Snowflake OverviewSnowflake ComputingOrganizations are struggling to make sense of their data within antiquated data platforms. Snowflake, the data warehouse built for the cloud, can help.
Grid computingChanchal SachdevaThis document provides an overview of grid computing, including what it is, the areas and users of grid computing, why organizations use it, how the grid architecture works, and the advantages and disadvantages. Grid computing allows for sharing and coordinated use of diverse distributed resources across dynamic virtual organizations. It enables global sharing, efficient resource use, and access regardless of distance through open standards and middleware that connects networked resources, users, and applications.
Introduction to linked dataOpen Data SupportThis document provides an introduction to linked data and open data. It discusses the evolution of the web from documents to interconnected data. The four principles of linked data are explained: using URIs to identify things, making URIs accessible, providing useful information about the URI, and including links to other URIs. The differences between open data and linked data are outlined. Key milestones in linked government data are presented. Formats for publishing linked data like RDF and SPARQL are introduced. Finally, the 5 star scheme for publishing open data as linked data is described.
PDPA Share & Learn: Data Processing Agreement (DPA) Example by Ramathibodi (S...Nawanan Theera-AmpornpuntPresented at Mahidol University, Nakhon Pathom, Thailand on September 20, 2021
Ubiquitous ComputingKamran AshrafUbiquitous computing refers to technology that is integrated into everyday life to the extent that it is indistinguishable from it. The vision is for computing services to be available anytime and anywhere through devices that are increasingly more powerful, smaller, and cheaper. Ubiquitous computing is changing daily activities by allowing people to communicate and interact with hundreds of computing devices in new ways. However, it also presents challenges in systems design, security and privacy, and how teaching and learning can take advantage of ubiquitous access to resources and tools.
Cloud operating systemsadak pramodhThe document discusses cloud operating systems. A cloud OS runs applications and stores data on remote servers that can be accessed from any internet-connected device. This is different than traditional desktop computing which stores programs and files locally. A cloud OS has several advantages like lower costs, automatic updates, universal access, and unlimited storage. However, it requires an internet connection and performance may be reduced without fast speeds. The document provides examples of cloud OSs, describes their architecture which involves clients connecting to a remote server over the network, and covers applications, demonstrations, storage features, advantages and disadvantages of cloud OSs.
Introduction to Neo4jNeo4jData is both our most valuable asset and our biggest ongoing challenge. As data grows in volume, variety and complexity, across applications, clouds and siloed systems, traditional ways of working with data no longer work.
Unlike traditional databases, which arrange data in rows, columns and tables, Neo4j has a flexible structure defined by stored relationships between data records.
We'll discuss the primary use cases for graph databases
Explore the properties of Neo4j that make those use cases possible
Look into the visualisation of graphs
Introduce how to write queries.
Webinar, 23 July 2020
Module-1.pptx63.pptxShrinivasa6This document provides an overview of a syllabus for a course on NoSQL databases. It discusses the evolution and fundamentals of NoSQL, various data distribution models, and explores different NoSQL data models like key-value, document, and graph databases. It also covers topics like MapReduce, CAP theorem, and different types of NoSQL databases compared to relational databases.
Data foundations for digital health.pptxHeatherLeslie14Plenary presentation to the International College of Emergency Medicine, 2022 06 22
Key messages
- We have a silo mentality for health data
- Interminable patching between systems is unsustainable
- We need a new approach
-- a 'little data' ecosystem
-- driven by clinicians
--peer reviewed by clinicians to ensure 'fit for purpose'
-- 2 level modelling -> tightly governed archetypes + clinically diverse templates
-- Create maximal data sets per concept as data building blocks; reuse and share
Couchbase 101 Dipti BorkarCouchbase 101 provides an overview of Couchbase including:
- Key concepts of Couchbase such as its use as a key-value store and document store using JSON documents.
- Single node and cluster-wide operations for reading, writing and updating documents.
- Cross data center replication (XDCR) to replicate data between geographically distributed clusters.
- Indexing and querying features including secondary indexes, views, and the new N1QL query language.
Data Centric Transformation in TelecomDataWorks SummitThe document discusses how telecom companies can undergo a data-centric transformation to better leverage customer data and remain competitive. It describes how telecoms are facing new challenges like social media, mobile apps, and customer expectations of better service. It argues telecoms should shift from an app-centric to data-centric model to better integrate and scale their use of data. This will allow them to gain better customer insights and optimize areas like customer experience, new digital services, and network management.
ntroducing to the Power of Graph TechnologyNeo4jNeo4j Presentation at AWS summit Stockholm
Kristof Neys, Graph Data Science Specialist, Field Engineering EMEA/APAC
Standard Datasets in Information Retrieval Jean BrendaThe document discusses standard datasets used for information retrieval (IR) system evaluation and research. It describes several major datasets including the Cranfield collection, which was the first test collection and used aeronautical papers, and the Text REtrieval Conference (TREC) collection, which is a large collection of newswire articles. It also mentions other datasets like Gov2, NTCIR, CLEF, and 20Newsgroups. The datasets provide documents, queries, and relevance judgments and allow comparison of IR systems and algorithms.
Visual SearchAmit PrabhudesaiVisual search, also known as content-based image retrieval, allows users to search for images using either text queries, visual queries by uploading an example image, or visual queries by drawing an image. It has many applications including searching product catalogs, maps, photo archives, and for law enforcement. A visual search system typically uses low-level image descriptors for color, texture, shape and spatial layout to extract machine-understandable features from images. It then calculates similarity distances between images and indexes them to allow efficient searching. Performance is measured using precision and recall metrics. Existing visual search engines can still struggle with semantic gaps between low-level features and high-level human concepts.
Inroduction to grid computing by gargi shankar vermagargishankar1981Grid 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.
Cloud computing basics (course1)Richard B AntalRichard B ANTAL created a course on cloud computing basics that is presented over multiple slides. The course covers the history of cloud computing beginning in 2002, defines it as accessing IT services over the internet running on someone else's infrastructure, and outlines the three main service models of SaaS, PaaS, and IaaS. The document discusses the key benefits of cloud computing including cost savings, flexibility, and agility, as well as the main deployment models of public, private, hybrid and community clouds. It seeks to establish whether cloud computing is real based on revenue figures and recap the benefits.
Interoperabilitysudhakar mandalThis document discusses interoperability between software components. It defines interoperability as the ability of independently developed components to interact meaningfully by communicating and exchanging data or services. Achieving interoperability is challenging due to heterogeneity between components in terms of programming languages, platforms, data formats, and assumptions. Common Object Request Broker Architecture (CORBA) and XML are examined as approaches to enabling interoperability, but both make assumptions that can limit their effectiveness and even introduce new interoperability issues in some cases. Shaw's taxonomy of interoperability solutions is also referenced.
Recent PhD Research Topic Ideas For Computer Science Engineering 2020PhD AssistanceRecent PhD Research Topic Ideas For Computer Science Engineering 2020 - PhD Assistance - http://bit.ly/2Pwmudf
Depiction of container-based clouds for allocation of resources using GA with dual chromosome.
A Cloud Robotic Network Energy Sensitive Computing Offloading strategy built using Genetic algorithm
A stable strategy for the deployment of containers using GA to protect against Cloud Attacks by Co-Residents
Read More : http://bit.ly/2NobDQb
#phdresearchtopicsmanagement
#phdresearchtopics
#phdresearchtopicscommerce
#phdresearchtopicseconomics
#phdresearchtopicsgeography
#howchoosephdresearchtopic
#phdresearchtopicslaw
#phdresearchtopicsfinance
For Any Queries : Website: www.phdassistance.com
Phd Research Lab : www.research.phdassistance.com
Email: info@phdassistance.com
Phone : +91-4448137070
Real Life Examples of Cybersecurity with Neo4jNeo4jThe document discusses graph databases as a solution for cybersecurity challenges. It presents real-life examples of how graph databases can be used by law enforcement to connect suspect data and by banks to detect fraud rings. The document concludes by demonstrating how a company's IT infrastructure and employees could be modeled as a graph to help with cybersecurity monitoring.
Grid computingPresentaionslive.blogspot.comThe 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
Cs6703 grid and cloud computing unit 3RMK ENGINEERING COLLEGE, CHENNAICloud 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.
Data sharing: How, what and why?dancrane_openPolicies from funders, publishers, and universities increasingly require researchers to share their data. Sharing data brings benefits like enabling replication and innovation by other researchers, safeguarding research integrity, and potentially increasing citations. Researchers should select what data to share, prepare it with good documentation and open file formats, and consider using repositories. The library provides support for data management plans, preparation, and sharing through services like Open Research Data Online.
WEB MINING.Sushil kasarWeb mining is the application of data mining techniques to extract knowledge from web data. There are three types of web mining: web usage mining analyzes server logs to learn about user behavior; web structure mining analyzes the hyperlink structure between websites; and web content mining analyzes the contents of web pages. Web mining has various applications in areas like e-commerce, advertising, search engines, and CRM to improve business decisions by understanding customer behavior and targeting customers. It allows businesses to increase sales, optimize websites, and gain marketing intelligence.
Semantic webImtiaz SiddiqueIntroduction to semantic web. Includes its goal, features, why we need, semantic web related framework, RDF's, Advantages, Uniform resource locator, web ontology language, micro-formats.
Key-Value NoSQL DatabaseHeman HosainpanaThis document discusses different types of distributed databases. It covers data models like relational, aggregate-oriented, key-value, and document models. It also discusses different distribution models like sharding and replication. Consistency models for distributed databases are explained including eventual consistency and the CAP theorem. Key-value stores are described in more detail as a simple but widely used data model with features like consistency, scaling, and suitable use cases. Specific key-value databases like Redis, Riak, and DynamoDB are mentioned.
Data Engineer's Lunch #85: Designing a Modern Data StackAnant CorporationWhat are the design considerations that go into architecting a modern data warehouse? This presentation will cover some of the requirements analysis, design decisions, and execution challenges of building a modern data lake/data warehouse.
Gis open source and cloud potentialsTim WilloughbyThis document discusses cloud and open source GIS. It highlights benefits like automated change management, flexible digital delivery, and data accuracy improvement. Open source is important because it allows for group collaboration, crowd sourcing, and benefits from many eyeballs finding bugs. Open source is now widely used including by 90% of supercomputers, 60% of internet servers, and 30% of smartphones. The cloud provides scalability and hosting for GIS applications and has enabled enterprise mapping, open data standards, and greater spatial analysis and adoption of GIS. While government has been slow to change, cloud and open source can help make government more transparent, efficient and user-oriented. There are still issues to address regarding data protection, security, standards
Introduction to Neo4jNeo4jData is both our most valuable asset and our biggest ongoing challenge. As data grows in volume, variety and complexity, across applications, clouds and siloed systems, traditional ways of working with data no longer work.
Unlike traditional databases, which arrange data in rows, columns and tables, Neo4j has a flexible structure defined by stored relationships between data records.
We'll discuss the primary use cases for graph databases
Explore the properties of Neo4j that make those use cases possible
Look into the visualisation of graphs
Introduce how to write queries.
Webinar, 23 July 2020
Module-1.pptx63.pptxShrinivasa6This document provides an overview of a syllabus for a course on NoSQL databases. It discusses the evolution and fundamentals of NoSQL, various data distribution models, and explores different NoSQL data models like key-value, document, and graph databases. It also covers topics like MapReduce, CAP theorem, and different types of NoSQL databases compared to relational databases.
Data foundations for digital health.pptxHeatherLeslie14Plenary presentation to the International College of Emergency Medicine, 2022 06 22
Key messages
- We have a silo mentality for health data
- Interminable patching between systems is unsustainable
- We need a new approach
-- a 'little data' ecosystem
-- driven by clinicians
--peer reviewed by clinicians to ensure 'fit for purpose'
-- 2 level modelling -> tightly governed archetypes + clinically diverse templates
-- Create maximal data sets per concept as data building blocks; reuse and share
Couchbase 101 Dipti BorkarCouchbase 101 provides an overview of Couchbase including:
- Key concepts of Couchbase such as its use as a key-value store and document store using JSON documents.
- Single node and cluster-wide operations for reading, writing and updating documents.
- Cross data center replication (XDCR) to replicate data between geographically distributed clusters.
- Indexing and querying features including secondary indexes, views, and the new N1QL query language.
Data Centric Transformation in TelecomDataWorks SummitThe document discusses how telecom companies can undergo a data-centric transformation to better leverage customer data and remain competitive. It describes how telecoms are facing new challenges like social media, mobile apps, and customer expectations of better service. It argues telecoms should shift from an app-centric to data-centric model to better integrate and scale their use of data. This will allow them to gain better customer insights and optimize areas like customer experience, new digital services, and network management.
ntroducing to the Power of Graph TechnologyNeo4jNeo4j Presentation at AWS summit Stockholm
Kristof Neys, Graph Data Science Specialist, Field Engineering EMEA/APAC
Standard Datasets in Information Retrieval Jean BrendaThe document discusses standard datasets used for information retrieval (IR) system evaluation and research. It describes several major datasets including the Cranfield collection, which was the first test collection and used aeronautical papers, and the Text REtrieval Conference (TREC) collection, which is a large collection of newswire articles. It also mentions other datasets like Gov2, NTCIR, CLEF, and 20Newsgroups. The datasets provide documents, queries, and relevance judgments and allow comparison of IR systems and algorithms.
Visual SearchAmit PrabhudesaiVisual search, also known as content-based image retrieval, allows users to search for images using either text queries, visual queries by uploading an example image, or visual queries by drawing an image. It has many applications including searching product catalogs, maps, photo archives, and for law enforcement. A visual search system typically uses low-level image descriptors for color, texture, shape and spatial layout to extract machine-understandable features from images. It then calculates similarity distances between images and indexes them to allow efficient searching. Performance is measured using precision and recall metrics. Existing visual search engines can still struggle with semantic gaps between low-level features and high-level human concepts.
Inroduction to grid computing by gargi shankar vermagargishankar1981Grid 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.
Cloud computing basics (course1)Richard B AntalRichard B ANTAL created a course on cloud computing basics that is presented over multiple slides. The course covers the history of cloud computing beginning in 2002, defines it as accessing IT services over the internet running on someone else's infrastructure, and outlines the three main service models of SaaS, PaaS, and IaaS. The document discusses the key benefits of cloud computing including cost savings, flexibility, and agility, as well as the main deployment models of public, private, hybrid and community clouds. It seeks to establish whether cloud computing is real based on revenue figures and recap the benefits.
Interoperabilitysudhakar mandalThis document discusses interoperability between software components. It defines interoperability as the ability of independently developed components to interact meaningfully by communicating and exchanging data or services. Achieving interoperability is challenging due to heterogeneity between components in terms of programming languages, platforms, data formats, and assumptions. Common Object Request Broker Architecture (CORBA) and XML are examined as approaches to enabling interoperability, but both make assumptions that can limit their effectiveness and even introduce new interoperability issues in some cases. Shaw's taxonomy of interoperability solutions is also referenced.
Recent PhD Research Topic Ideas For Computer Science Engineering 2020PhD AssistanceRecent PhD Research Topic Ideas For Computer Science Engineering 2020 - PhD Assistance - http://bit.ly/2Pwmudf
Depiction of container-based clouds for allocation of resources using GA with dual chromosome.
A Cloud Robotic Network Energy Sensitive Computing Offloading strategy built using Genetic algorithm
A stable strategy for the deployment of containers using GA to protect against Cloud Attacks by Co-Residents
Read More : http://bit.ly/2NobDQb
#phdresearchtopicsmanagement
#phdresearchtopics
#phdresearchtopicscommerce
#phdresearchtopicseconomics
#phdresearchtopicsgeography
#howchoosephdresearchtopic
#phdresearchtopicslaw
#phdresearchtopicsfinance
For Any Queries : Website: www.phdassistance.com
Phd Research Lab : www.research.phdassistance.com
Email: info@phdassistance.com
Phone : +91-4448137070
Real Life Examples of Cybersecurity with Neo4jNeo4jThe document discusses graph databases as a solution for cybersecurity challenges. It presents real-life examples of how graph databases can be used by law enforcement to connect suspect data and by banks to detect fraud rings. The document concludes by demonstrating how a company's IT infrastructure and employees could be modeled as a graph to help with cybersecurity monitoring.
Grid computingPresentaionslive.blogspot.comThe 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
Cs6703 grid and cloud computing unit 3RMK ENGINEERING COLLEGE, CHENNAICloud 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.
Data sharing: How, what and why?dancrane_openPolicies from funders, publishers, and universities increasingly require researchers to share their data. Sharing data brings benefits like enabling replication and innovation by other researchers, safeguarding research integrity, and potentially increasing citations. Researchers should select what data to share, prepare it with good documentation and open file formats, and consider using repositories. The library provides support for data management plans, preparation, and sharing through services like Open Research Data Online.
WEB MINING.Sushil kasarWeb mining is the application of data mining techniques to extract knowledge from web data. There are three types of web mining: web usage mining analyzes server logs to learn about user behavior; web structure mining analyzes the hyperlink structure between websites; and web content mining analyzes the contents of web pages. Web mining has various applications in areas like e-commerce, advertising, search engines, and CRM to improve business decisions by understanding customer behavior and targeting customers. It allows businesses to increase sales, optimize websites, and gain marketing intelligence.
Semantic webImtiaz SiddiqueIntroduction to semantic web. Includes its goal, features, why we need, semantic web related framework, RDF's, Advantages, Uniform resource locator, web ontology language, micro-formats.
Key-Value NoSQL DatabaseHeman HosainpanaThis document discusses different types of distributed databases. It covers data models like relational, aggregate-oriented, key-value, and document models. It also discusses different distribution models like sharding and replication. Consistency models for distributed databases are explained including eventual consistency and the CAP theorem. Key-value stores are described in more detail as a simple but widely used data model with features like consistency, scaling, and suitable use cases. Specific key-value databases like Redis, Riak, and DynamoDB are mentioned.
Data Engineer's Lunch #85: Designing a Modern Data StackAnant CorporationWhat are the design considerations that go into architecting a modern data warehouse? This presentation will cover some of the requirements analysis, design decisions, and execution challenges of building a modern data lake/data warehouse.
Gis open source and cloud potentialsTim WilloughbyThis document discusses cloud and open source GIS. It highlights benefits like automated change management, flexible digital delivery, and data accuracy improvement. Open source is important because it allows for group collaboration, crowd sourcing, and benefits from many eyeballs finding bugs. Open source is now widely used including by 90% of supercomputers, 60% of internet servers, and 30% of smartphones. The cloud provides scalability and hosting for GIS applications and has enabled enterprise mapping, open data standards, and greater spatial analysis and adoption of GIS. While government has been slow to change, cloud and open source can help make government more transparent, efficient and user-oriented. There are still issues to address regarding data protection, security, standards
خدمات الويب (Web Services) و كيف تنشئها lunarhaloهذه المحاضرة ألقيت في اجتماع الرياض قيكس في مقر بادر في يوليو 2010. شرحت فيها كيف تقوم ببناء خدمة ويب و التفاصيل البرمجية حولها.
Cloud GIS - GIS in the Rockies 2011chelmThe document discusses the evolution of GIS from desktop-based to cloud-based systems hosted on the internet. It outlines several periods in GIS history, from pre-interactive to the current social-location-mobile period. The present involves mobile and cloud GIS, with location-based services accessible from various devices. Major players in cloud GIS are mentioned, with examples of capabilities like visualization, analysis, and geoprocessing available through cloud-hosted systems.
Mobile agentsSantosh PandeyMobile agents are programs that can autonomously migrate between nodes in a network. They travel from node to node performing tasks on behalf of users. When a mobile agent moves, it transfers its code and state to the next node where it resumes execution. This allows processing to occur closer to where data is located, reducing network usage and improving response times, especially in low-bandwidth environments. However, security challenges must be addressed since mobile agents could potentially misuse or damage resources at the nodes they visit.
Contracting for Agile Software Developmentcspag67Many software development organizations work within the bounds of contractual agreements where the limitations imposed by the “Iron Triangle” of fixed timelines, budgets, and scope challenge their ability to embrace change and focus on value delivery. Agile practitioners often comment that agile contracting is a difficult problem, but proven solutions are rarely presented. Rachel Weston and Chris Spagnuolo offer some tools they have used in their own agile contracting work to help agile practitioners deal with different contracting scenarios while promoting agile practices, protecting the development organization, and still providing value and protection to the client’s organization. Through a combined workshop and facilitated collaborative session, Rachel and Chris present new agile contracting tools that can be added to your toolbox. You will gain a deeper understanding of the problems associated with agile contracting as well as practical solutions for dealing with contracts in an agile manner.
Grid computing & its applicationsAlokeparna ChoudhuryGrid 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.
Mobile agent Anjan MondalThe document contains information about mobile agents from several slides presented on July 25, 2012. It defines mobile agents as programs that can migrate between systems in a network to perform processing and decide where to move. The key properties are discussed, including carrying code, data, and execution state between systems (strong mobility). Threats to mobile agent systems are also summarized, such as malicious agents that attack host systems or other agents, and malicious hosts that can interfere with agent execution through passive traffic analysis or active attacks.
Grid computingDikshita_ViradiaIn 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 and cluster_computing_chapter1Bharath KumarGrid computing allows for the sharing of distributed computing resources over a network. It provides users with access to high-end computing facilities in a dependable, consistent, and inexpensive manner. A grid aggregates distributed computing power to solve large-scale problems. It enables virtual organizations through coordinated sharing of resources across locations, organizations, and hardware/software boundaries. Grid computing provides computational utility to consumers by managing resource identification, allocation, and consolidation through middleware software. It allows under-utilized resources to be dynamically distributed in an equitable manner.
Grid computing [2005]Raul SotoGrid 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.
Introduction to Grid ComputingabhijeetnawalThis 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 notesSyed Mustafa1. Grid computing is a distributed computing approach that allows users to access computational resources over a network. It aims to dynamically allocate resources like processing power, storage, or software according to user demands.
2. Grid computing provides a utility-like model for accessing computing resources. Users can access resources from a grid in the same way users access utilities like power or water grids.
3. Key benefits of grid computing include maximizing resource utilization, providing fast and cheap computing services, and enabling collaboration through secure resource sharing across organizations. Grid computing has applications in scientific research, businesses, and e-governance.
Grid computingKeshab NathGrid 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.
Unit i introduction to grid computingsudha karGrid 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.
Grid computing pptRicha ChaudharyGrid 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.
Networkingmohamednacimشرح مفصل عن الشبكات المحلية السلكية واللاسلكية واسلوب الربط للشبكة مع شرح عن امن الشبكات وكيفية الحفاض على امن المعلومات من السرقة او التخريب
Ccent اساسيات الشبكات من شركة سيسكوMustafa SadiqCcent اساسيات الشبكات من شركة سيسكو
للمزيد تفضلوا بزيارة موقعي على الرابط التالي
https://mustafasadiq0.com
Towards secure SDNsSafi Beik KarboujResearch paper I wrote in Arabic describing in technical detail "Software Defined Networking" which is the future of Network Systems Development.
(Software-defined networking (SDN) is an architecture designed to make a network more flexible and easier to manage. SDN centralizes management by abstracting the control plane from the data forwarding function in the discrete networking devices.)
#Practical Examples of modern SDNs (AWS, Azure, Digital Ocean)
22.pptHanaMohammed39التجاره الالكترونيه:
شبكة من الشبكات حيث تربط مجموعة كبيرة من شبكات الحاسوب الواسعة التي تنتشر في شتى أنحاء العالم.
وقد عّرف مجلس الشبكات الفدرالي بأنها نظام عالمي للمعلومات
#7: الطبقة النسيجية : تعرف واجهات الموارد المحلية و التي تتم مشاركتها . و هي تتضمن الحواسب . أنظمة التخزين , البرامج و غيرها من الموارد و هي معرفة من وجهة نظر منطقية و ليس فيزيائية
طبقة الربط : تعرف بروتوكولات الاتصال و التحقق و هي تتضمن مكدس بروتوكولات IP بالإضافة إلى بروتوكولات الامن
طبقة الموارد : تستخدم هذه الطبقة بروتوكولات الامن و الاتصال المعرفة من قبل طبقة الربط للتحكم بالتفويض الامن , التهيئة , المراقبة , المحاسبة . الدفع من أجل مشاركة موارد مستقلة . و هي تستدعي وظائف الطبقة النسيجية للنفاذ و التحكم بالموارد المحلية . و هي تعالج الموارد المنفردة . و تتجاهل الحالات العامة التي هي من اختصاص طبقة التجميع
التجميع : مسؤولة عن جميع عمليات إدارة الموارد العامة و التفاعل بين مجموعة موارد و هي تقوم بعمليات المشاركة باستخدام عدد قليل من بروتوكولات طبقة الموارد و الربط
التطبيقات : تستخدم الموارد في بيئة الشبكة من خلال بروتوكول النفاذ إلى الموارد