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.
Parallel computing is computing architecture paradigm ., in which processing required to solve a problem is done in more than one processor parallel way.
Virtualization allows multiple operating systems and applications to run on a single computer using a hypervisor. It is considered green computing because it decreases energy usage and toxic waste by reducing the number of physical devices needed. There are several types of virtualization including server, application, network, storage, and desktop virtualization. Server virtualization specifically allows many virtual servers to run on a single physical server, decreasing energy usage and saving floor space. Overall, virtualization improves hardware utilization and flexibility while lowering costs and environmental impact through reduced resource consumption.
1) Parallel computing involves using multiple processors simultaneously to solve computational problems. It breaks problems into discrete parts that can be solved concurrently.
2) There are four types of parallel processor organizations: single instruction single data (SISD), single instruction multiple data (SIMD), multiple instruction single data (MISD), and multiple instruction multiple data (MIMD).
3) Historically parallel computing was used for scientific modeling but now also powers commercial applications involving large data processing like web search engines and databases.
Clustering: Large Databases in data miningZHAO Sam
The document discusses different approaches for clustering large databases, including divide-and-conquer, incremental, and parallel clustering. It describes three major scalable clustering algorithms: BIRCH, which incrementally clusters incoming records and organizes clusters in a tree structure; CURE, which uses a divide-and-conquer approach to partition data and cluster subsets independently; and DBSCAN, a density-based algorithm that groups together densely populated areas of points.
Information privacy and data mining
The document discusses information privacy and data mining. It defines information privacy as an individual's ability to control how information about them is shared. It outlines the basic OECD principles for protecting information privacy, including collection limitation, purpose specification, use limitation, security safeguards, and accountability. It describes common uses of data mining like fraud prevention but also potential misuses that can violate privacy. The document also discusses the primary aims of data mining applications and five pitfalls like unintentional mistakes, intentional abuse, and mission creep.
This document discusses various graph algorithms techniques that can be implemented in MapReduce frameworks:
1. Local aggregation reduces the amount of intermediate data using combiners and in-mapper aggregation to minimize network traffic.
2. The "pairs" and "stripes" patterns refer to different ways of organizing intermediate co-occurrence data from document analysis tasks. Stripes using associative arrays can better utilize combiners.
3. Order inversion allows relative frequencies to be computed in one MapReduce job by signaling marginal totals ahead of detailed records.
Parallel computing is computing architecture paradigm ., in which processing required to solve a problem is done in more than one processor parallel way.
Virtualization allows multiple operating systems and applications to run on a single computer using a hypervisor. It is considered green computing because it decreases energy usage and toxic waste by reducing the number of physical devices needed. There are several types of virtualization including server, application, network, storage, and desktop virtualization. Server virtualization specifically allows many virtual servers to run on a single physical server, decreasing energy usage and saving floor space. Overall, virtualization improves hardware utilization and flexibility while lowering costs and environmental impact through reduced resource consumption.
1) Parallel computing involves using multiple processors simultaneously to solve computational problems. It breaks problems into discrete parts that can be solved concurrently.
2) There are four types of parallel processor organizations: single instruction single data (SISD), single instruction multiple data (SIMD), multiple instruction single data (MISD), and multiple instruction multiple data (MIMD).
3) Historically parallel computing was used for scientific modeling but now also powers commercial applications involving large data processing like web search engines and databases.
Clustering: Large Databases in data miningZHAO Sam
The document discusses different approaches for clustering large databases, including divide-and-conquer, incremental, and parallel clustering. It describes three major scalable clustering algorithms: BIRCH, which incrementally clusters incoming records and organizes clusters in a tree structure; CURE, which uses a divide-and-conquer approach to partition data and cluster subsets independently; and DBSCAN, a density-based algorithm that groups together densely populated areas of points.
Information privacy and data mining
The document discusses information privacy and data mining. It defines information privacy as an individual's ability to control how information about them is shared. It outlines the basic OECD principles for protecting information privacy, including collection limitation, purpose specification, use limitation, security safeguards, and accountability. It describes common uses of data mining like fraud prevention but also potential misuses that can violate privacy. The document also discusses the primary aims of data mining applications and five pitfalls like unintentional mistakes, intentional abuse, and mission creep.
This document discusses various graph algorithms techniques that can be implemented in MapReduce frameworks:
1. Local aggregation reduces the amount of intermediate data using combiners and in-mapper aggregation to minimize network traffic.
2. The "pairs" and "stripes" patterns refer to different ways of organizing intermediate co-occurrence data from document analysis tasks. Stripes using associative arrays can better utilize combiners.
3. Order inversion allows relative frequencies to be computed in one MapReduce job by signaling marginal totals ahead of detailed records.
This document describes a graduate course on computational complexity taught by Antonis Antonopoulos. It includes the course syllabus, which covers topics like Turing machines, complexity classes, randomized computation, interactive proofs, and derandomization of complexity classes. It also provides recommended textbooks and lecture notes. The document lists some of the major complexity classes like P, NP, BPP, and includes definitions of time-constructible and space-constructible functions, which are used to formally define complexity classes. It also discusses the relationships between different complexity classes and proves theorems like the time hierarchy theorem.
Cloud computing & energy efficiency using cloud to decrease the energy use in...Puru Agrawal
Cloud can be used to decrease the energy use in large companies. This presentation deals with a model which explains as how cloud can be used to decrease the energy uses. This is a field related to green computing and minimum use of energy resources.
Please contact me to download this pres.A comprehensive presentation on the field of Parallel Computing.It's applications are only growing exponentially day by days.A useful seminar covering basics,its classification and implementation thoroughly.
Visit www.ameyawaghmare.wordpress.com for more info
An explicitly parallel program must specify concurrency and interaction between concurrent subtasks.
The former is sometimes also referred to as the control structure and the latter as the communication model.
The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and an ecosystem of related projects like Hive, HBase, Pig and Zookeeper that provide additional functions. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
Lecture 2 role of algorithms in computingjayavignesh86
This document discusses algorithms and their role in computing. It defines an algorithm as a set of steps to solve a problem on a machine in a finite amount of time. Algorithms must be unambiguous, have defined inputs and outputs, and terminate. The document discusses designing algorithms, proving their correctness, and analyzing their performance and complexity. It provides examples of algorithm problems like sorting, searching, and graphs. The goal of analyzing algorithms is to evaluate and compare their performance as the problem size increases.
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.
The document discusses the CAP theorem which states that it is impossible for a distributed computer system to simultaneously provide consistency, availability, and partition tolerance. It defines these terms and explores how different systems address the tradeoffs. Consistency means all nodes see the same data at the same time. Availability means every request results in a response. Partition tolerance means the system continues operating despite network failures. The CAP theorem says a system can only choose two of these properties. The document discusses how different types of systems, like CP and AP systems, handle partitions and trade off consistency and availability. It also notes the CAP theorem is more nuanced in reality with choices made at fine granularity within systems.
This document provides an overview of the introductory lecture to the BS in Data Science program. It discusses key topics that were covered in the lecture, including recommended books and chapters to be covered. It provides a brief introduction to key terminologies in data science, such as different data types, scales of measurement, and basic concepts. It also discusses the current landscape of data science, including the difference between roles of data scientists in academia versus industry.
The document discusses parallel algorithms and their analysis. It introduces a simple parallel algorithm for adding n numbers using log n steps. Parallel algorithms are analyzed based on their time complexity, processor complexity, and work complexity. For adding n numbers in parallel, the time complexity is O(log n), processor complexity is O(n), and work complexity is O(n log n). The document also discusses models of parallel computation like PRAM and designs of parallel architectures like meshes and hypercubes.
This document discusses machine learning concepts including supervised vs. unsupervised learning, clustering algorithms, and specific clustering methods like k-means and k-nearest neighbors. It provides examples of how clustering can be used for applications such as market segmentation and astronomical data analysis. Key clustering algorithms covered are hierarchy methods, partitioning methods, k-means which groups data by assigning objects to the closest cluster center, and k-nearest neighbors which classifies new data based on its closest training examples.
Distributed systems allow independent computers to appear as a single coherent system by connecting them through a middleware layer. They provide advantages like increased reliability, scalability, and sharing of resources. Key goals of distributed systems include resource sharing, openness, transparency, and concurrency. Common types are distributed computing systems, distributed information systems, and distributed pervasive systems.
This document discusses the Green Grid framework and concepts related to green computing such as virtualization, telecommuting, and data centers. It covers virtualization of IT systems and how virtualization can promote green computing by improving server utilization rates and eliminating planned downtime. The document also discusses the role of electric utilities, power management at different levels including hardware, firmware, operating system, virtualization and data center levels, and defines key terms like hypervisor, virtual machine, and telecommuting.
Parallel computing involves solving computational problems simultaneously using multiple processors. It can save time and money compared to serial computing and allow larger problems to be solved. Parallel programs break problems into discrete parts that can be solved concurrently on different CPUs. Shared memory parallel computers allow all processors to access a global address space, while distributed memory systems require communication between separate processor memories. Hybrid systems combine shared and distributed memory architectures.
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-scale applications, but also challenges around resource sharing and licensing across multiple servers.
Parallel computing and its applicationsBurhan Ahmed
Parallel computing is a type of computing architecture in which several processors execute or process an application or computation simultaneously. Parallel computing helps in performing large computations by dividing the workload between more than one processor, all of which work through the computation at the same time. Most supercomputers employ parallel computing principles to operate. Parallel computing is also known as parallel processing.
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A computer cluster is a group of tightly coupled computers that work together like a single computer (Paragraph 1). Clusters are commonly connected through fast local area networks and have evolved to support applications ranging from e-commerce to databases (Paragraph 2). A cluster uses interconnected standalone computers that cooperate to create the illusion of a single computer with parallel processing capabilities. Clusters provide benefits like reduced costs, high availability if components fail, and scalability by allowing addition of nodes (Paragraphs 3-4). The history of clusters began in the 1970s and operating systems like Linux are now commonly used (Paragraph 5). Clusters have architectures with interconnected nodes that appear as a single system to users (Paragraph 6). Clusters are categorized based on availability
Asymptotic analysis of parallel programsSumita Das
The document compares four algorithms for sorting a list of numbers in parallel. It presents a table showing the number of processing elements, parallel runtime, speedup, efficiency, and processing element-time product for each algorithm. It analyzes that algorithm A1 has the lowest parallel runtime and is the best if the metric is speed, while algorithms A2 and A4 have the highest efficiency and are the best if the metric is efficiency or cost. The document emphasizes the importance of identifying the objectives of the analysis and using the appropriate metrics.
This document discusses real-time operating systems for embedded systems. It begins by defining embedded systems as specialized computer systems designed to perform dedicated functions with real-time constraints. It then explains that real-time embedded systems must manage time-critical processes. Common real-time operating system (RTOS) functions include task management, inter-task communication, dynamic memory allocation, timers, and device I/O. RTOSs allow embedded systems to schedule tasks, communicate between processes, and interface with hardware in a timely manner. Examples of widely-used RTOSes are given.
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
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.
Grid Computing - Collection of computer resources from multiple locationsDibyadip Das
Grid computing is the collection of computer resources from multiple locations to reach a common goal. The grid can be thought of as a distributed system with non-interactive workloads that involve a large number of files.
This document describes a graduate course on computational complexity taught by Antonis Antonopoulos. It includes the course syllabus, which covers topics like Turing machines, complexity classes, randomized computation, interactive proofs, and derandomization of complexity classes. It also provides recommended textbooks and lecture notes. The document lists some of the major complexity classes like P, NP, BPP, and includes definitions of time-constructible and space-constructible functions, which are used to formally define complexity classes. It also discusses the relationships between different complexity classes and proves theorems like the time hierarchy theorem.
Cloud computing & energy efficiency using cloud to decrease the energy use in...Puru Agrawal
Cloud can be used to decrease the energy use in large companies. This presentation deals with a model which explains as how cloud can be used to decrease the energy uses. This is a field related to green computing and minimum use of energy resources.
Please contact me to download this pres.A comprehensive presentation on the field of Parallel Computing.It's applications are only growing exponentially day by days.A useful seminar covering basics,its classification and implementation thoroughly.
Visit www.ameyawaghmare.wordpress.com for more info
An explicitly parallel program must specify concurrency and interaction between concurrent subtasks.
The former is sometimes also referred to as the control structure and the latter as the communication model.
The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and an ecosystem of related projects like Hive, HBase, Pig and Zookeeper that provide additional functions. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
Lecture 2 role of algorithms in computingjayavignesh86
This document discusses algorithms and their role in computing. It defines an algorithm as a set of steps to solve a problem on a machine in a finite amount of time. Algorithms must be unambiguous, have defined inputs and outputs, and terminate. The document discusses designing algorithms, proving their correctness, and analyzing their performance and complexity. It provides examples of algorithm problems like sorting, searching, and graphs. The goal of analyzing algorithms is to evaluate and compare their performance as the problem size increases.
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.
The document discusses the CAP theorem which states that it is impossible for a distributed computer system to simultaneously provide consistency, availability, and partition tolerance. It defines these terms and explores how different systems address the tradeoffs. Consistency means all nodes see the same data at the same time. Availability means every request results in a response. Partition tolerance means the system continues operating despite network failures. The CAP theorem says a system can only choose two of these properties. The document discusses how different types of systems, like CP and AP systems, handle partitions and trade off consistency and availability. It also notes the CAP theorem is more nuanced in reality with choices made at fine granularity within systems.
This document provides an overview of the introductory lecture to the BS in Data Science program. It discusses key topics that were covered in the lecture, including recommended books and chapters to be covered. It provides a brief introduction to key terminologies in data science, such as different data types, scales of measurement, and basic concepts. It also discusses the current landscape of data science, including the difference between roles of data scientists in academia versus industry.
The document discusses parallel algorithms and their analysis. It introduces a simple parallel algorithm for adding n numbers using log n steps. Parallel algorithms are analyzed based on their time complexity, processor complexity, and work complexity. For adding n numbers in parallel, the time complexity is O(log n), processor complexity is O(n), and work complexity is O(n log n). The document also discusses models of parallel computation like PRAM and designs of parallel architectures like meshes and hypercubes.
This document discusses machine learning concepts including supervised vs. unsupervised learning, clustering algorithms, and specific clustering methods like k-means and k-nearest neighbors. It provides examples of how clustering can be used for applications such as market segmentation and astronomical data analysis. Key clustering algorithms covered are hierarchy methods, partitioning methods, k-means which groups data by assigning objects to the closest cluster center, and k-nearest neighbors which classifies new data based on its closest training examples.
Distributed systems allow independent computers to appear as a single coherent system by connecting them through a middleware layer. They provide advantages like increased reliability, scalability, and sharing of resources. Key goals of distributed systems include resource sharing, openness, transparency, and concurrency. Common types are distributed computing systems, distributed information systems, and distributed pervasive systems.
This document discusses the Green Grid framework and concepts related to green computing such as virtualization, telecommuting, and data centers. It covers virtualization of IT systems and how virtualization can promote green computing by improving server utilization rates and eliminating planned downtime. The document also discusses the role of electric utilities, power management at different levels including hardware, firmware, operating system, virtualization and data center levels, and defines key terms like hypervisor, virtual machine, and telecommuting.
Parallel computing involves solving computational problems simultaneously using multiple processors. It can save time and money compared to serial computing and allow larger problems to be solved. Parallel programs break problems into discrete parts that can be solved concurrently on different CPUs. Shared memory parallel computers allow all processors to access a global address space, while distributed memory systems require communication between separate processor memories. Hybrid systems combine shared and distributed memory architectures.
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-scale applications, but also challenges around resource sharing and licensing across multiple servers.
Parallel computing and its applicationsBurhan Ahmed
Parallel computing is a type of computing architecture in which several processors execute or process an application or computation simultaneously. Parallel computing helps in performing large computations by dividing the workload between more than one processor, all of which work through the computation at the same time. Most supercomputers employ parallel computing principles to operate. Parallel computing is also known as parallel processing.
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Watch my videos on snack here: --> --> http://sck.io/x-B1f0Iy
@ Kindly Follow my Instagram Page to discuss about your mental health problems-
-----> https://instagram.com/mentality_streak?utm_medium=copy_link
@ Appreciate my work:
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Thank-you !
A computer cluster is a group of tightly coupled computers that work together like a single computer (Paragraph 1). Clusters are commonly connected through fast local area networks and have evolved to support applications ranging from e-commerce to databases (Paragraph 2). A cluster uses interconnected standalone computers that cooperate to create the illusion of a single computer with parallel processing capabilities. Clusters provide benefits like reduced costs, high availability if components fail, and scalability by allowing addition of nodes (Paragraphs 3-4). The history of clusters began in the 1970s and operating systems like Linux are now commonly used (Paragraph 5). Clusters have architectures with interconnected nodes that appear as a single system to users (Paragraph 6). Clusters are categorized based on availability
Asymptotic analysis of parallel programsSumita Das
The document compares four algorithms for sorting a list of numbers in parallel. It presents a table showing the number of processing elements, parallel runtime, speedup, efficiency, and processing element-time product for each algorithm. It analyzes that algorithm A1 has the lowest parallel runtime and is the best if the metric is speed, while algorithms A2 and A4 have the highest efficiency and are the best if the metric is efficiency or cost. The document emphasizes the importance of identifying the objectives of the analysis and using the appropriate metrics.
This document discusses real-time operating systems for embedded systems. It begins by defining embedded systems as specialized computer systems designed to perform dedicated functions with real-time constraints. It then explains that real-time embedded systems must manage time-critical processes. Common real-time operating system (RTOS) functions include task management, inter-task communication, dynamic memory allocation, timers, and device I/O. RTOSs allow embedded systems to schedule tasks, communicate between processes, and interface with hardware in a timely manner. Examples of widely-used RTOSes are given.
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
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.
Grid Computing - Collection of computer resources from multiple locationsDibyadip Das
Grid computing is the collection of computer resources from multiple locations to reach a common goal. The grid can be thought of as a distributed system with non-interactive workloads that involve a large number of files.
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.
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.
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.
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.
Grid computing involves applying the resources of many computers in a network to solve large problems simultaneously. It shares idle computing resources over an intranet to distribute large files efficiently. Security measures like authentication are needed. Resources are managed through remote job submission. Major business uses include life sciences, financial modeling, education, engineering, and government collaboration. The proposed intranet grid would make downloading multiple files very fast while maintaining security.
UnaCloud: an opportunistic cloud computing Infrastructure as a Service (IaaS) model implementation, which provides at lower cost, fundamental computing resources (processing, storage and networking) to run arbitrary software, including operating systems and applications.
Grid and Cloud Computing Lecture-2a.pptxDrAdeelAkram2
The document discusses grid architecture and tools. It covers the hourglass model of grid architecture, which focuses on core services to enable diverse solutions. It also discusses the layered grid architecture with four layers - fabric, connectivity, collective, and application. Simulation tools for modeling grid environments like GridSim are presented. The document then discusses clouds and defines cloud computing. Key aspects of clouds like scalability, virtualization, and on-demand services are covered. It compares clouds to grids and other paradigms. Finally, it introduces service-oriented architecture and defines the characteristics of services.
Open Cloud Frameworks - Open Standards for the Cloud Communitybefreax
A presentation about the RESERVOIR project and the need for open standards in the Cloud Community. This is demonstrated by the example of the Open Cloud Computing Interface. More information and a transcript here: http://85.114.139.198/nohuddleoffense/?p=369
Abstract:-
This paper is based on the study of grid computing and cloud computing technology. These two technologies are related with geographically defined network standards. The main aspect of this paper is deep learning of latest technology and trends in the field of networking.
Keywords:-Technology,Cloud Computing,Grid Computing
Comprehensive Study on Deployment Models and Service Models in Cloud Computing.IRJET Journal
This document provides a comprehensive study of deployment models and service models in cloud computing. It discusses the key cloud computing models including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). It also examines the major cloud deployment models such as public cloud, private cloud, hybrid cloud, and community cloud. The document analyzes the characteristics, benefits, and limitations of each service and deployment model. It provides comparisons of the different models and concludes that cloud computing delivers scalable services but also faces challenges regarding data security, network security, and lack of user control that need to be addressed.
Detailed Analysis of Security Challenges in the Domain of Hybrid CloudIRJET Journal
This document discusses security challenges in hybrid cloud computing. It begins by defining hybrid clouds and describing their increasing use by major organizations. The main security challenges discussed include ensuring compliance, protecting data privacy across private and public clouds, managing risks associated with loss of control over data in public clouds, and protecting against distributed denial-of-service attacks. Several security solutions for hybrid clouds are then outlined, such as using virtual private clouds, OpenVPN, OpenCitrix, CloudKnox, and Guardicore Centra to securely connect private infrastructure to public clouds.
The Open Grid Forum (OGF) is a leading standards development organization for cloud, grid, and distributed computing. OGF has developed many relevant standards over its history dating back to 2001. These standards include specifications for identity management, job submission, data transfer, service agreements, and cloud computing interfaces. OGF actively collaborates with other standards bodies and its standards see widespread adoption in both research and industry implementations of distributed computing infrastructure.
11th International Conference on Cloud Computing: Services and Architecture (...ijccsa
11th International Conference on Cloud Computing: Services and Architecture (CLOUD 2022) helps enterprises transform business and technology. Companies have begun to look for solutions that would help reduce their infrastructures costs and improve profitability. Cloud computing is becoming a foundation for benefits well beyond IT cost savings. Yet, many business leaders are concerned about cloud security, privacy, availability and data protection. To discuss and address these issues, we are inviting researchers who focus on cloud computing to shed more light on this emerging field. This conference aims to bring together researchers and practitioners in all security aspects of cloud-centric and outsourced computing, including (but not limited to):
This document provides an overview of cloud computing. It defines cloud computing as storing and accessing data and computing services over the Internet. It then describes various cloud models including public, private, hybrid and community clouds. It also discusses the different cloud service models of Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). The document outlines the advantages of cloud computing as well as characteristics of the different cloud models and services.
Stefan Freitag presented on the D-Grid infrastructure in Germany. D-Grid supported multiple middleware platforms like gLite, UNICORE, and Globus Toolkit across over 30,000 CPU cores and 5 petabytes of storage. A reference system was created to help resources install software stacks consistently. A cloud computing prototype was also developed using OpenNebula to utilize idle resources and attract new users. Lessons learned included a lack of adoption of the reference system and legal issues around dual-use technologies and liability in virtual organizations. Future challenges include merging with the German NGI initiative to avoid duplication and better integrating services.
This document discusses requirements and technology issues for data centers of the future. It outlines a vision for modular, proximity-based data centers with mixed compute environments, redundancy, workload migration capabilities, and automation/orchestration. Current issues include a lack of standardization in orchestration/integration, limitations on linear scaling, and "flatness" challenges from multi-tiered network designs. The data center of the future aims to address these through software-defined networking, computing, and storage orchestrated in a secure, flat design. Companies that implement these technologies gain competitive advantages around utilization and rapid expansion.
Grid computing allows for large-scale, collaborative computing across organizational boundaries. While some current grid solutions meet this definition, many only meet a broader definition by harnessing clusters of dedicated servers within an organization. True grids that can manage resources across organizations still require custom building. In the future, trends toward outsourcing and commodity IT, coupled with technologies like grids and utility computing, will likely move IT infrastructure management out of enterprises and into external utility-style services.
Call for Paper - 3rd International Conference on Machine learning and Cloud C...ijmnct
3rd International Conference on Machine learning and Cloud Computing (MLCL 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Cloud computing. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
This presentation lecture was delivered in HITEC University, Pakistan. This is my view of the cloud and next generation computing infrastructure supported by the cloud infrastructure.
This document compares and contrasts cloud computing and grid computing. Grid computing refers to cooperation between multiple computers and servers to boost computational power, with a focus on high-capacity CPU tasks. Cloud computing delivers on-demand access to shared computing resources like networks, servers, storage and applications via the internet. Key differences include grid computing having a lower level of abstraction and scalability compared to cloud computing. Cloud computing also has stronger fault tolerance, is more widely accessible via the internet, and offers real-time services through its utility-based pricing model.
Cloud computing promises to offer great opportunities for research groups; however when researchers want to execute applications in cloud infrastructures many complex processes must be accomplished. In this presentation we present the e-Clouds project which will allow researchers to easily execute many applications on public Infrastructure as a Service (IaaS) solutions. Designed for being a Software as a Service (SaaS) marketplace for scientific applications, e-Clouds allows researchers to submit jobs which are transparently executed on public IaaS platforms, such as Amazon Web Services (AWS). e-Clouds manages the on-demand provisioning and configuration of computing instances, storage, applications, schedulers, jobs, and data. The architectural design and how a first application has been supported on e-Clouds are presented. e-Clouds will allow researchers to easily share and execute applications in the cloud at low TCO (Total Cost of Ownership) and without the complexities associated with details of IT configurations and management. e-Clouds provides new opportunities for research groups with low or none budget for dedicated cluster or grid solutions, providing on-demand access to ready-to-use applications and accelerating the result generation of e-Science projects.
"DECIDE. Towards supporting the extended DevOps Approach through multi-cloud ...DECIDEH2020
This document discusses supporting multi-cloud applications through an extended DevOps approach. It motivates the need for DevOps and multi-cloud applications, outlines key research challenges, and presents an approach called ARCHITECT OPTIMUS ACSmI. The approach aims to provide tools and mechanisms to support the software development and operations lifecycles of multi-cloud apps through continuous delivery, pre-deployment, architecting, adaptation and development. It involves patterns for multi-cloud app design, tools for optimization and pre-deployment, and a registry for discovering cloud services.
3rd International Conference on Machine learning and Cloud Computing (MLCL 2022)ijccsa
3rd International Conference on Machine learning and Cloud Computing (MLCL 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Cloud computing. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
3rd International Conference on Machine learning and Cloud Computing (MLCL 2022)ijujournal
3rd International Conference on Machine learning and Cloud Computing (MLCL 2022)
will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of on Machine Learning & Cloud computing. The aim of the
conference is to provide a platform to the researchers and practitioners from both academia as
well as industry to meet and share cutting-edge development in the field.
2. Outline
➔
Introduction to Grid Computing
➔
Grid Construction
➔
Grid Frameworks
➔
Globus Toolkit
➔
Gridbus Toolkit
➔
UNICORE
➔
Legion
30 September 2012 Grid Computing Frameworks 2
3. Outline
➔
Comparison
➔
Other emerging frameworks
➔
Conclusion
30 September 2012 Grid Computing Frameworks 3
4. Introduction to Grid Computing (I)
End of 1998 the concept of "Grid computing" was introduced in the monograph "The
Grid: Blueprint for a New Computing Infrastructure" by I. Foster and C. Kesselman.
30 September 2012 Grid Computing Frameworks 4
5. Introduction to Grid Computing (II)
➔
The notion of grid computing:
➔
The term grid is chosen as an analogy to a power grid !
➔
Grid computing is a special type of parallel computing
➔
How it differs from supercomputing?
➔
Few essential concepts related to grid computing:
➔
Utility computing
➔
Volunteer computing
➔
CPU scavenging
➔
Loosely coupled system
➔
Virtual supercomputers
30 September 2012 Grid Computing Frameworks 5
6. Introduction to Grid Computing (III)
➔
grids typically share at least some of the following characteristics:
➔
They are numerous.
➔
They are owned and managed by different, potentially mutually-distrustful
organizations and individuals.
➔
They are potentially faulty.
➔
They have different security requirements and policies.
➔
They are heterogeneous, e.g., they have different CPU architectures, run different
operating systems, and have different amounts of memory and disk.
➔
They are connected by heterogeneous, multi-level networks.
➔
They have different resource management policies.
➔
They are likely to be geographically-separated (on a campus, in an enterprise, on a
continent).
30 September 2012 Grid Computing Frameworks 6
7. Introduction to Grid Computing (IV)
Compute Grids Data Grids
Access Grids Knowledge Grids
Bio Grids Commodity Grids
Campus Grid Tera Grids
Science Grids Sensor Grids
Cluster Grids
30 September 2012 Grid Computing Frameworks 7
9. Grid Construction (I)
➔
General Principles (four main aspects characterize a grid)
➔
Multiple administrative domain and autonomy
➔
Heterogeneity
➔
Scalability
➔
Dynamicity or adaptability
30 September 2012 Grid Computing Frameworks 9
10. Grid Construction (II)
➔
The steps necessary to realize a grid
➔
Integration of individual software and hardware components
➔
Deployment of
➔
Low level middleware
➔
User level middleware
➔
Development and optimization of distributed
applications.
30 September 2012 Grid Computing Frameworks 10
11. Grid Construction (III)
➔
A Layered Grid Architecture
30 September 2012 Grid Computing Frameworks 11
13. Grid Frameworks (I)
➔
A software framework (or middleware)
➔
contains executables or tools
➔
provides inversion of control
➔
has a default behavior
➔
extensibility
➔
non-modifiable framework code
30 September 2012 Grid Computing Frameworks 13
14. Grid Frameworks (II)
●
Grid framework (or middleware), is a software stack that facilitates
●
writing grid applications
●
and manages the underlying grid infrastructure
➔
Grid frameworks can be categorized by the grid layers.
➔
Core middleware and toolkit:
➔
Globus, Gridbus (Alchemi, GridSim), UNICORE, Legion, GridGain, gLite
➔
User-level middleware and toolkit:
➔
SAGA, MetaMPI, Cactus, GrADS, Gridport, WebFlow, XtremeWeb
30 September 2012 Grid Computing Frameworks 14
15. Globus Toolkit (I)
➔
Open source software toolkit for developing Grid applications
➔
The de facto standard for open source grid computing infrastructure
➔
Supported by industry leaders such as IBM, Intel, HP with others (The Globus
Consortium)
➔
R&D project conducted by the “Globus Alliance”
➔
Work on the toolkit first began in 1996. Historically, the Globus Toolkit was used
widely by three groups of people
➔
Grid builders
➔
Application developers
➔
Application framework developers
30 September 2012 Grid Computing Frameworks 15
16. Globus Toolkit (II)
➔
Provides three main groups of services accessible through a security layer :
1. Resource Management
2. Data Management
3. Information Services
30 September 2012 Grid Computing Frameworks 16
17. Globus Toolkit (III)
Impact: Globus Toolkit have enabled many exciting new scientific and business
grids. (Source: http://www.globus.org/alliance/impact/)
30 September 2012 Grid Computing Frameworks 17
18. Gridbus Toolkit (I)
➔
Originated from Gridbus (GRIDcomputing andBUSiness) project.
➔
Toolkit for Service Oriented Grid and Utility Computing
➔
Supports development of grid infrastructure for eScience and eBusiness
applications.
➔
Uses economic models (supply and demand) for efficient management
of shared resources.
➔
Promotes commoditization of grid services at various levels:
➔
Raw resources level
➔
Application level
➔
Aggregated service level
30 September 2012 Grid Computing Frameworks 18
20. Gridbus Toolkit (III)
Impact: Gridbus Toolkit have enabled several exciting scientific and business grids.
(Source: http://www.cloudbus.org/applications.html)
● High Energy Physics and Grid Networks (BelleDataGrid): Melbourne School of Physics
● NeuroGrid: Brain Activity Analysis on the Grid : Osaka University, Japan
● KidneyGrid - Distributed Kidney Models Integration: Melbourne Medical School
● Austronomy: Australian Virtual Observatory
30 September 2012 Grid Computing Frameworks 20
21. UNICORE (I)
➔
UNICORE (Uniform Interface to Computing Resources)
➔
is a ready-to-run Grid system including client and server software
➔
is part of the European Middleware Initiative.
➔
Project was initially funded by the Federal Ministry of Education and Research (BMBF)
➔
UNICORE was started before "Grid computing"
➔
developed by several European partners under the leadership of Jülich Supercomputing
Centre.
➔
platform-independent, based on open standards and technologies such as Web Services
➔
mostly written in Java and is available as open source under BSD license and available at
SourceForge. Current version is UNICORE 6
30 September 2012 Grid Computing Frameworks 21
22. UNICORE (II)
➔
The architecture of UNICORE 6 is three-layered in
➔
client layer,
➔
service layer
➔
and system layer
UNICORE 6 Architecture
30 September 2012 Grid Computing Frameworks 22
23. UNICORE (III)
Impact: UNICORE6 has been the middleware of choice in numerous grids in EU.
(Source: http://www.unicore.eu/community/projects/)
30 September 2012 Grid Computing Frameworks 23
24. Legion (I)
➔
Legion is an object-based metasystem developed at the University of
Virginia.
➔
The software developed under the Legion project has been
commercialized by a spin-off company called Avaki Corporation
➔
The Legion system uses an object-oriented approach. In the Legion system
the following apply
➔
Everything is an object.
➔
Classes manage their instances
➔
Users can define their own classes
➔
The Legion interfaces are described in an Interface Definition Language
(IDL).
30 September 2012 Grid Computing Frameworks 24
25. Legion (II)
➔
Legion core objects support the basic services needed by the metasystem.
➔
Legion objects are independent, active, and capable of communicating
with each other via unordered non-blocking calls.
➔
Some core objects in Legion are:
➔
Host objects: represent processors in Legion.
➔
Vault objects: represent persistent storage.
➔
Context objects: Context objects map context names to Legion object IDs
➔
Binding agents: A binding agent maps object IDs to physical addresses
➔
Implementation object: hides the storage details of object implementations
➔
Class object : is used to define and manage its corresponding Legion object.
Class objects are given system-level responsibility.
30 September 2012 Grid Computing Frameworks 25
30. Other Emerging
Frameworks
➔
Alchemi: a .NET-based grid computing framework. For more
information on Alchemi please visit http://www.alchemi.net/
➔
Gridgain 2.0: Java Grid Computing Framework Released by GridGain
Systems.
➔
Since its release in August 2007 GridGain became the fastest growing Java grid
computing infrastructure with over 10,000 downloads
➔
more than 500 unique projects utilizing it
➔
and deployed in a dozen production systems.
➔
gLite: a framework for building applications tapping into distributed
computing and storage resources across the Internet
➔
used in the CERN LHC experiments and in other scientific domains
➔
adopted by more than 250 computing centres and used by more than 15000
researchers in Europe and around the world.
30 September 2012 Grid Computing Frameworks 30
32. References
➔
[01]: Parvin Asadzadeh et. al. “Global Grids and Software Toolkits: A
Study of Four Grid Middleware Technologies”.
➔
[02]: Mark Baker et. al. “Grids and Grid technologies for wide-area
distributed computing”
30 September 2012 Grid Computing Frameworks 32