grid layers into internet layers.
GRID & WEB
The grid is a next-generation internet. "The grid" is not an alternative to "the internet": it is rather a set of additional protocols and sen/ices that build on internet protocols and services to support the creation and use of computation or it is a layer of software and services that "sits on top of operating systems and links different systems together, allowing them to share resources - and data-enriched environments. Any resource that is "on the grid" is also, by definition, "on the Net".
The Web is not (yet) a grid: its open, general-purpose protocols support access to distributed resources but not the coordinated use of those resources to deliver interesting qualities of service. Grid computing has been proclaimed as the successor to the Web. Current internet technologies address communication and information exchange among computers but do not provide integrated approaches to the coordinated use of resource at multiple sites for computation. By adding the ability to extensively share computing power, applications and storage to the Web's current ability to share text and multimedia files, problems that require a lot of computing resources can be resolved, devices can work past their own limits and collaboration can become more intense. Let's investigate each of these more closely. The Web has z'ovided a good test bed for grid computing, both through its successes and its Shortcomings. For grid computing to prosper, it will need to solve problems of standards, property rights, access and authorization and modularization and dispatching.
> Standards: there are no clear standards for elements such as security and quality of service.
> Property rights - most software used today is proprietary, and a significant amount of code being written for grid computing is owned by specific businesses. Beyond fair and legal return for the property, proprietary software may stunt the growth and development of grid computing or even determine its future direction.
> Access and authorization - Are all resources available to everyone Most people would not mind if others used spare cycles on their systems in exchange for similar consideration, but content and personal intellectual property (such as term papers and proprietary bidding software) might be more problematic. In addition, security and property rights must be protected.
> Dispatching and modularization - How are resources shared What is the priority Where are assignments dispatched How does one deal with latency These are all nontrivial concerns, and the answers are not clear. A more difficult problem is modularizing code to distribute processing.
The path to the future for grid computing will vary depending upon how these problems are solved. Internet traffic could grow eight times more than forecast over the next decade because of commercial adoption of grid computing.
BENEFITS OF GRID COMPUTING
Grid computing is a model for allowing companies to use a large number of computing resources on demand, no matter where they are located. Grid computing can provide many benefits not available with traditional computing models:
> Better utilization of resources - grid computing uses distributed resources more efficiently and delivers more usable computing power. This can decrease time-to-market, allow for innovation, or enable additional testing and simulation for improved product quality. By employing existing resources, grid computing helps protect IT investments, containing costs while providing more capacity.
> Increased user productivity - by providing transparent access to resources, work can be completed more quickly. Users gain additional productivity as they can focus on design and development rather than wasting valuable time hunting for resources and manually scheduling and managing large numbers of jobs.
> Scalability - Grid can grow seamlessly over time, allowing many thousands of processors to be integrated into one cluster. Components can be updated independently and additional resources can be added as needed, reducing large one-time expenses.
> Flexibility - Grid computing provides computing power where it is needed most, helping to better meet dynamically changing work loads. Grids can contain heterogeneous compute nodes, allowing resources to be added and removed as needs dictate.
> Uniformity - All resources and data should appear to come from the same source even when they don't. Beckhardt refers back to his electrical utility analogy, saying, "When you plug into the wall, you don't know if the power is coming from PG & E or Con Edison."
> Transparency - Researchers should be able to manipulate all of the data available on the grid regardless of the source. In other words, data in different formats and file types should be integrated into a virtual database.
> Reliability - The grid should almost always be available. Think fault tolerance and redundancy. Achieving this requires robust storage, multiple power sources,
and sophisticated networking.
> Pervasiveness - Resources connected to the grid should be available to as many people as possible - a tremendous challenge given the range of platforms and operating systems employed by intended BioGrid users.
> Security - If the databases contain intellectual property, they must be protected. Beckhardt says that a tendency among early grid efforts to neglect data security "has to be resolved before (grid computing) becomes a commercial reality".
APPLICATIONS
The concepts of grid computing started as a project to page link geographically dispersed supercomputers, but now it has grown far beyond its original intent. A grid platform couid be used for many different types of applications. The applications are categorized into four main classes: including collaborative engineering, data exploration, high throughput computing, and distributed supercomputing.
Distributed supercomputing
Distributed supercomputing applications have large computational requirements that can be met only by simultaneous, execution across multiple supercomputers. Distributed computing is a science which solves a large problem by giving small parts of the problem to many computers to solve and then combining the solutions for the parts into a solution for the problem.
Recent distributed computing projects have been designed to use the computers of hundreds of thousands of volunteers all over the world, via the internet. These projects are so large, and require so much computing power to solve, the they would be impossible for any one computer or person to solve in a reasonable amount of time.
Teleimmersive applications
Teleimmersive applications combine simulation, virtual reality, and collaborative environments to provide a shared, virtual design space. Teleimmersive applications can be extremely demanding, requiring large amounts of computation and stringent and diverse network support.
Smart instruments
Computational grids can enhance the power of scientific instruments by providing access to data archives and on-line processing capabilities.
Data intensive applications
Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. This encompasses a number of different finical approaches, such as clustering, data summarizations, learning classification t5 finding dependency net works, analyzing changes, and detecting anomalies. Data analysis tends to work from the data up and the best techniques are those
developed with an orientation towards large volumes of data, making use of as much of the collected data as possible to arrive at reliable conclusions and decisions. The analysis process starts with a set of data, uses a methodology to develop an optimal representation of the structure of the data during which time knowledge is acquired. One knowledge has been acquired this can be extended to larger sets of data working on the assumption that the larger data set has a structure similar to the sample data.
Collaborative design
When collaboration is conducted in a distributed and indirect way, information such as documented results of design decisions and ideas can be shared easily among participants of a working team.
In design behavior, conflicts and argument are regular. Hence designers need to dynamically exchange their opinions both formally and informally using multi media integrated into the system. Collaborative software can help to manage and record information of every process of the product design. Computer based design environment integrates the various knowledge and experience together. Every team member is able to contribute ideas, and the team can jointly explore design concepts early in the project.
CONCLUSION
Applying the resources of many computers in a network to a single problem at the same time - usually a scientific or technical problem that requires a great number of computer processing cycles or access to large amounts of data. Grid computing uses software to divide and farm out pieces of a program to as many as several thousand computers. A number of corporations, professional groups and university consortia have developed frameworks and software for managing grid computing projects. Grid computing provides clustering of remotely distributed computing. The principal focus of grid computing to date has been on maximizing the use of available processor resources for compute-intensive applications. Grid computing along with storage virtualization and ser CONTENTS
1. Introduction '
2. What is grid computing 2
3. Origin of grid computing 3
4. Different type of grid 4
5. Levels of developments 6
6. The Grid Architecture 7
7. The Three Tire System Architecture 7
8. The Layered Architecture 8
9. Grid And Web 12
10. Benefits of Grid Computing 14
I I. Applications *»
12. Conclusion '°
er virtua
