Cs6703 grid and cloud computing Study material
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Cs6703 grid and cloud computing book
CS6703 Grid and Cloud computing Book as per Anna University regulation 2013 syllabus covered. Complete reference of Text book..If you need call to 8012582176
Grid computing
Grid computing allows for sharing and coordinated use of diverse computing resources virtually. It provides uniform access to computational resources over the Internet similar to how the web provides access to documents. Key motivations for grid computing include enabling large-scale science through geographically dispersed resources. Grid architectures have fabric, connectivity, resource, collective, and application layers. The Globus Toolkit is commonly used open source software that provides components for security, data management, scheduling, and more. Grids are used in various domains like earthquake and climate simulation.
Challenges and advantages of grid computing
The document discusses several challenges of grid computing including lack of clear standards, difficulty distinguishing it from distributed computing, limited grid-enabled software, sharing resources across different types of services and organizations, complex administration and management, and limited applications. Key challenges are heterogeneity of resources, security, resource management, programming for applications, and accounting infrastructure. Benefits include exploiting underutilized resources, massive parallel processing, virtual collaboration environments, access to additional resources, load balancing, reliability, and improved management of distributed systems.
Grid Computing Frameworks
This document provides an overview of grid computing frameworks. It introduces grid computing and discusses its key concepts. Several popular grid frameworks are described, including Globus Toolkit, Gridbus Toolkit, UNICORE, and Legion. Each framework is summarized in terms of its origins, architecture, and impact. The document concludes by noting that grid frameworks facilitate the development of grid applications and management of grid infrastructure.
Grid computing & its applications
Grid computing involves distributing computing resources across a network to tackle large problems. The Worldwide LHC Computing Grid (WLCG) was established to support the Large Hadron Collider (LHC) experiment, which produces around 15 petabytes of data annually. The WLCG uses a four-tiered model, with raw data stored at Tier-0 (CERN), copies distributed to Tier-1 data centers, computational resources provided by Tier-2 centers, and Tier-3 facilities providing additional analysis capabilities. This distributed model has proven effective in supporting the first year of LHC data collection and analysis through globally shared computing resources.
Grid computing
Actually Grid Computing is a network in which there are many computers and they can share their resources with each other using this network
Grid computing
The document discusses grid computing and provides examples. It begins with an introduction to supercomputers and provides Param Padma as an example. It then defines grid computing, discussing its evolution and advantages over supercomputers. Design considerations for grid computing include assigning work randomly to nodes to check for accurate results due to lack of central control. Implementation involves using middleware like BOINC and Alchemi, which are described. The document outlines service-oriented grid architecture and challenges. It provides examples of grid initiatives worldwide like TeraGrid in the US and Garuda in India.
Grid computing [2005]
Grid computing allows for the sharing and aggregation of distributed computing resources like computers, networks, databases and instruments. It provides a large virtual computing system for end users and applications. Key characteristics include facilitating solutions to large, complex problems across locations and organizations through integrated and collaborative use of heterogeneous resources. Popular applications include medical research, astronomy, climate modeling and more. Examples of operational grids discussed are TeraGrid, Pauá Grid Project and academic research projects like SETI@home.
Recommended
Cs6703 grid and cloud computing book
CS6703 Grid and Cloud computing Book as per Anna University regulation 2013 syllabus covered. Complete reference of Text book..If you need call to 8012582176
Grid computing
Grid computing allows for sharing and coordinated use of diverse computing resources virtually. It provides uniform access to computational resources over the Internet similar to how the web provides access to documents. Key motivations for grid computing include enabling large-scale science through geographically dispersed resources. Grid architectures have fabric, connectivity, resource, collective, and application layers. The Globus Toolkit is commonly used open source software that provides components for security, data management, scheduling, and more. Grids are used in various domains like earthquake and climate simulation.
Challenges and advantages of grid computing
The document discusses several challenges of grid computing including lack of clear standards, difficulty distinguishing it from distributed computing, limited grid-enabled software, sharing resources across different types of services and organizations, complex administration and management, and limited applications. Key challenges are heterogeneity of resources, security, resource management, programming for applications, and accounting infrastructure. Benefits include exploiting underutilized resources, massive parallel processing, virtual collaboration environments, access to additional resources, load balancing, reliability, and improved management of distributed systems.
Grid Computing Frameworks
This document provides an overview of grid computing frameworks. It introduces grid computing and discusses its key concepts. Several popular grid frameworks are described, including Globus Toolkit, Gridbus Toolkit, UNICORE, and Legion. Each framework is summarized in terms of its origins, architecture, and impact. The document concludes by noting that grid frameworks facilitate the development of grid applications and management of grid infrastructure.
Grid computing & its applications
Grid computing involves distributing computing resources across a network to tackle large problems. The Worldwide LHC Computing Grid (WLCG) was established to support the Large Hadron Collider (LHC) experiment, which produces around 15 petabytes of data annually. The WLCG uses a four-tiered model, with raw data stored at Tier-0 (CERN), copies distributed to Tier-1 data centers, computational resources provided by Tier-2 centers, and Tier-3 facilities providing additional analysis capabilities. This distributed model has proven effective in supporting the first year of LHC data collection and analysis through globally shared computing resources.
Grid computing
Actually Grid Computing is a network in which there are many computers and they can share their resources with each other using this network
Grid computing
The document discusses grid computing and provides examples. It begins with an introduction to supercomputers and provides Param Padma as an example. It then defines grid computing, discussing its evolution and advantages over supercomputers. Design considerations for grid computing include assigning work randomly to nodes to check for accurate results due to lack of central control. Implementation involves using middleware like BOINC and Alchemi, which are described. The document outlines service-oriented grid architecture and challenges. It provides examples of grid initiatives worldwide like TeraGrid in the US and Garuda in India.
Grid computing [2005]
Grid computing allows for the sharing and aggregation of distributed computing resources like computers, networks, databases and instruments. It provides a large virtual computing system for end users and applications. Key characteristics include facilitating solutions to large, complex problems across locations and organizations through integrated and collaborative use of heterogeneous resources. Popular applications include medical research, astronomy, climate modeling and more. Examples of operational grids discussed are TeraGrid, Pauá Grid Project and academic research projects like SETI@home.
Grid computing
This document discusses grid architecture design. It covers building grid architectures, different types of grids like computational and data grids, common grid topologies including intra, extra, and inter grids. It also outlines the phases and activities in grid design like deciding the grid type, using a methodology of workshops, documentation, and prototyping. Finally, it discusses benefits of grids such as exploiting underutilized resources, enabling parallel processing and collaboration, improving access to and balancing of resources, and better reliability and management.
Gcc notes unit 1
This document discusses the evolution of distributed computing from centralized mainframes to modern cloud, grid, and parallel computing systems. It covers key topics like:
- The shift from high-performance computing (HPC) to high-throughput computing (HTC) and new paradigms like cloud, grid, and peer-to-peer networks.
- The progression of computing platforms and generations from mainframes to personal computers to modern distributed systems.
- Degrees of parallelism including bit-level, instruction-level, data-level, task-level, and job-level and how these have improved over time.
- Major applications that have driven distributed computing including science, engineering, banking, and
Grid Computing
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.
Unit i introduction to grid computing
Grid computing is the sharing of computer resources from multiple administrative domains to achieve common goals. It allows for independent, inexpensive access to high-end computational capabilities. Grid computing federates resources like computers, data, software and other devices. It provides a single login for users to access distributed resources for tasks like drug discovery, climate modeling and other data-intensive applications. Current grids are used for distributed supercomputing, high-throughput computing, on-demand computing and other methods. Grids benefit scientists, engineers and other users who need to solve large problems or collaborate globally.
4. the grid evolution
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.
Grid computing
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.
Grid computing ppt 2003(done)
The document discusses the grid, which allows for integrated and collaborative use of geographically separated computing resources. Grid computing enables sharing and aggregation of distributed autonomous resources dynamically based on availability, capability, performance, cost and user requirements. Key characteristics of grid systems include coordinating resources not controlled by a central authority, using open standards, and providing quality of service.
Grid Presentation
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.
grid computing
The document discusses grid computing and the development of computational grids. Key points:
- Grids allow for sharing of computing power and resources across geographic locations through networked supercomputers, databases, and instruments.
- Major organizations like NASA, DOE, and NSF are working to build computational grids for applications like scientific simulations and instrument control.
- Indiana University is involved in grid research through various departments and projects focused on resource sharing, portals, middleware, and more.
Grid computing the grid
This case study report presentation provides an overview of grid computing. It defines grid computing and discusses its key building blocks including networks, computational nodes, and common infrastructure standards. The presentation also examines grid computing models like distributed super-computing and data-intensive computing. Challenges of grid computing and examples of applications in fields like life sciences, engineering, and physical sciences are outlined.
Grid computing ppt
Grid computing allows for the sharing of computer resources across a network. It utilizes both reliable tightly-coupled cluster resources as well as loosely-coupled unreliable machines. The grid system balances resource usage to provide quality of service to participants. Grid computing works by having at least one administrative computer and middleware that allows computers on the network to share processing power and data storage. It has advantages like improved efficiency, resilience, and ability to handle large applications, but also challenges around resource sharing and licensing across multiple servers.
Introduction to Grid Computing
This document introduces grid computing by discussing its applications to problems requiring large-scale data analysis, such as high energy physics experiments. It defines a grid as an infrastructure involving integrated and collaborative use of computers, networks, databases, and instruments across multiple organizations. Grids allow for computational, data, and network sharing and aim to provide a cost-effective, scalable platform for data-intensive problems. Virtual organizations are dynamically formed groups that define rules for sharing resources to solve specific problems. The document outlines grid architecture and operations, including resource discovery, scheduling jobs, and accounting. Benefits of grids include exploiting underutilized resources and parallel processing capacity.
Grid Computing
Grid computing involves linking together distributed computer resources from multiple administrative domains to achieve a common goal. Resources in a grid are heterogeneous and geographically dispersed. A grid differs from a cluster in that it provides a consistent, dependable, and transparent collection of computing resources across wide distances. Grid infrastructure must respect local autonomy, handle heterogeneous hardware, and be resilient and dynamic.
Grid computing
This document provides an overview of grid computing. It defines a grid as a collection of distributed heterogeneous computing and data resources available through network tools and protocols. It discusses several examples of grid computing projects like SETI@home, Distributed.net, and virtual organizations. It also covers types of grids based on shared resources, topology, and behavior. The document outlines the layered structure of a grid and standards like OGSA, OGSI, and GSI that enable interoperability. It provides descriptions of key grid components like resource brokers, information services, security, data transfer, job submission, and problem solving environments.
Inroduction to grid computing by gargi shankar verma
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 2007
Grid computing allows for the sharing and coordinated use of distributed computing resources. It enables organizations to share idle computing systems and resources. Key benefits include exploiting underutilized resources, enabling large-scale parallel processing and collaboration, and providing access to additional resources. Common applications involve scientific research where data is collected and stored across different sites and organizations and requires large-scale analysis.
Grid computing
The Grid means the infrastructure for the Advanced Web, for computing, collaboration and communication.
The goal is to create the illusion of a simple yet large and powerful self managing virtual computer out of a large collection of connected heterogeneous systems sharing various combinations of resources.
“Grid” computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications, and ,in some cases, high-performance orientation .
We presented the Grid concept in analogy with that of an electrical power grid and Grid vision
Grid Computing
Grid computing enables sharing of geographically distributed computing resources through a network. It allows for virtual organizations to collaborate on common goals without central control. The document discusses the types of grid computing including computational, data, and scavenging grids. It also outlines the key components of a grid including protocols, architecture, security, and resource management. Examples of existing grid projects are provided such as SETI@Home, EGEE, and BeINGrid.
Grid computing
Grid computing is a distributed computing system where a group of connected computers work together as a single large computing resource. It allows users to submit tasks that are divided into independent subtasks and distributed across available grid resources. Key benefits include solving larger problems faster through collaboration and making better use of existing hardware. While standards are still evolving, grid computing has enabled projects like the Large Hadron Collider which involves over 1,800 physicists across 32 countries.
Grid protocol architecture
The document discusses the five layers of the grid protocol architecture: 1) the fabric layer which provides access to different resource types, 2) the connectivity layer which defines core communication and authentication protocols, 3) the resource layer which defines protocols for publishing, discovering, and accessing individual resources, 4) the collective layer which captures interactions across collections of resources through directory services, and 5) the application layer which comprises user applications built on top of the lower layers and operate in virtual organization environments.
Cloud Computing Automation: Integrating USDL and TOSCA
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Jorge cardoso caise-usdl-tosca-2013-06-18c
The document discusses integrating the USDL (Unified Service Description Language) and TOSCA (Topology and Orchestration Specification for Cloud Applications) standards to automate parts of the lifecycle of cloud applications. It proposes using Linked USDL to provide unique identifiers and access service descriptions, and using TOSCA to describe application deployment and management in an executable way. The approach aims to enable discovery, selection, deployment and management of cloud applications through the combined use of USDL and TOSCA. It also discusses challenges around routing service requests, dynamic binding of descriptors, and achieving interoperability between the two standards.
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Grid computing
This document discusses grid architecture design. It covers building grid architectures, different types of grids like computational and data grids, common grid topologies including intra, extra, and inter grids. It also outlines the phases and activities in grid design like deciding the grid type, using a methodology of workshops, documentation, and prototyping. Finally, it discusses benefits of grids such as exploiting underutilized resources, enabling parallel processing and collaboration, improving access to and balancing of resources, and better reliability and management.
Gcc notes unit 1
This document discusses the evolution of distributed computing from centralized mainframes to modern cloud, grid, and parallel computing systems. It covers key topics like:
- The shift from high-performance computing (HPC) to high-throughput computing (HTC) and new paradigms like cloud, grid, and peer-to-peer networks.
- The progression of computing platforms and generations from mainframes to personal computers to modern distributed systems.
- Degrees of parallelism including bit-level, instruction-level, data-level, task-level, and job-level and how these have improved over time.
- Major applications that have driven distributed computing including science, engineering, banking, and
Grid Computing
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.
Unit i introduction to grid computing
Grid computing is the sharing of computer resources from multiple administrative domains to achieve common goals. It allows for independent, inexpensive access to high-end computational capabilities. Grid computing federates resources like computers, data, software and other devices. It provides a single login for users to access distributed resources for tasks like drug discovery, climate modeling and other data-intensive applications. Current grids are used for distributed supercomputing, high-throughput computing, on-demand computing and other methods. Grids benefit scientists, engineers and other users who need to solve large problems or collaborate globally.
4. the grid evolution
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.
Grid computing
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.
Grid computing ppt 2003(done)
The document discusses the grid, which allows for integrated and collaborative use of geographically separated computing resources. Grid computing enables sharing and aggregation of distributed autonomous resources dynamically based on availability, capability, performance, cost and user requirements. Key characteristics of grid systems include coordinating resources not controlled by a central authority, using open standards, and providing quality of service.
Grid Presentation
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.
grid computing
The document discusses grid computing and the development of computational grids. Key points:
- Grids allow for sharing of computing power and resources across geographic locations through networked supercomputers, databases, and instruments.
- Major organizations like NASA, DOE, and NSF are working to build computational grids for applications like scientific simulations and instrument control.
- Indiana University is involved in grid research through various departments and projects focused on resource sharing, portals, middleware, and more.
Grid computing the grid
This case study report presentation provides an overview of grid computing. It defines grid computing and discusses its key building blocks including networks, computational nodes, and common infrastructure standards. The presentation also examines grid computing models like distributed super-computing and data-intensive computing. Challenges of grid computing and examples of applications in fields like life sciences, engineering, and physical sciences are outlined.
Grid computing ppt
Grid computing allows for the sharing of computer resources across a network. It utilizes both reliable tightly-coupled cluster resources as well as loosely-coupled unreliable machines. The grid system balances resource usage to provide quality of service to participants. Grid computing works by having at least one administrative computer and middleware that allows computers on the network to share processing power and data storage. It has advantages like improved efficiency, resilience, and ability to handle large applications, but also challenges around resource sharing and licensing across multiple servers.
Introduction to Grid Computing
This document introduces grid computing by discussing its applications to problems requiring large-scale data analysis, such as high energy physics experiments. It defines a grid as an infrastructure involving integrated and collaborative use of computers, networks, databases, and instruments across multiple organizations. Grids allow for computational, data, and network sharing and aim to provide a cost-effective, scalable platform for data-intensive problems. Virtual organizations are dynamically formed groups that define rules for sharing resources to solve specific problems. The document outlines grid architecture and operations, including resource discovery, scheduling jobs, and accounting. Benefits of grids include exploiting underutilized resources and parallel processing capacity.
Grid Computing
Grid computing involves linking together distributed computer resources from multiple administrative domains to achieve a common goal. Resources in a grid are heterogeneous and geographically dispersed. A grid differs from a cluster in that it provides a consistent, dependable, and transparent collection of computing resources across wide distances. Grid infrastructure must respect local autonomy, handle heterogeneous hardware, and be resilient and dynamic.
Grid computing
This document provides an overview of grid computing. It defines a grid as a collection of distributed heterogeneous computing and data resources available through network tools and protocols. It discusses several examples of grid computing projects like SETI@home, Distributed.net, and virtual organizations. It also covers types of grids based on shared resources, topology, and behavior. The document outlines the layered structure of a grid and standards like OGSA, OGSI, and GSI that enable interoperability. It provides descriptions of key grid components like resource brokers, information services, security, data transfer, job submission, and problem solving environments.
Inroduction to grid computing by gargi shankar verma
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 2007
Grid computing allows for the sharing and coordinated use of distributed computing resources. It enables organizations to share idle computing systems and resources. Key benefits include exploiting underutilized resources, enabling large-scale parallel processing and collaboration, and providing access to additional resources. Common applications involve scientific research where data is collected and stored across different sites and organizations and requires large-scale analysis.
Grid computing
The Grid means the infrastructure for the Advanced Web, for computing, collaboration and communication.
The goal is to create the illusion of a simple yet large and powerful self managing virtual computer out of a large collection of connected heterogeneous systems sharing various combinations of resources.
“Grid” computing has emerged as an important new field, distinguished from conventional distributed computing by its focus on large-scale resource sharing, innovative applications, and ,in some cases, high-performance orientation .
We presented the Grid concept in analogy with that of an electrical power grid and Grid vision
Grid Computing
Grid computing enables sharing of geographically distributed computing resources through a network. It allows for virtual organizations to collaborate on common goals without central control. The document discusses the types of grid computing including computational, data, and scavenging grids. It also outlines the key components of a grid including protocols, architecture, security, and resource management. Examples of existing grid projects are provided such as SETI@Home, EGEE, and BeINGrid.
Grid computing
Grid computing is a distributed computing system where a group of connected computers work together as a single large computing resource. It allows users to submit tasks that are divided into independent subtasks and distributed across available grid resources. Key benefits include solving larger problems faster through collaboration and making better use of existing hardware. While standards are still evolving, grid computing has enabled projects like the Large Hadron Collider which involves over 1,800 physicists across 32 countries.
Grid protocol architecture
The document discusses the five layers of the grid protocol architecture: 1) the fabric layer which provides access to different resource types, 2) the connectivity layer which defines core communication and authentication protocols, 3) the resource layer which defines protocols for publishing, discovering, and accessing individual resources, 4) the collective layer which captures interactions across collections of resources through directory services, and 5) the application layer which comprises user applications built on top of the lower layers and operate in virtual organization environments.
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Inroduction to grid computing by gargi shankar verma
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An Exploration of Grid Computing to be Utilized in Teaching and Research at TU
This document describes building a simple grid computing environment from existing computing resources at Taiz University in Yemen. It outlines:
1) Installing and configuring software like Globus Toolkit, Tomcat, and OGCE portal on three machines to set up basic grid services like a certificate authority server, MyProxy server, and portal server.
2) Configuring the hardware nodes, installing the portal server, setting up the certificate authority server, and MyProxy server.
3) Testing basic grid services like credential delegation to MyProxy, retrieval from MyProxy, and GridFTP file transfers.
The results indicate the proposed grid model is promising for teaching and research at Taiz University and could serve as a
International Journal of Grid Computing & Applications (IJGCA)
Service-oriented computing is a popular design methodology for large scale business computing systems. Grid computing enables the sharing of distributed computing and data resources such as processing, networking and storage capacity to create a cohesive resource environment for executing distributed applications in service-oriented computing. Grid computing represents more business-oriented orchestration of pretty homogeneous and powerful distributed computing resources to optimize the execution of time consuming process as well. Grid computing have received a significant and sustained research interest in terms of designing and deploying large scale and high performance computational in e-Science and businesses. The objective of the journal is to serve as both the premier venue for presenting foremost research results in the area and as a forum for introducing and exploring new concepts.
Application of cloud computing based on e learning teaching tool
Abstract
The demand for cloud computing has pressured the development of new market offerings, representing various cloud services and
delivery models. These models significantly expand the range of available options and tasks.Cloud computing allows changes in
businesses and organizations with more choices regarding how to run infrastructures, save costs, and delegate liabilities to thirdparty
providers. It has become an integral part of technology and business models, and has forced businesses to adapt to new
technology strategies .Now Cloud computing introduces efficient scale mechanism which let the construction of E-Learning
systems to be entrusted to all suppliers and provide a new mode for E-Learning.
Keywords : Cloud Computing, E-Learning, CloudE-Learning
Seminar report on microsoft azure
ABSTRACT
Software industry is heading towards centralized computing. Due to this trend data and programs are being taken away from traditional desktop PCs and placed in Compute clouds instead. Compute clouds are enormous server farms packed with computing power and storage space accessible through the Internet.
Instead of having to manage one’s own infrastructure to run applications, server time and storage space can be bought from an external service provider. From the customers point of view the benefit behind this idea is to be able to dynamically adjust computing power up or down to meet the demand for that power at a particular moment. This kind of flexibility not only ensures that no costs are incurred by excess processing capacity, but also enables hardware infrastructure to scale up with business growth. Because of growing interest in taking advantage of cloud computing a number of service providers are working on providing cloud services. Amazon, Salerforce.com and Google are examples of firms that already have working solutions on the market. Recently also Microsoft released a preview version of its cloud platform called the Azure. Early adopters can test the platform and development tools free of charge.
The main purpose of this paper is to shed light on the internals of Microsoft’s Azure platform. In addition to examining how Azure platform works.
The benefits of Azure platform are explored. The most important benefit in
Microsoft’s solution is that it resembles existing Windows environment a lot. Developers can use the same application programming interfaces (APIs) and development tools they are already used to. The second benefit is that migrating applications to cloud is easy. This partially stems from the fact that Azure’s services can be exploited by an application whether it is run locally or in the cloud.
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原版制作(Humboldt毕业证书)柏林大学毕业证学位证一模一样
原件一模一样【微信：bwp0011】《(Humboldt毕业证书)柏林大学毕业证学位证》【微信：bwp0011】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问微bwp0011
【主营项目】
一.毕业证【微bwp0011】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
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◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微bwp0011】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
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一比一原版(爱大毕业证书)爱荷华大学毕业证如何办理
原版一模一样【微信：741003700 】【(爱大毕业证书)爱荷华大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(爱大毕业证书)爱荷华大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
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Cs6703 grid and cloud computing Study material
- 1. We are providing 25% of Discount for Students to purchase the book.(Rs.180/-) Contact: 8012582176
- 2. GRID AND CLOUD COMPUTING (For Common to B.E CSE & IT Branches) AS PER THE LATEST SYLLABUS OF ANNA UNIVERSITY, CHENNAI (Regulation 2013) Mr. D.KALEESWARAN Assistant Professor Dept. of Computer Science and Engineering St. Michael College of Engineering & Technology Sivagangai, Tamil Nadu Dr. R. KAVITHA Professor Dept. of Computer Science and Engineering Velammal College of Engineering & Technology Madurai, Tamil Nadu.
- 3. GRID AND CLOUD COMPUTING By, Mr. D. Kaleeswaran M.E., (Ph.D) & Dr. R. Kavitha, M.E., Ph.D. First Edition : April 2016 Copy Right © Exclusive by the publisher with Author All Right Reserved No part of the publication to reproduced stored in a retrieval system or transmitted in any form or by any means, mechanical, photocopying, recording or otherwise, without the prior written permission of the author. Price Rs:- 280/- Published by Copies can be had form: Publication Name: Eeswar Publishers Distributors Pvt Ltd Mail id: eeswarpublicationmdu@gmail.com
- 4. ANNA UNIVERSITY CHENNAI – REGULATION 2013 GRID AND CLOUD COMPUTING UNIT I INTRODUCTION Evolution of Distributed computing: Scalable computing over the Internet – Technologies for network based systems – clusters of cooperative computers - Grid computing Infrastructures – cloud computing - service oriented architecture – Introduction to Grid Architecture and standards – Elements of Grid – Overview of Grid Architecture. UNIT II GRID SERVICES Introduction to Open Grid Services Architecture (OGSA) – Motivation – Functionality Requirements – Practical & Detailed view of OGSA/OGSI – Data intensive grid service models – OGSA services. UNIT III VIRTUALIZATION Cloud deployment models: public, private, hybrid, community – Categories of cloud computing: Everything as a service: Infrastructure, platform, software - Pros and Cons of cloud computing – Implementation levels of virtualization – virtualization structure – virtualization of CPU, Memory and I/O devices – virtual clusters and Resource Management – Virtualization for data center automation. UNIT IV PROGRAMMING MODEL Open source grid middleware packages – Globus Toolkit (GT4) Architecture , Configuration – Usage of Globus – Main components and Programming model - Introduction to Hadoop Framework - Mapreduce, Input splitting, map and reduce functions, specifying input and output parameters, configuring and running a job – Design of Hadoop file system, HDFS concepts, command line and java interface, dataflow of File read & File write. UNIT V SECURITY Trust models for Grid security environment – Authentication and Authorization methods – Grid security infrastructure – Cloud Infrastructure security: network, host and application level – aspects of data security, provider data and its security, Identity and access management architecture, IAM practices in the cloud, SaaS, PaaS, IaaS availability in the cloud, Key privacy issues in the cloud. SYLLABUS
- 5. 1 INTRODUCTION 1.1 Evolution of Distributed computing 1 1.2 Scalable computing over the Internet 3 1.3 Technologies for network based systems 7 1.4 Clusters of cooperative computers 15 1.5 Grid computing Infrastructures 19 1.6 Cloud computing 22 1.7 Service oriented architecture 26 1.8 Introduction to Grid Architecture and standards 29 1.9 Elements of Grid 33 1.10 Overview of Grid Architecture. 33 2 GRID SERVICES 2.1 Introduction to Open Grid Services Architecture (OGSA) 42 2.2 Motivation 43 2.3 Functionality Requirements 44 2.4 Practical & Detailed view of OGSA/OGSI 46 2.5 Data intensive grid service models 50 2.6 OGSA services. 53 3 VIRTUALIZATION 3.1 Cloud deployment models: public, private, hybrid, community 59 3.2 Categories of cloud computing 62 3.3 Everything as a service: Infrastructure, platform, software 63 3.4 Pros and Cons of cloud computing 68 CONTENTS
- 6. 3.5 Implementation levels of virtualization and Virtualization structure 69 3.6 Virtualization of CPU, Memory and I/O devices 75 3.7 Virtual clusters and Resource Management 78 3.8 Virtualization for data center automation. 83 4 PROGRAMMING MODEL 4.1 Open source grid middleware and packages 90 4.2 Globus Toolkit (GT4) Architecture , Configuration 93 4.3 Usage of Globus 96 4.4 Main components and Programming model 98 4.5 Introduction to Hadoop Framework 100 4.6 Mapreduce 104 4.7 Input splitting, map and reduce functions 106 4.8 Specifying input and output parameters 108 4.9 Configuring and running a job and Design of Hadoop file system 109 4.10 HDFS concepts and Command line and java interface , Dataflow of File read & File write 115 5 SECURITY 5.1 Trust models for Grid security environment 121 5.2 Authentication and Authorization methods 124 5.3 Grid security infrastructure 126 5.4 Cloud Infrastructure security: network, host and application level 129 5.5 Aspects of data security 131 5.6 Provider data and its security 131 5.7 Identity and access management architecture , IAM practices in the cloud 132 5.8 SaaS, PaaS, IaaS availability in the cloud 135 5.9 Key privacy issues in the cloud 136