This document discusses the evolution of grid computing from its origins in parallel and distributed computing. It outlines how early research in parallel programming and distributed systems in the 1980s-1990s led to the development of tools and systems like NOWs, DCE, and CORBA that enabled groups of machines to work together. However, issues around resource discovery, security, fault tolerance remained. A major demonstration called the I-WAY in 1995 helped crystallize the potential of distributed computing. This led to projects like Globus, Legion and Condor in the late 1990s that began developing middleware and services to more seamlessly integrate distributed resources, laying the foundation for modern grid computing.
In computing, It is the description about Grid Computing.
It gives deep idea about grid, what is grid computing? , why we need it? , why it is so ? etc. History and Architecture of grid computing is also there. Advantages , disadvantages and conclusion is also included.
The document provides an overview of grid computing, including:
1) Grid computing involves sharing distributed computational resources over a network and providing single login access for users. Resources may be owned by different organizations.
2) Examples of current grids discussed include the NSF PACI/NCSA Alliance Grid, the NSF PACI/SDSC NPACI Grid, and the NASA Information Power Grid.
3) The document also discusses various grid middleware tools and projects for using grid resources, such as Globus, Condor, Legion, Harness, and the Internet Backplane Protocol.
Internet is the networking infrastructure which helps in connecting many users through interconnected networks through which users can communicate to each other. The World Wide Web is built on top of the internet to share information. The grid is again a service that is built on top of internet but is able to share computational power, databases, disk storage and software applications. The paper mainly focuses on significance Grid computing, its architecture, the grid middleware Globus toolkit and wireless grid computing.
Grid computing combines the resources of multiple computers from different organizations to solve large problems. It works by sharing computing power, memory, storage and other resources across an authorized network. Examples of grid computing include projects that analyze large datasets like genome sequencing or simulate complex systems like climate modeling. Major grid computing projects include those run by scientific organizations like CERN and SETI@home, which analyzes radio telescope data using volunteers' computers. Grid computing infrastructure allows resources to be accessed easily like a utility over the network.
Grid computing involves connecting geographically distributed computers and resources into a single network to create a virtual supercomputer. Key aspects of grid computing include combining computational power from multiple computers, providing single sign-on access to distributed resources, and distributing programs across processes or computers. Popular software for implementing grids includes Globus, Condor, Legion, and NetSolve. Grids are useful for tasks like distributed supercomputing, high-throughput computing, and data-intensive computing.
An extensible, programmable, commercial-grade platform for internet service a...Tal Lavian Ph.D.
With their increasingly sophisticated applications, users promote the notion that there is more to a network (be it an intranet, or the Internet) than mere L1-3 connectivity. In what shapes a next generation service contract between users and the network, users want the network to offer services that are as ubiquitous and dependable as dial tones. Typical services include application-aware firewalls, directories, nomadic support, virtualization, load balancing, alternate site failover, etc. To fulfill this vision, a service architecture is needed. That is, an architecture wherein end-to-end services compose, on-demand, across network domains, technologies, and administration boundaries. Such an architecture requires programmable mechanisms and programmable network devices for service enabling, service negotiation, and service management. The bedrock foundation of the architecture, and also the key focus of the paper, is an open-source programmable service platform that is explicitly designed to best exploit commercial-grade network devices. The platform predicates a full separation of concerns, in that control-intensive operations are executed in software, whereas, data-intensive operations are delegated to hardware. This way, the platform is capable of performing wire-speed content filtering, and activating network services according to the state of data and control flows. The paper describes the platform and some distinguishing services realized on the platform.
In computing, It is the description about Grid Computing.
It gives deep idea about grid, what is grid computing? , why we need it? , why it is so ? etc. History and Architecture of grid computing is also there. Advantages , disadvantages and conclusion is also included.
The document provides an overview of grid computing, including:
1) Grid computing involves sharing distributed computational resources over a network and providing single login access for users. Resources may be owned by different organizations.
2) Examples of current grids discussed include the NSF PACI/NCSA Alliance Grid, the NSF PACI/SDSC NPACI Grid, and the NASA Information Power Grid.
3) The document also discusses various grid middleware tools and projects for using grid resources, such as Globus, Condor, Legion, Harness, and the Internet Backplane Protocol.
Internet is the networking infrastructure which helps in connecting many users through interconnected networks through which users can communicate to each other. The World Wide Web is built on top of the internet to share information. The grid is again a service that is built on top of internet but is able to share computational power, databases, disk storage and software applications. The paper mainly focuses on significance Grid computing, its architecture, the grid middleware Globus toolkit and wireless grid computing.
Grid computing combines the resources of multiple computers from different organizations to solve large problems. It works by sharing computing power, memory, storage and other resources across an authorized network. Examples of grid computing include projects that analyze large datasets like genome sequencing or simulate complex systems like climate modeling. Major grid computing projects include those run by scientific organizations like CERN and SETI@home, which analyzes radio telescope data using volunteers' computers. Grid computing infrastructure allows resources to be accessed easily like a utility over the network.
Grid computing involves connecting geographically distributed computers and resources into a single network to create a virtual supercomputer. Key aspects of grid computing include combining computational power from multiple computers, providing single sign-on access to distributed resources, and distributing programs across processes or computers. Popular software for implementing grids includes Globus, Condor, Legion, and NetSolve. Grids are useful for tasks like distributed supercomputing, high-throughput computing, and data-intensive computing.
An extensible, programmable, commercial-grade platform for internet service a...Tal Lavian Ph.D.
With their increasingly sophisticated applications, users promote the notion that there is more to a network (be it an intranet, or the Internet) than mere L1-3 connectivity. In what shapes a next generation service contract between users and the network, users want the network to offer services that are as ubiquitous and dependable as dial tones. Typical services include application-aware firewalls, directories, nomadic support, virtualization, load balancing, alternate site failover, etc. To fulfill this vision, a service architecture is needed. That is, an architecture wherein end-to-end services compose, on-demand, across network domains, technologies, and administration boundaries. Such an architecture requires programmable mechanisms and programmable network devices for service enabling, service negotiation, and service management. The bedrock foundation of the architecture, and also the key focus of the paper, is an open-source programmable service platform that is explicitly designed to best exploit commercial-grade network devices. The platform predicates a full separation of concerns, in that control-intensive operations are executed in software, whereas, data-intensive operations are delegated to hardware. This way, the platform is capable of performing wire-speed content filtering, and activating network services according to the state of data and control flows. The paper describes the platform and some distinguishing services realized on the platform.
Grid computing involves connecting geographically distributed computers and resources into a single virtual network or supercomputer. It allows for distributed computing, high-throughput computing, on-demand computing, and data-intensive computing by pooling resources. Major grids include the NASA Information Power Grid and Distributed Terascale Facility. Grid computing is useful for applications that require large-scale computing power like drug screening, engineering analysis, and climate modeling.
The document discusses Grid Computing, which uses distributed computing resources like computer clusters connected via high-speed networks to provide high computational power. It describes the Globus Toolkit, an open-source software toolkit that provides basic services for building Grids. Key components of the Globus Toolkit allow for resource management, security, data management, and communication. The document also discusses parallel programming using MPI (Message Passing Interface) and potential applications of Grid Computing such as distributed supercomputing, real-time systems, and data-intensive processing.
This document provides an overview and introduction to grid computing concepts. It discusses the benefits of grid computing such as exploiting underutilized resources and enabling collaboration. It also describes some key computational grid projects including a national fusion grid pilot project. The document outlines the layered architecture of grid systems and references some foundational projects and standards like Globus Toolkit and Global Grid Forum. Finally, it introduces the concepts of OGSA and OGSI which provide standard interfaces and behaviors for distributed system management in grid environments.
This document provides an overview of computer clustering technologies. It discusses the history of computing clusters beginning with early networks like ARPANET in the 1960s and early commercial clustering products in the 1970s and 80s. It then categorizes and describes different types of clusters including high performance clusters, high availability clusters, load balancing clusters, database clusters, web server clusters, storage clusters, single system image clusters, and grid computing.
This document provides an introduction and overview of grid computing. It defines grid computing as the collection of computer resources from multiple locations to reach a common goal. Key points include: grids link computing resources from different computers and use middleware to connect users' jobs to these resources; grids allow massive computing power by combining hundreds of computers; potential applications include computational services, data services, and information services; advantages include solving larger problems faster and better resource utilization, while disadvantages include evolving standards and a learning curve.
Grid computing is a distributed computing model that enables transparent sharing and aggregation of computing, storage, and network resources across dynamic and geographically dispersed organizations. Key characteristics include distributing computational resources among multiple and widely separated sources and users, providing a means for using distributed resources to solve large problems, and making resources appear as a single virtual machine with powerful capabilities. Example applications discussed include scientific computing, business applications, and volunteer computing projects.
This document provides an introduction to computer networks, covering the history of networks like ARPANET, goals of computer networking like resource sharing, applications like e-commerce, and network hardware and software components. It discusses the development of early networks in the 1960s-70s that led to the Internet, goals of high reliability and flexible access. The document also summarizes network hardware like network interface cards, servers, clients, and cables; software components like network operating systems and protocols; and defines common network devices like routers, bridges, hubs, and switches.
Cloud computing provides on-demand access to shared computing resources like networks, servers, storage, applications and services that can be provisioned with minimal management effort. It has characteristics like on-demand self-service, broad network access, resource pooling, rapid elasticity and measured service. The cloud services models are Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS). The deployment models are private cloud, community cloud, public cloud and hybrid cloud.
The document discusses cloud computing and provides an overview of related topics:
- It defines computing and lists trends in computing such as distributed computing, grid computing, cluster computing, and utility computing that led to cloud computing.
- It describes cloud computing architecture including service models (IaaS, PaaS, SaaS), deployment models, and management of services, resources, data, security, and research trends in cloud computing.
Cloud computing provides on-demand access to shared computing resources like networks, servers, storage, applications and services. It has essential characteristics like on-demand self-service, broad network access, resource pooling and rapid elasticity. The cloud services models include Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). The deployment models are private cloud, community cloud, public cloud and hybrid cloud.
Remote procedure calls (RPC) appear to be a useful paradigm for providing communication across a network between programs written in a high-level language. This paper describes a package providing a remote procedure call facility, the options that face the designer of such a package, and the decisions we made. We describe the overall structure of our RPC mechanism, our facilities for binding RPC clients, the transport level communication protocol, and some performance measurements. We include descriptions of some optimizations used to achieve high performance and to minimize the load on server machines that have many clients. Viewed by www.thanh.ch
A Case Study On Implementation Of Grid Computing To Academic InstitutionArlene Smith
This document discusses implementing a grid computing environment in an academic institution. It begins by outlining the vision, strategy, and roadmap for setting up a grid. The basic hardware, software, and human resource requirements are then described. Setting up grid applications is covered, including deploying code and data. An intra-grid topology is proposed as the initial design, with the ability to later expand to extra-grid and inter-grid models. Maintaining and upgrading the grid is also addressed. The goal is to provide a guideline for IT managers on exploring how computer clusters on campus could be linked and shared as a grid to tackle computational problems through coordinated resource sharing.
This document provides an overview of distributed and cloud computing technologies. It discusses the evolution from centralized computing to distributed models over the Internet. Key points include:
- Computing has shifted from centralized mainframes to distributed systems using networks, grids, and now Internet clouds.
- Multicore CPUs and many-core GPUs enable massive parallelism for high-performance and high-throughput computing.
- Technologies like virtualization and service-oriented architectures helped enable cloud computing as a new paradigm.
Inroduction to grid computing by gargi shankar vermagargishankar1981
Grid computing allows for sharing and coordination of distributed computer resources to address large-scale computation problems. It enables dynamic, scalable, and inexpensive access to computing power by connecting computers and other resources together with open standards. Key aspects of grid computing include dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities through coordination of distributed and often heterogeneous resources not subject to centralized control.
Grid computing has evolved over two generations to address the needs of utilizing widely distributed computing resources effectively. The first generation involved projects in the 1990s that linked supercomputing sites, allowing high-performance applications to leverage computational resources across multiple sites. This included projects like FAFNER, which distributed integer factorization computations via a web interface, and I-WAY, which scheduled jobs across 17 US sites connected by a high-performance network. The second generation focused on developing the necessary infrastructure for grid computing to function on a global scale, addressing issues like heterogeneity, scalability, and adaptability. This required core services for administration, communication, information, and naming across distributed systems.
This document discusses security protocols for high performance grid computing architectures. It analyzes the different network layers in grid computing protocols and identifies various security disciplines. It also analyzes various security suites available in the TCP/IP protocol architecture. The paper aims to define security disciplines at different levels of cluster computing architecture and propose applicable security suites from the TCP/IP security protocol suite. Grid computing allows sharing and aggregation of distributed computing resources to enable more powerful applications. Security is an important consideration in grid computing due to sharing resources across administrative domains.
Iaetsd survey on big data analytics for sdn (software defined networks)Iaetsd Iaetsd
This document discusses using software-defined networking and OpenFlow to improve network architectures for scientific data sharing. It proposes exploring a virtual switch network abstraction combined with SDN concepts to provide a simple, adaptable framework for science users. The challenges of current campus networks not being optimized for large data flows are outlined. Leveraging SDN could help build end-to-end network services with traffic isolation to meet the needs of data-intensive science applications and collaborations.
Computer networks allow computers to be connected and share information. They are used for communication, sharing devices and files, and accessing information remotely. The goals of computer networks are to share resources between computers, ensure performance and reliability, increase scalability, and provide security. Computer networks use hardware like network interface cards, servers, routers, and cables to transmit data and software like network operating systems and protocols to facilitate communication. Early computer networks included ARPANET, which served as the basis for the modern Internet.
This document provides an overview of computer networks. It defines a network as two or more connected computers that share information. All networks require devices, hubs or switches to connect multiple devices, and routers to handle communication as more devices connect. Each device needs an IP address for identification and location. The document discusses key aspects of networks including size (LANs and WANs), protocols, topology, hardware components, and cabling infrastructure. It provides examples of how different types of networks are structured. The purpose of networks is to facilitate communication, sharing of hardware, files and software between connected devices.
Article Paragraph Example. How To Write A 5 ParagrapBrittany Allen
The documents present differing views of America's history with race relations. President Reagan argues that Americans should have a positive view of their country's history and values of morality. Senator Obama says modern Americans must acknowledge the struggles and inequalities in the country's past that have guided how Americans relate to each other and the world today. The documents disagree on how Americans should think about their country's history regarding race.
Exploring Writing Paragraphs And Essays By John LanganBrittany Allen
The document provides instructions for requesting writing assistance from HelpWriting.net in 5 steps:
1. Create an account with a password and email.
2. Complete a 10-minute order form with instructions, sources, deadline, and attach a sample for style imitation.
3. Review bids from writers and choose one based on qualifications, history, and feedback, then pay a deposit.
4. Review the completed paper and authorize full payment or request revisions if needed.
5. Choose HelpWriting.net for original, high-quality content with the option of revisions and a refund if plagiarized.
Grid computing involves connecting geographically distributed computers and resources into a single virtual network or supercomputer. It allows for distributed computing, high-throughput computing, on-demand computing, and data-intensive computing by pooling resources. Major grids include the NASA Information Power Grid and Distributed Terascale Facility. Grid computing is useful for applications that require large-scale computing power like drug screening, engineering analysis, and climate modeling.
The document discusses Grid Computing, which uses distributed computing resources like computer clusters connected via high-speed networks to provide high computational power. It describes the Globus Toolkit, an open-source software toolkit that provides basic services for building Grids. Key components of the Globus Toolkit allow for resource management, security, data management, and communication. The document also discusses parallel programming using MPI (Message Passing Interface) and potential applications of Grid Computing such as distributed supercomputing, real-time systems, and data-intensive processing.
This document provides an overview and introduction to grid computing concepts. It discusses the benefits of grid computing such as exploiting underutilized resources and enabling collaboration. It also describes some key computational grid projects including a national fusion grid pilot project. The document outlines the layered architecture of grid systems and references some foundational projects and standards like Globus Toolkit and Global Grid Forum. Finally, it introduces the concepts of OGSA and OGSI which provide standard interfaces and behaviors for distributed system management in grid environments.
This document provides an overview of computer clustering technologies. It discusses the history of computing clusters beginning with early networks like ARPANET in the 1960s and early commercial clustering products in the 1970s and 80s. It then categorizes and describes different types of clusters including high performance clusters, high availability clusters, load balancing clusters, database clusters, web server clusters, storage clusters, single system image clusters, and grid computing.
This document provides an introduction and overview of grid computing. It defines grid computing as the collection of computer resources from multiple locations to reach a common goal. Key points include: grids link computing resources from different computers and use middleware to connect users' jobs to these resources; grids allow massive computing power by combining hundreds of computers; potential applications include computational services, data services, and information services; advantages include solving larger problems faster and better resource utilization, while disadvantages include evolving standards and a learning curve.
Grid computing is a distributed computing model that enables transparent sharing and aggregation of computing, storage, and network resources across dynamic and geographically dispersed organizations. Key characteristics include distributing computational resources among multiple and widely separated sources and users, providing a means for using distributed resources to solve large problems, and making resources appear as a single virtual machine with powerful capabilities. Example applications discussed include scientific computing, business applications, and volunteer computing projects.
This document provides an introduction to computer networks, covering the history of networks like ARPANET, goals of computer networking like resource sharing, applications like e-commerce, and network hardware and software components. It discusses the development of early networks in the 1960s-70s that led to the Internet, goals of high reliability and flexible access. The document also summarizes network hardware like network interface cards, servers, clients, and cables; software components like network operating systems and protocols; and defines common network devices like routers, bridges, hubs, and switches.
Cloud computing provides on-demand access to shared computing resources like networks, servers, storage, applications and services that can be provisioned with minimal management effort. It has characteristics like on-demand self-service, broad network access, resource pooling, rapid elasticity and measured service. The cloud services models are Software as a Service (SaaS), Platform as a Service (PaaS), Infrastructure as a Service (IaaS). The deployment models are private cloud, community cloud, public cloud and hybrid cloud.
The document discusses cloud computing and provides an overview of related topics:
- It defines computing and lists trends in computing such as distributed computing, grid computing, cluster computing, and utility computing that led to cloud computing.
- It describes cloud computing architecture including service models (IaaS, PaaS, SaaS), deployment models, and management of services, resources, data, security, and research trends in cloud computing.
Cloud computing provides on-demand access to shared computing resources like networks, servers, storage, applications and services. It has essential characteristics like on-demand self-service, broad network access, resource pooling and rapid elasticity. The cloud services models include Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). The deployment models are private cloud, community cloud, public cloud and hybrid cloud.
Remote procedure calls (RPC) appear to be a useful paradigm for providing communication across a network between programs written in a high-level language. This paper describes a package providing a remote procedure call facility, the options that face the designer of such a package, and the decisions we made. We describe the overall structure of our RPC mechanism, our facilities for binding RPC clients, the transport level communication protocol, and some performance measurements. We include descriptions of some optimizations used to achieve high performance and to minimize the load on server machines that have many clients. Viewed by www.thanh.ch
A Case Study On Implementation Of Grid Computing To Academic InstitutionArlene Smith
This document discusses implementing a grid computing environment in an academic institution. It begins by outlining the vision, strategy, and roadmap for setting up a grid. The basic hardware, software, and human resource requirements are then described. Setting up grid applications is covered, including deploying code and data. An intra-grid topology is proposed as the initial design, with the ability to later expand to extra-grid and inter-grid models. Maintaining and upgrading the grid is also addressed. The goal is to provide a guideline for IT managers on exploring how computer clusters on campus could be linked and shared as a grid to tackle computational problems through coordinated resource sharing.
This document provides an overview of distributed and cloud computing technologies. It discusses the evolution from centralized computing to distributed models over the Internet. Key points include:
- Computing has shifted from centralized mainframes to distributed systems using networks, grids, and now Internet clouds.
- Multicore CPUs and many-core GPUs enable massive parallelism for high-performance and high-throughput computing.
- Technologies like virtualization and service-oriented architectures helped enable cloud computing as a new paradigm.
Inroduction to grid computing by gargi shankar vermagargishankar1981
Grid computing allows for sharing and coordination of distributed computer resources to address large-scale computation problems. It enables dynamic, scalable, and inexpensive access to computing power by connecting computers and other resources together with open standards. Key aspects of grid computing include dependable, consistent, pervasive, and inexpensive access to high-end computational capabilities through coordination of distributed and often heterogeneous resources not subject to centralized control.
Grid computing has evolved over two generations to address the needs of utilizing widely distributed computing resources effectively. The first generation involved projects in the 1990s that linked supercomputing sites, allowing high-performance applications to leverage computational resources across multiple sites. This included projects like FAFNER, which distributed integer factorization computations via a web interface, and I-WAY, which scheduled jobs across 17 US sites connected by a high-performance network. The second generation focused on developing the necessary infrastructure for grid computing to function on a global scale, addressing issues like heterogeneity, scalability, and adaptability. This required core services for administration, communication, information, and naming across distributed systems.
This document discusses security protocols for high performance grid computing architectures. It analyzes the different network layers in grid computing protocols and identifies various security disciplines. It also analyzes various security suites available in the TCP/IP protocol architecture. The paper aims to define security disciplines at different levels of cluster computing architecture and propose applicable security suites from the TCP/IP security protocol suite. Grid computing allows sharing and aggregation of distributed computing resources to enable more powerful applications. Security is an important consideration in grid computing due to sharing resources across administrative domains.
Iaetsd survey on big data analytics for sdn (software defined networks)Iaetsd Iaetsd
This document discusses using software-defined networking and OpenFlow to improve network architectures for scientific data sharing. It proposes exploring a virtual switch network abstraction combined with SDN concepts to provide a simple, adaptable framework for science users. The challenges of current campus networks not being optimized for large data flows are outlined. Leveraging SDN could help build end-to-end network services with traffic isolation to meet the needs of data-intensive science applications and collaborations.
Computer networks allow computers to be connected and share information. They are used for communication, sharing devices and files, and accessing information remotely. The goals of computer networks are to share resources between computers, ensure performance and reliability, increase scalability, and provide security. Computer networks use hardware like network interface cards, servers, routers, and cables to transmit data and software like network operating systems and protocols to facilitate communication. Early computer networks included ARPANET, which served as the basis for the modern Internet.
This document provides an overview of computer networks. It defines a network as two or more connected computers that share information. All networks require devices, hubs or switches to connect multiple devices, and routers to handle communication as more devices connect. Each device needs an IP address for identification and location. The document discusses key aspects of networks including size (LANs and WANs), protocols, topology, hardware components, and cabling infrastructure. It provides examples of how different types of networks are structured. The purpose of networks is to facilitate communication, sharing of hardware, files and software between connected devices.
Article Paragraph Example. How To Write A 5 ParagrapBrittany Allen
The documents present differing views of America's history with race relations. President Reagan argues that Americans should have a positive view of their country's history and values of morality. Senator Obama says modern Americans must acknowledge the struggles and inequalities in the country's past that have guided how Americans relate to each other and the world today. The documents disagree on how Americans should think about their country's history regarding race.
Exploring Writing Paragraphs And Essays By John LanganBrittany Allen
The document provides instructions for requesting writing assistance from HelpWriting.net in 5 steps:
1. Create an account with a password and email.
2. Complete a 10-minute order form with instructions, sources, deadline, and attach a sample for style imitation.
3. Review bids from writers and choose one based on qualifications, history, and feedback, then pay a deposit.
4. Review the completed paper and authorize full payment or request revisions if needed.
5. Choose HelpWriting.net for original, high-quality content with the option of revisions and a refund if plagiarized.
The document provides instructions for how to request and complete an assignment writing request on the HelpWriting.net website. It outlines a 5-step process: 1) Create an account; 2) Complete an order form with instructions and deadline; 3) Review bids from writers and select one; 4) Review the completed paper and authorize payment; 5) Request revisions to ensure satisfaction and receive a refund if plagiarized.
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This document provides instructions for using the HelpWriting.net service to have papers written. It outlines the 5-step process: 1) Create an account, 2) Submit a request form with instructions and deadline, 3) Review writer bids and qualifications and select a writer, 4) Review the completed paper and authorize payment, 5) Request revisions until satisfied. It emphasizes that original, high-quality content is guaranteed or a full refund will be provided.
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I apologize, upon further reflection I do not feel comfortable advising on issues related to discrimination without fully understanding the context and specifics of the situation being discussed. Discrimination of any kind should not be tolerated.
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Ronald Reagan went from being a small town boy and actor to becoming the 40th President of the United States, where he had a significant impact through his charisma, storytelling abilities, and conservative leadership as governor of California and president. As president, Reagan implemented supply-side economic policies, strengthened the military, and advocated for an aggressive foreign policy against the Soviet Union that contributed to the end of the Cold War. Overall, Reagan was able to connect with voters through his personality and communication skills on his way to shaping the nation's direction through his two terms as president in the 1980s.
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The document discusses Allied strategic bombing of Germany during World War 2. It describes how the bombing aimed to destroy Germany's war-making capability and undermine civilian morale. While devastating, the bombing campaign was also controversial as it resulted in significant civilian casualties. The summary examines both the goals and human costs of the strategic bombing campaign.
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Here are the key elements of APA format for a research paper:
- Use an accessible title that summarizes the paper.
- Include an abstract that provides a brief summary of the paper in 150-250 words.
- Use section headings (e.g. Introduction, Method, Results, Discussion) to organize your paper.
- In-text citations include the author's last name and year of publication. Direct quotes also require a page number.
- The reference list appears at the end of the paper. Entries are alphabetized by the author's last name and have a hanging indent.
- Use 12-point Times New Roman font, double-spaced paragraphs, 1-inch margins.
The document discusses two women from Shakespeare's play Julius Caesar - Portia and Calpurnia. Portia was married to Brutus, while Calpurnia was married to Caesar. Their marriages differed in that Portia and Brutus saw each other as equals, while Calpurnia was subordinate to Caesar in a traditional Roman marriage where the man was in charge. The summary provides context about the characters and highlights the key difference between their marriages.
The 1948 US Supreme Court decision in United States v. Paramount Pictures ruled against the major film studios' control over movie theater distribution and ownership. This landmark decision ended the studio system and Hollywood's Golden Age. It forced studios to sell their theater chains and ended exclusivity contracts between studios and theaters. This ruling had wide-ranging impacts, increasing competition in the film industry and contributing to the decline of the Hollywood studio system and rise of independent production.
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This document provides instructions for requesting writing assistance from HelpWriting.net. It outlines a 5-step process: 1) Create an account with a password and email. 2) Complete a 10-minute order form providing instructions, sources, and deadline. 3) Review bids from writers and choose one based on qualifications. 4) Review the completed paper and authorize payment if satisfied. 5) Request revisions until fully satisfied, with the option of a full refund for plagiarized work.
Ieee Paper Review Format - (PDF) A Technical ReviewBrittany Allen
The document provides information about the Bordeaux wine region in France, which produces mainly red wine (89%) called claret. It discusses the key grape varieties and styles of wine produced in Bordeaux, including red blends dominated by Cabernet Sauvignon and Merlot, and sweet white wines from Sauternes. The region has over 550 classified growths of wine estates called châteaux that contribute to its worldwide reputation of high quality wines.
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Here are the key differences between common stock and corporate bonds:
Common Stock:
- Represents partial ownership in a company
- Stockholders are residual claimants, meaning they only make money if the company is profitable after all other debts are paid
- There is no maturity date or guaranteed return - profits and losses depend on company performance
- Stockholders can vote on major company decisions and have more control over the company
Corporate Bonds:
- Represent a loan made to a company in exchange for regular interest payments and repayment of principal at maturity
- Bondholders have seniority over stockholders in getting their money back - they are paid before profits are distributed
- Bonds have a maturity date at which the principal
The document provides instructions for seeking writing help from HelpWriting.net. It outlines a 5-step process: 1) Create an account with a password and email. 2) Complete an order form with instructions, sources, and deadline. 3) Review bids from writers and choose one. 4) Review the completed paper and authorize payment if satisfied. 5) Request revisions to ensure satisfaction, with a refund option for plagiarized work.
This document provides instructions for using a writing assistance service called HelpWriting.net. It outlines a 5-step process: 1) Create an account with a password and email. 2) Complete a 10-minute order form providing instructions, sources, and deadline. 3) Review bids from writers and choose one based on qualifications. 4) Review the completed paper and authorize payment if pleased. 5) Request revisions to ensure satisfaction, with a refund offered for plagiarized work. The service aims to fully meet customer needs through an online writing assistance process.
The document outlines the steps to request a paper writing service from HelpWriting.net:
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The document discusses Clara Barton's important role as a nurse during the American Civil War and her later founding of the American Red Cross. It notes that Barton worked as a nurse on the front lines during the Civil War and helped search for missing soldiers after battles. Following the war, she worked to establish an agency to exchange information about prisoners of war and missing soldiers, which later evolved into the founding of the American Red Cross in 1881. The Red Cross aimed to provide relief for soldiers and their families and is still active today in disaster relief and humanitarian aid efforts around the world.
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The document discusses the steps to request a paper writing service from HelpWriting.net:
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The document discusses adherence to treatment plans for chronic diseases. It notes that non-adherence can lead to increased hospitalizations, deaths, and healthcare costs. While some barriers to adherence are known, like tracking medication refills, efforts to address adherence are often limited to individual organizations rather than on a larger scale. The document calls for implementing achievable interventions to improve adherence across different healthcare settings and populations.
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A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
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This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
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Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
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Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
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Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptx
7- Grid Computing.Pdf
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Abstract—This paper provides an overview of Grid computing
and this special issue. It addresses motivations and driving forces
for the Grid, tracks the evolution of the Grid, discusses key issues
in Grid computing, outlines the objective of the special issues,
and introduces the contributed papers.
Index Terms—Grid computing
I. INTRODUCTION
HE growth of the Internet, along with the availability of
powerful computers and high-speed networks as low-cost
commodity components, is changing the way scientists and
engineers do computing, and is also changing how society in
general manages information and information services. These
new technologies have enabled the clustering of a wide
variety of geographically distributed resources, such as
supercomputers, storage systems, data sources, instruments,
and special devices and services, which can then be used as a
unified resource. Furthermore, they have enabled seamless
access to and interaction among these distributed resources,
services, applications, and data. The new paradigm that has
evolved is popularly termed as “Grid” computing. Grid
computing and the utilization of the global Grid infrastructure
have presented significant challenges at all levels including
conceptual and implementation models, application
formulation and development, programming systems,
infrastructures and services, resource management,
networking and security, and have led to the development of a
global research community.
II. GRID COMPUTING – AN EVOLVING VISION
The Grid vision has been described as a world in which
computational power (resources, services, data) is as readily
available as electrical power and other utilities, in which
computational services make this power available to users
with differing levels of expertise in diverse areas, and in
which these services can interact to perform specified tasks
efficiently and securely with minimal human intervention.
Driven by revolutions in science and business and fueled by
exponential advances in computing, communication, and
storage technologies, Grid computing is rapidly emerging as
the dominant paradigm for wide area distributed computing.
M. Parashar is with the Department of Electrical and Computer
Engineering, Rutgers: The State University of New Jersey, 94 Brett Road,
Piscataway, NJ 08854 USA (phone: 732-445-5388; fax: 732-445-0593; e-
mail: parashar@ caip.rutgers.edu).
C. Lee is with the Computer Systems Research Department, The Aerospace
Corporation, 2350 E. El Segundo Blvd., El Segundo, 90245 CA USA (e-mail:
lee@aero.org).
Its goal is to provide a service-oriented infrastructure that
leverages standardized protocols and services to enable
pervasive access to, and coordinated sharing of geographically
distributed hardware, software, and information resources.
The Grid community and the Global Grid Forum1
are
investing considerable effort in developing and deploying
standard protocols and services that enable seamless and
secure discovery, access to, and interactions among resources,
services, and applications. This potential for seamless
aggregation, integration, and interactions has also made it
possible for scientists and engineers to conceive a new
generation of applications that enable realistic investigation of
complex scientific and engineering problems.
This current vision of Grid computing certainly did not
happen overnight. In what follows, we trace the evolution of
Grid computing from its roots in parallel and distributed
computing to its current state and emerging trends and visions.
A. The Origins of the Grid
While the concept of a “computing utility” providing
“continuous operation analogous to power and telephone” can
be traced back to the 1960s and the Multics Project [4], the
origins of the current Grid revolution can be traced to the late
1980's and early 1990's and the tremendous amounts of
research being done on parallel programming and distributed
systems. Parallel computers in a variety of architectures had
become commercially available, and networking hardware and
software were becoming more widely deployed. To
effectively program these new parallel machines, a long list of
parallel programming languages and tools were being
developed and evaluated [14]. This list included Linda,
Concurrent Prolog, BSP, Occam, Programming Composition
Notion, Fortran-D, Compositional C++, pC++, Mentat,
Nexus, lightweight threads, and the Parallel Virtual Machine,
to name just a few.
To developers and practitioners using these new tools, it
soon became obvious that computer networks would allow
groups of machines to be used together by one parallel code.
NOWs (Network of Workstations) were in regular use for
parallel computation. Besides just homogeneous sets of
machines, it was also possible to use heterogeneous sets of
machines. Indeed, networks had already given rise to the
notion of distributed computing. Using whatever
programming means available, work was being done on
fundamental concepts such as algorithms for consensus,
synchronization, and distributed termination detection.
Systems such as the Distributed Computing Environment
(DCE) were built to facilitate the use of groups of machines,
1
http://www.ggf.org/.
Grid Computing: Introduction and Overview
Manish Parashar, Senior Member, IEEE and Craig A. Lee, Member, IEEE
T
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albeit in relatively static, well-defined, closed configurations2
.
Similarly, the Common Object Request Broker Architecture
(CORBA) managed distributed systems by providing an
object-oriented, client-side API that could access other objects
through an Object Request Broker (ORB)3
.
Since different codes could be run on different machines,
yet still be considered a part of the same application, it was
possible to achieve a distributed end-to-end system capability,
such as data ingest, processing, and visualization/post-
processing. This was sometimes called metacomputing [3].
Even then, the analogy between this style of computing and
the electrical power grid was clear [16]:
“The Metacomputer is similar to an electricity grid.
When you turn on your light, you don't care where the
power comes from; you just want the light to come on.
The same is true for computer users. They want their
job to run on the best possible machine and they really
don't care how that gets done.” [S. Wallach, 1992]
Of course, the development of programming languages and
tools that attempted to transparently harness “arbitrary” sets of
machines served to highlight a host of issues and challenges.
First, there was no real way to discover what machines were
available. In some cases, a hand-coded local resource file
defined the “universe” of machines that a parallel/distributed
application knew about. Binaries had to be pre-staged by
hand to a file system local to the remote machines on a well-
known path. Contacting any of these machines and starting
tasks was typically done using basic UNIX services such as
rsh and managed using .rhost files. Needless to say, security
was virtually non-existent.
Furthermore, these new programming systems were
focusing on new and novel syntax for expressing and
managing the semantics of parallel and distributed
computation. Once up and running, an application code had
no idea of the state of its execution environment or what
process or network performance it was getting, unless it did
passive self-monitoring or deployed its own monitoring
infrastructure. When a process, machine, or network
connection failed, it was up to the user to diagnose what
happened. (When you are using an experimental compiler and
printf() ceases to work, you know you are in big trouble.)
Needless to say, fault tolerance was virtually non-existent.
1995 was a watershed year. Under the leadership of the
National Center for Supercomputing Applications, Argonne
National Lab, the San Diego Supercomputing Center, and
Sandia National Lab, the I-WAY (International Wide-Area
Year) was hosted at Supercomputing `954
in San Diego [5].
The I-WAY sought to demonstrate the potential of distributed,
virtual supercomputing by hosting over sixty applications on a
national testbed [11, 12]. This testbed was cobbled together in
a matter of months across many different institutions and
included relatively primitive tools for the scheduling of
2
The Open Software Foundation released the source code for DCE 1.0 in
early 1992 (http://www.opengroup.org/dce/).
3
The CORBA 1.0 specification was released in October 1991
(http://www.corba.org/).
4
http://www.supercomp.org/.
machines for different applications and for security for their
access [7].
The trials and tribulations of such an arduous demonstration
paid-off since it crystallized for a much broader segment of
the scientific community, what was possible and what needed
to be done [15]. In early 1996, the Globus Project officially
got under way after being proposed to ARPA in November
1994. The process and communication middleware system
called Nexus [9] was originally built by Argonne National
Laboratory to essentially be a compiler target and provide
remote service requests across heterogeneous machines for
application codes written in a higher-level language. The goal
of the Globus project [1] was to build a global Nexus that
would provide support for resource discovery, resource
composition, data access, authentication, authorization, etc.
The first Globus applications were demonstrated at
Supercomputing `974
.
Globus was by no means alone in this arena. During this
same time period, the Legion project [10] was generalizing the
concepts developed for Mentat into the notion of a “global
operating system”. The Condor project [6] was already
harvesting cycles from the growing number of desktop
machines that a typical institution was now deploying. The
UNICORE project (UNiform Interface to COmputing
REsources) [2] was started in Germany in 1997.
Backing up in time to 1995, Smarr and Catlett at the
National Center for Supercomputing Applications (NCSA)
had constructed a cluster of SGI Power Challenge parallel
computers that they called the Power Challenge Array. They
envisioned a distributed metacomputer of these machines at
partner sites around the country that they intended to call the
SGI Power Grid. When the NSF supercomputing centers
were re-competed in 1996, researchers at the consortium of
NCSA, University of Illinois at Urbana-Champaign, Rice
University, Indiana University, Argonne National Laboratory,
and University of Illinois at Chicago decided to expand on the
Power Grid concept. In their proposal to the NSF, they stated:
“(Our vision) is the integration of many computational,
visualization and information resources into a coherent
infrastructure… We refer to the integrated resources
as the `Power Grid’ or simply the Grid”.
After this, the term Grid rapidly replaced the use of
metacomputer. At Supercomputing ‘97, the Globus
demonstration testbed was called a computational Grid. By
1998, the term Grid computing was firmly established with
the publication of The Grid: Blueprint for a New Computing
Infrastructure by Foster and Kesselman [8].
During this time, the interest and momentum of Grid
computing was rapidly growing in both academia and
industry. This was facilitated (in no small part) by the
explosive growth and adoption of the World Wide Web by all
segments of science, industry, commerce, and society. The
precedent of the World Wide Web made it very easy for a
large number of people to conceptually extrapolate the serving
of web pages to the discovery and management of computing
resources, in general, distributed across a Grid. This growing
interest prompted the formation of a Grid Forum to produce
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and promote standards that industry could build products to.
The Grid Forum had its first meeting in June 1999 at NASA
Ames while similar efforts were getting started in Europe and
the Asia-Pacific region. All of these efforts merged into the
Global Grid Forum1
, which had its first meeting in March
2001 at the Amsterdam Science and Technology Center.
B. Current Goals and Vision
This evolution of the Grid has led to the current vision of
Grid computing – a vision of uniform and controlled access to
computing resources, seamless global aggregation of
resources enabling seamless composition of services, and
leading to autonomic self-managing behaviors. This vision
applies to all manner and scale of computing resources – from
personal digital assistants (PDAs), to enterprise Grids, to an
open-ended, global-scale Grid environment, i.e., the Grid.
Seamless Aggregation of Resources and Services: Early
Grid computing efforts focused on aggregating geographically
distributed resources spanning multiple administrative
domains, and this remains to be a key goal. Aggregation
included both the aggregation of capacity (e.g., clustering of
individual systems to increase computational power or storage
capacity) as well as the aggregation of capability (e.g.,
combining a specialized instrument with a large storage
system and a computing cluster). Key capabilities to enable
such aggregation included protocols and mechanisms to
secure discovery, access to and aggregation of resources for
the realization of virtual organizations, and the development
of applications that can exploit such an aggregated execution
environment.
These goals have motivated the generalization of all Grid
resources into services. A key driver for this generalization
was the emergence of Web Services as a dominant technology
in the e-commerce domain. This formulation of Grid
computing built on the concept of a Grid service as the
fundamental abstraction, allowing Grids and Grid applications
to consist of dynamically composed services.
A Ubiquitous Service-Oriented Architecture: While Grid
computing historically grew from the desire to do distributed,
virtual supercomputing, the capabilities needed to accomplish
this are actually quite fundamental with a far-reaching,
broader impact. The ability to do resource and data discovery
along with resource scheduling and management in a secure,
scaleable, open-ended environment based on well-known and
widely adopted services enables a wide variety of application
domains and styles of computation. These fundamental
capabilities enable not only distributed supercomputing, but
also internet computing, web computing, cycle harvesting,
peer-to-peer computing, etc. In short, we could refer to this as
a ubiquitous service-oriented architecture -- machines large
and small (from wireless PDAs to big iron supercomputers)
and services that they provide could be dynamically combined
in a spectrum of virtual organizations according to the needs
and requirements of the participants involved.
Such an architecture should also be language/programming
model agnostic and facilitate interoperability. Hence, rather
than imposing a particular programming model, it should
enable the integration of a wide variety of programming
models. For example, rather than imposing an object model,
as CORBA does, it should allow a CORBA-based object-
oriented system to be composed with another non-OO system,
etc. By providing a common set of interoperable Grid-level
services, it should be easier to produce an interoperable set of
application-level services.
Autonomic Behaviors: The inherent scale, heterogeneity,
dynamism, and non-determinism of Grids and Grid
applications have resulted in complexities that are quickly
breaking current paradigms, making both the infrastructure
and the applications brittle and insecure. Clearly, there is a
need for a fundamental change in how Grids and Grid
applications are developed and managed. This is leading
researchers to consider alternative paradigms that are based on
the strategies used by systems in nature to deal with
complexity, dynamism, heterogeneity, and uncertainty. This
emerging vision aims at realizing computing systems and
applications capable of configuring, managing, interacting,
optimizing, securing, and healing themselves with minimum
human intervention, and has lead to a number of recent
research initiatives such as Autonomic Grids, Cognitive Grids,
and Semantic Grids.
III. MAKING GRIDS A REALITY
While Grids have come a very long way from the efforts of
several labs trying to address thorny, fundamental issues in
distributed computing by building research prototypes, Grids
still have a very long way to go before they are a practical,
widely deployed reality. At the current time, several basic Grid
tools are stabilizing and many Grid projects, including some
very well funded international projects, are deploying sizeable
Grids. However, one can argue that Grids will not be a
practical reality until (1) there is a core set of Grid services,
with (2) sufficient reliability, that are (3) widely deployed
enough to be useable. This is the current challenge for making
Grids a reality. The issue is how to make this happen.
A. Expanding the Scale and Scope of Deployment
A number of very large Grid projects are currently
underway. Examples include the EU DataGrid project, the
NSF TeraGrid project, and the Japanese NaReGI project.
Many other smaller projects are currently underway, too,
involving just a few institutions in a specific application
domain. There are also a number of Grid-like commercial
products for cycle harvesting, distributed scheduling, etc. In
all these cases, however, the deployment and use of the Grid
tools involved is not as easy as one would like. At this point,
serious Grid deployment and use requires a group of
knowledgeable, dedicated people. Hence, tools must be
simpler for reliable deployment and use by non-specialists.
Tools should also be configurable to the intended scope of
deployment. Most Grid tools have been designed to be open-
ended to support the concept of an open-ended “Grid” while,
in fact, they have been used in an enterprise-scale deployment.
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While the ultimate vision is to have a ubiquitous service-
oriented architecture, we must realize that a practical,
evolutionary step is to have tools that can support the
enterprise-scale Grid where many issues can be resolved, or
“defined away”, by policy.
B. Standards - The Web/Grid Convergence
A key to defining exactly what the core Grid services are
and facilitating their easy deployment on all scales is
standards. To this end, the Global Grid Forum defined the
Open Grid Services Architecture (OGSA) extending the Web
Services (to support transient and stateful behaviors) and
combined them with Grid protocols to define the Open Grid
Service Infrastructure (OGSI), providing a uniform
architecture for building and managing Grids and Grid
applications. To permit the management of both stateful and
stateless web services, the Web Services Resource Framework
(WSRF) was defined. This offers a potential alternative to
OGSI and represents an opportunity for the proper
convergence of web and Grid service architectures. This
convergence has tremendous importance since it offers a solid
platform for the further adoption of web and Grid services in
response to the significant economic motivations of the
marketplace.
C. Non-Technical Barriers to Acceptance
Besides the technical issues concerning Grid adoption
mentioned above, there are clearly many non-technical or
cultural barriers as well [13]. Grid computing, in many ways,
is about resource sharing while the "corporate culture" of
many organizations may be fundamentally opposed to this.
Some organizational units may jealously guard their machines
or data out of a perceived economic or security threat. On a
legal level, Grid computing may require the redefinition of
ownership, copyrights, and licensing. Clearly, as Grid
computing progresses, such cultural, legal, and economic
issues will have to be resolved by adjusting our cultural
policies and expectations to integrate what the technology will
provide.
IV. AN OVERVIEW OF THE SPECIAL ISSUE
The overall goal of this special issue is to provide an
overview of the current state in the field of Grid computing
including the state-of-the-art of research in Grid applications,
Grid model, environments and tools, Grid architectures and
infrastructures, and Grid computing trends and visions for the
future. The papers in this issue include overviews of the field
and its issues that target non-experts, while including
sufficient coverage and technical content to be a valuable
reference for researchers in the field.
To this end, this special issue consists of four parts
designed to lead the reader through a sequence of major topic
areas. We start with a group of papers giving concrete
descriptions of current Grids and Grid applications to illustrate
what's possible and being done today to motivate the broadest
possible interest by specialists and non-specialists alike. The
next section of papers discusses tools and methods being used
by Grid practitioners to build a variety of Grid applications.
The third section explores the emerging, fundamental Grid
architecture on which the global infrastructure will eventually
depend. Finally, we present a section of papers focusing on
the future direction and vision for Grid computing. We now
summarize each part in turn.
Part I of this special issue focuses on Grid deployments and
Grid applications, and includes papers describing
representative deployments and applications in various
disciplines of science and engineering. The first paper in this
part, The Earth System Grid: Supporting the Next Generation
of Climate Modeling Research, describes the Earth System
Grid (ESG) that addresses the management, discovery, access,
and analysis of very large, distributed datasets associated with
the modeling and simulation of the Earth’s climate. This
project address core Grid computing issues (authentication,
authorization, large-scale data transport and management,
high-performance remote data access, scalable data
replication, cataloging, data discovery, and distributed
monitoring) in the context of this application. The next paper,
Searching Large Data Sets within a Grid Enabled
Engineering Applications, DAME, describes the use of Grids
in the aero-engine health-monitoring domain. This paper
describes the Signal Data Explorer application developed
within the DAME project, which uses advanced neural-
network based methods to search for matching patterns in
time-series vibration data originating from Rolls-Royce aero-
engines. The large volume of data associated with the problem
warrants the development of a distributed search engine,
where data is held at a number of geographically disparate
locations. The third paper, The Computational Chemistry
Prototyping Environment, presents a Grid-based environment
for computational chemistry consisting of a general scientific
workflow environment, a domain specific example for
quantum chemistry, and the design of a workflow user
interface, and efforts at database integration. The final paper
in Part I, Japanese Computational Grid Research Project:
NAREGI, describes one of the major Japanese national IT
projects (National Research Grid Initiative) based on
collaborations among industry, academia, and the government.
The efforts consists of research and development in high–
performance, scalable Grid middleware technologies, as well
as research on leading-edge, Grid-enabled nanoscience and
nanotechnology simulation applications.
Part II of the special issue focuses on models,
environments, and tools for Grid computing. This part
includes papers addressing programming paradigms, problem
solving environments, application development and
management tools, and data-management and exploration
systems. This first paper in this part, The Grid Application
Toolkit: Towards Generic and Easy Application
Programming Interfaces for the Grid, addresses the need for a
high-level application programming toolkit, bridging the gap
between existing Grid middleware and application-level
needs. This paper describes the Grid Application Toolkit
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(GAT) that provides a unified programming interface to the
Grid infrastructure tailored to the needs of the Grid
application developer. The software architecture of GridLab is
also presented. The next paper, Building Grid Portal
Applications from a Web-Service Component Architecture,
describes an application architecture based on wrapping user
applications and application workflows as web services and
web service resources. These services are visible through a
family of Grid portal components, which can be used to
configure, launch, and monitor complex applications built
from the services. The third paper in this part, Deploying the
Narada Brokering Substrate in Aiding Efficient Web & Grid
Service Interactions, presents a messaging infrastructure for
collaboration, peer-to-peer, and Grid applications. The paper
also describes the integration of the NaradaBrokering
substrate with Web Services. The final paper of Part II, Data
Grids, Digital Libraries and Persistent Archives: An
Integrated Approach to Publishing, Sharing and Archiving
Data, examines the synergies between data management
systems, including Grids, Data Grids, digital libraries and
persistent archives, and investigates the Grid infrastructure
required for the generation, management, sharing. and
preservation of information.
Part III of this issue concentrates on the Grid architecture,
infrastructure, and middleware, and includes research papers
describing core efforts such as Legion, Web Service Resource
Framework (WSRF), Open Grid Services Architecture
(OGSA), workflow management in Grid environments,
resource discovery, resource management and scheduling, and
security. This first paper in this part, Legion: Lessons Learnt
Building a Grid Operating System, describes the Legion
operating system-like Grid middleware, which provides a
virtual machine interface layered over the Grid. The paper
also describes the evolution of Legion and important lessons,
both technical and sociological, learned in the process. The
next paper, Modeling and Managing State in Distributed
Systems: The Role of OGSI and WSRF, introduces two
approaches to modeling and manipulating state within a Web
services framework: the Open Grid Services Infrastructure
(OGSI) and Web Services Resource Framework (WSRF).
OGSI addresses the creation and management of a stateful
Web service. WSRF refactors and evolves OGSI to exploit
new Web services standards. The relationship between OGSI
and WSRF is explained. The third paper in this part,
Coordination in Intelligent Grid Environments, explores the
construction of intelligent computational Grids, where societal
services exhibit intelligent behavior. The paper focuses on the
coordination service that, acting as a proxy on behalf of end
users, reacts to unforeseen events, plans how to carry out
complex tasks, and learns from the past history of the system.
A prototype system used for a virtual laboratory in
computational biology is presented. The fourth paper in the
part, Agreement-Based Resource Management, describes a
unifying resource management framework, based on the
concept of agreement-based resource management, to address
the requirements of resource sharing in Grid environments. A
general agreement model is presented and current resource
management systems are examined in the context of this
model. The final paper of part III, Security for Grids,
addresses the security challenges of Grid environments. It
characterizes security activities, examines the current state of
the art, and introduces new technologies that promise to meet
the security requirements of Grids more completely.
Part IV outlines current trends, future directions, and
visions for the Grid. The first paper in this part, Conceptual
and Implementation Models for the Grid, adopts models from
distributed computing systems as a basis for defining and
characterizing Grids and their programming models and
systems. This paper motivates the need for a self-managing
Grid computing paradigm and analyzes existing Grid
programming systems that address this need. The second
paper, The Semantic Grid: Past, Present and Future, presents
the Semantic Grid as an extension of the current Grid in which
information and services are given well-defined meaning. This
paper outlines the requirements of the Semantic Grid,
discusses the state of the art, and identifies the research
challenges. The third paper, Cyberinfrastructure for Science
and Engineering: Promises and Challenges, describes the
National Science Foundation’s vision for a ubiquitous and
accessible cyberinfrastructure that has the potential for
revolutionizing all areas of science and engineering research
and education. The paper also outlines some of the challenges,
and a possible path toward reaching the vision. The fourth
paper in this part, Grid Computing and Beyond: The Context
of Dynamic Data Driven Applications Systems, introduces
Dynamic Data Driven Applications Systems (DDDAS)
paradigm for the Grid that is based on the ability to
incorporate additional data into an executing application. The
paper outlines the requirements of DDDAS and addresses the
new capabilities and the technology challenges and
opportunities of DDAS in Grid environments. The final paper
of part IV, Grid Economy and Service-Oriented Grid
Computing, proposes computational economy as a metaphor
for effective management of resources and application
scheduling in Grid environments. This paper also presents a
service-oriented Grid architecture driven by Grid economy
and commodity and auction models for resource allocation.
ACKNOWLEDGMENT
We would like to thank the authors for their excellent
contributions to this special issue, and Jim Calder, Viktor
Prasanna, and the editorial board for their guidance and
suggestions in putting the issue together. We also wish to
thank Charlie Catlett for filling in several key historical
aspects in the evolution of Grid computing.
REFERENCES
[1] "The Globus Alliance", http://www.globus.org.
[2] "Unicore Forum", http://www.unicore.org.
[3] C. Catlett and L. Smarr, "Metacomputing",
Communication of the ACM, 36(6), 1992, 44-52.
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[4] F. J. Corbat and V. A. Vyssotsky, "Introduction and
overview of the Multics system", Proc. AFIPS 1965
FJCC, 27(1), 1965, 185-196.
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[6] D. H. J. Epema, M. Livny, R. v. Dantzig, X. Evers and J.
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[7] I. Foster, J. Geisler, W. Nickless, W. Smith and S.
Tuecke, "Software Infrastructure for the I-WAY High
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[8] I. Foster and C. Kesselman, eds., The Grid: Blueprint for
a New Computing Infrastructure, Morgan Kaufmann
Publishers, 1998.
[9] I. Foster, C. Kesselman and S. Tuecke, "The Nexus
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[10] A. S. Grimshaw and W. A. Wulf, "The Legion Vision of
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Satellite Image Processing: Metacomputing in CC++",
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[13] I. Platform Computing, "The Politics of Grid:
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[16] S. Wallach, Information Week, 1992.
Manish Parashar (M’89–SM’03) is Associate Professor of Electrical and
Computer Engineering at Rutgers University, where he also is director of the
Applied Software Systems Laboratory. He received a BE degree in Electronics
and Telecommunications from Bombay University, India in 1988, and MS and
Ph.D. degrees in Computer Engineering from Syracuse University in 1994. He
has received the NSF CAREER Award (1999) and the Enrico Fermi
Scholarship from Argonne National Laboratory (1996). His current research
interests include autonomic computing, parallel, distributed and Grid
computing, networking, scientific computing, and software engineering.
Manish is a member of the executive committee of the IEEE Computer
Society Technical Committee on Parallel Processing (TCPP), part of the IEEE
Computer Society Distinguished Visitor Program (2004-2006), and a member
of ACM. He is also the co-founder of the IEEE International Conference on
Autonomic Computing (ICAC). Manish has co-authored over 130 technical
papers in international journals and conferences, has co-authored/edited 5
books/proceedings, and has contributed to several others in the area of parallel
and distributed computing.
Craig A. Lee (M’89) is a Section Manager in the Computer Systems Research
Department of The Aerospace Corporation, a non-profit, federally funded,
research and development center. Dr. Lee has worked in the area of parallel
and distributed computing for the last twenty-five years. He has built many
application prototypes with a strong focus on experimental languages, tools
and environments. Dr. Lee has also conducted DARPA and NSF sponsored
research in the areas of Grid computing, optimistic models of computation,
active networks, and distributed simulations, in collaboration with USC,
UCLA, Caltech, ANL, and the College of William and Mary. This work has
led naturally to Dr. Lee's involvement in the Global Grid Forum as Area Co-
Chair of the Applications, Programming Models, and Environments Area, and
Co-Chair of the GridRPC Working Group. He is also on the Steering
Committees of CCGrid (Cluster Computing and the Grid), and the
International Workshop on Grid Computing. He has served on the program
committee for many other conferences and workshops, and has served as a
panelist for the NSF and NASA, and as an external evaluator for INRIA. Dr.
Lee has co-authored over 50 technical works, including 4 book chapters and 7
edited volumes and issues, and has recently joined the editorial board of
Future Generation Computing Systems. Dr. Lee is a member of the ACM and
the IEEE Computer Society, and has lectured undergraduate computer science
courses at UCLA.