Grid
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Grid computing involves using large clusters of computers and large data sets across big networks to tackle computationally and data intensive tasks. It provides seamless access to computing resources distributed globally similar to how the electric grid provides access to power anywhere. The idea of distributed computing has existed for awhile under different names but grid technology now allows it to be realized on a global scale. Major grid projects exist around the world at research institutions and government labs focused on scientific domains like high energy physics, earth systems, and bioinformatics.
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Don't Be Scared. Data Don't Bite. Introduction to Big Data.
This document provides an introduction to big data, including definitions, characteristics, examples, and challenges. It defines big data as high-volume, high-velocity, and high-variety information assets that require new processing methods. Examples discussed include the Sloan Digital Sky Survey, Human Genome Project, and Large Hadron Collider experiments. Challenges of big data include storage, networking, data integrity, and the need for new technologies to handle the volume, velocity and variety. Emerging solutions involve distributed storage, local computation near data, and frameworks like Hadoop and MapReduce.
Big Data
This document provides an introduction to big data, including:
- Big data is characterized by its volume, velocity, and variety, which makes it difficult to process using traditional databases and requires new technologies.
- Technologies like Hadoop, MongoDB, and cloud platforms from Google and Amazon can provide scalable storage and processing of big data.
- Examples of how big data is used include analyzing social media and search data to gain insights, enabling personalized experiences and targeted advertising.
- As data volumes continue growing exponentially from sources like sensors, simulations, and digital media, new tools and approaches are needed to effectively analyze and make sense of "big data".
Big data and cloud computing 9 sep-2017
It describe cloud infrastructure required for big data. It discusses the object storage and virtualization required for big data. Ceph is discussed as example.
Supreet swaran's grid
Grid computing involves sharing computer resources from multiple institutions to work towards a common goal. It originated in the 1990s as a way to link distributed computer resources. Key figures like Ian Foster, Carl Kesselman, and Steve are considered the "fathers of the grid." Grid computing is used by application developers, end users, and system administrators to solve large problems that require massive computational power, such as analyzing petabytes of climate data or screening thousands of chemical compounds per hour. Examples of highly visible grids include the NASA Information Power Grid and the Distributed Terascale Facility Project.
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This document discusses using dedicated servers called data transfer nodes (DTNs) to improve data transfer speeds between research institutions. DTNs are part of a network architecture called a Science DMZ that optimizes high-speed transfers. The document recommends:
- Deploying high-performance DTNs with fast storage in a separate network zone dedicated to research data and services.
- Configuring lossless connections and security policies that don't impede transfers between DTNs and research networks.
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2012 02 pre_hbs_grid_overview_ianstokesrees_pt2
The document discusses scientific research trends that are driving the need for collaborative computing environments like grids. It notes that research is increasingly international, computational, and data-intensive. Managing and sharing large data volumes and running computationally intensive techniques requires high performance computing resources. Web-based tools are also becoming more important for scientific work. The document argues that a collaborative environment is required to support compute and data-intensive science at large scales. It provides overviews of existing grid computing platforms like XSEDE and the Open Science Grid that aim to address these needs.
Lambda Data Grid: An Agile Optical Platform for Grid Computing and Data-inten...
Lambda Data Grid
An Agile Optical Platform for Grid Computing
and Data-intensive Applications
Focus on BIRN Mouse application.
Great vision –
LambdaGrid is one step towards this concepts
LambdaGrid –
A novel service architecture
Lambda as a Scheduled Service
Lambda as a prime resource - like storage and computation
Change our current systems assumptions
Potentially opens new horizon
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This document discusses big data on the cloud. It notes that the internet enables vast amounts of data to be gathered from both humans and sensors. The cloud allows for this large, fast-moving data to be easily collected and analyzed at scale. Popular cloud platforms like Google Cloud and Amazon Web Services provide tools and services for computation, storage, databases, and analysis of big data. While security, data movement limitations, and learning costs present challenges, the cloud is well-suited for big data applications, especially for startups, experimental projects, and when data is already cloud-based. Big data and cloud computing will continue advancing to enable better understanding through large-scale data analysis.
GridComputing-an introduction.ppt
Grid computing is a form of distributed computing that utilizes a network of loosely coupled computers acting together to perform large tasks. It facilitates large-scale resource sharing and coordinated problem solving among organizations. The key aspects of grid computing covered in the document include grid middleware, methods of grid computing like distributed supercomputing and data-intensive computing, grid architectures like layered grid architecture and data grid architecture, and simulation tools for modeling grid systems.
Grid computing assiment
Grid computing combines the resources of multiple computers from different organizations to solve large problems. It works by sharing computing power, memory, storage and other resources across an authorized network. Examples of grid computing include projects that analyze large datasets like genome sequencing or simulate complex systems like climate modeling. Major grid computing projects include those run by scientific organizations like CERN and SETI@home, which analyzes radio telescope data using volunteers' computers. Grid computing infrastructure allows resources to be accessed easily like a utility over the network.
Cloud and Grid Integration OW2 Conference Nov10
This document discusses integrating cloud and grid computing resources. It provides background on CDAC, India's national grid called Garuda, and CDAC's private cloud infrastructure. The key requirements for integration are discussed, such as resource management across networks, execution management, monitoring, and security. The scope of integration is described as utilizing unused grid resources through cloud bursting and optimizing utilization of grid resources by deploying cloud applications on the grid. An integration architecture is proposed using an Integrator module to interface scheduling systems and deploy images and applications across grid and cloud resources.
Colloborative computing
CERN uses a global collaborative computing grid called the Worldwide LHC Computing Grid (WLCG) to process and analyze the huge amounts of data generated by the Large Hadron Collider (LHC). The WLCG links over 170 computing centers in 42 countries to provide the necessary computing power. It processes over 1.5 million computing jobs daily, equivalent to a single computer running for over 600 years. This distributed computing approach allows CERN scientists to solve problems too large for any single machine by pooling resources from around the world.
Week 1 lecture material cc
Cloud computing provides on-demand access to shared computing resources like networks, servers, storage, applications and services. It has essential characteristics like on-demand self-service, broad network access, resource pooling and rapid elasticity. The cloud services models include Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). The deployment models are private cloud, community cloud, public cloud and hybrid cloud.
Grid computing
Grid computing involves connecting geographically distributed computers and resources into a single network to create a virtual supercomputer. Resources may include computers, storage devices, instruments, and data owned by diverse organizations. Users can access these heterogeneous resources through a single account, similar to how an electrical power grid provides power from different sources. Key aspects of grid computing include distributed supercomputing, high-throughput computing, on-demand computing, and data-intensive computing. Major companies involved in developing grid computing include IBM, Intel, and Sun Microsystems. Limitations include the need for standardization and use of command line interfaces or programming.
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Grid
- 2. Distributed Computing Zoo • Grid Computing • Also called High-Performance Computing • Big clusters, Big data, Big pipes, Big centers • Globus backbone, which now includes Services and Gateways • Decentralized control • Cluster Computing • local interconnect between identical cpu’s • Peer-to-Peer (Napster, Kazaa) • Systems for sharing data without centeral server • Internet Computing • Screensaver cycle scavenging • eg SETI@home, Einstein@home, ClimatePrediction.net, etc • Access Grid • A videoconferencing system • Globus • A popular software package to federate resources into a grid • TeraGrid • A $150M award from NSF to the Supercomputer centers (NCSA, SCSC, PSC, etc etc)
- 3. What is the Grid? • The World Wide Web provides seamless access to information that is stored in many millions of different geographical locations • In contrast, the Grid is an emerging infrastructure that provides seamless access to computing power and data storage capacity distributed over the globe.
- 4. What is the Grid? • “Grid” was coined by Ian Foster and Carl Kesselman “The Grid: blueprint for a new computing infrastructure”. • Analogy with the electric power grid: plug-in to computing power without worrying where it comes from, like a toaster. • The idea has been around under other names for a while (distributed computing, metacomputing, …). • Technology is in place to realise the dream on a global scale.
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