Cloud computing has grown tremendously and is projected to continue rapid growth. It provides massive computing resources that can be accessed on-demand and paid for through utility-based pricing models. There are various types of cloud services including Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). Cloud computing enables data-intensive applications through massive data storage and the ability to run large-scale distributed computing frameworks.
There are many cloud providers that offer infrastructure, platform, and software services. Clouds can be public, accessible to any paying customer, or private and only accessible internally. Customers save significant time and money using cloud services by gaining rapid access to flexible computing resources without large upfront investments.
This document discusses the key aspects of cloud computing. It begins by outlining the massive scale of today's clouds, with companies like Facebook, Microsoft, and Amazon operating clouds with tens or hundreds of thousands of servers. It then describes the main categories of cloud services - Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) - which allow on-demand access to computing resources. Finally, it notes that clouds are data-intensive in nature and have enabled new programming paradigms like MapReduce that make it easy to write highly parallel programs for large datasets.
This document discusses the key aspects of cloud computing. It begins by outlining the massive scale of today's clouds, with companies like Facebook, Microsoft, and Amazon operating clouds with tens or hundreds of thousands of servers. It then discusses the main characteristics of cloud computing, including on-demand access in a pay-as-you-go model, the data-intensive nature of workloads involving terabytes and petabytes of data, and new programming paradigms like MapReduce. The document also covers the differences between public, private, and academic clouds, and factors to consider in choosing between outsourcing to a public cloud or operating your own private cloud.
This document discusses the key aspects of cloud computing. It begins by outlining the massive scale of today's clouds, with companies like Facebook, Microsoft, and Amazon operating clouds with tens or hundreds of thousands of servers. It then discusses the main characteristics of cloud computing, including on-demand access in a pay-as-you-go model, the data-intensive nature of workloads involving terabytes or petabytes of data, and new programming paradigms like MapReduce. The document also covers the differences between public, private, and academic clouds, and factors to consider in choosing between outsourcing to a public cloud or operating your own private cloud.
This document discusses the key aspects of cloud computing. It begins by outlining the massive scale of today's clouds, with companies like Facebook, Microsoft, and Amazon operating clouds with tens or hundreds of thousands of servers. It then discusses the main characteristics of cloud computing: massive scale, on-demand access in a pay-as-you-go model, the data-intensive nature of workloads, and new programming paradigms like MapReduce. The document also covers the differences between public, private, and academic clouds, and factors to consider in choosing between outsourcing to a public cloud or operating your own private cloud.
Here are the key points to consider in choosing between public and private clouds:
- Public clouds offer massive scale and flexibility as you pay for only the resources you use. However, private data/IP may not be as secure as in a private cloud where you control the infrastructure.
- Private clouds give you more security and control over your infrastructure but require upfront investment in hardware and ongoing maintenance costs. It may not scale as easily as tapping into vast public cloud resources.
- A hybrid approach using a private cloud for sensitive workloads and a public cloud for other flexible needs may strike the best balance between security, control and scalability. Starting small in a public cloud to test demand before scaling up is a common approach for new
This document provides an introduction to cloud computing. It discusses key features of clouds including massive scale, on-demand access in a pay-as-you-go model, data-intensive workloads producing terabytes and petabytes of data, and new programming paradigms like MapReduce. The document also discusses different types of clouds including public, private and academic clouds. It provides examples of large cloud deployments from companies like Amazon, Microsoft, Google and others to illustrate the massive scale of modern cloud computing infrastructure.
There are many cloud providers that offer infrastructure, platform, and software services. Clouds can be public, accessible to any paying customer, or private and only accessible internally. Customers save significant time and money using cloud services by gaining rapid access to flexible computing resources without large upfront investments.
This document discusses the key aspects of cloud computing. It begins by outlining the massive scale of today's clouds, with companies like Facebook, Microsoft, and Amazon operating clouds with tens or hundreds of thousands of servers. It then describes the main categories of cloud services - Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) - which allow on-demand access to computing resources. Finally, it notes that clouds are data-intensive in nature and have enabled new programming paradigms like MapReduce that make it easy to write highly parallel programs for large datasets.
This document discusses the key aspects of cloud computing. It begins by outlining the massive scale of today's clouds, with companies like Facebook, Microsoft, and Amazon operating clouds with tens or hundreds of thousands of servers. It then discusses the main characteristics of cloud computing, including on-demand access in a pay-as-you-go model, the data-intensive nature of workloads involving terabytes and petabytes of data, and new programming paradigms like MapReduce. The document also covers the differences between public, private, and academic clouds, and factors to consider in choosing between outsourcing to a public cloud or operating your own private cloud.
This document discusses the key aspects of cloud computing. It begins by outlining the massive scale of today's clouds, with companies like Facebook, Microsoft, and Amazon operating clouds with tens or hundreds of thousands of servers. It then discusses the main characteristics of cloud computing, including on-demand access in a pay-as-you-go model, the data-intensive nature of workloads involving terabytes or petabytes of data, and new programming paradigms like MapReduce. The document also covers the differences between public, private, and academic clouds, and factors to consider in choosing between outsourcing to a public cloud or operating your own private cloud.
This document discusses the key aspects of cloud computing. It begins by outlining the massive scale of today's clouds, with companies like Facebook, Microsoft, and Amazon operating clouds with tens or hundreds of thousands of servers. It then discusses the main characteristics of cloud computing: massive scale, on-demand access in a pay-as-you-go model, the data-intensive nature of workloads, and new programming paradigms like MapReduce. The document also covers the differences between public, private, and academic clouds, and factors to consider in choosing between outsourcing to a public cloud or operating your own private cloud.
Here are the key points to consider in choosing between public and private clouds:
- Public clouds offer massive scale and flexibility as you pay for only the resources you use. However, private data/IP may not be as secure as in a private cloud where you control the infrastructure.
- Private clouds give you more security and control over your infrastructure but require upfront investment in hardware and ongoing maintenance costs. It may not scale as easily as tapping into vast public cloud resources.
- A hybrid approach using a private cloud for sensitive workloads and a public cloud for other flexible needs may strike the best balance between security, control and scalability. Starting small in a public cloud to test demand before scaling up is a common approach for new
This document provides an introduction to cloud computing. It discusses key features of clouds including massive scale, on-demand access in a pay-as-you-go model, data-intensive workloads producing terabytes and petabytes of data, and new programming paradigms like MapReduce. The document also discusses different types of clouds including public, private and academic clouds. It provides examples of large cloud deployments from companies like Amazon, Microsoft, Google and others to illustrate the massive scale of modern cloud computing infrastructure.
Here are the key considerations in choosing between public and private clouds for a new service/company:
- Public clouds like AWS provide massive scalability and flexibility with no upfront investment, allowing you to focus resources on your core product. However, you lose some control and security over your infrastructure.
- Private clouds give you more control and security over your infrastructure but require managing and maintaining servers. Upfront investment is needed to set up hardware. Scaling can be more difficult than public clouds.
- A hybrid approach using a public cloud for non-critical loads and a private cloud for sensitive workloads may strike the best balance of cost, control and flexibility for a new company.
- Consider your security, data ownership and compliance
This document discusses the key aspects of cloud computing. It begins by outlining the massive scale of today's clouds, with companies like Facebook, Microsoft, and Amazon operating clouds with tens or hundreds of thousands of servers. It then discusses the main characteristics of cloud computing, including on-demand access in a pay-as-you-go model, the data-intensive nature of workloads involving terabytes or petabytes of data, and new programming paradigms like MapReduce. The document also covers the differences between public, private, and academic clouds, and factors to consider in choosing between outsourcing to a public cloud or running your own private cloud.
L2-3.FA19descriptions lead to more readersOlajide Kuku
descriptions lead to more readersdescriptions lead to more readersdescriptions lead to more readersdescriptions lead to more readersdescriptions lead to more readersdescriptions lead to more readers
Here are the key considerations in choosing between public and private clouds for a new service/company:
- Public clouds like AWS provide massive scalability and flexibility with no upfront investment, which is attractive for new startups. You pay only for the resources you use so costs scale with your business. However, you have less control and security may be a concern if handling sensitive data.
- Private clouds run within your own data center so give you more control and security, but require upfront investment in hardware and ongoing maintenance costs. Scaling can be more difficult as your infrastructure is fixed.
- A hybrid approach using a public cloud for initial development and growth, then transitioning to a private cloud later as the business scales, provides
This document discusses the key aspects of cloud computing. It begins by outlining the massive scale of today's clouds, with companies like Facebook, Microsoft, and Amazon operating clouds with tens or hundreds of thousands of servers. It then discusses the main characteristics of cloud computing, including on-demand access in a pay-as-you-go model, the data-intensive nature of workloads involving terabytes or petabytes of data, and new programming paradigms like MapReduce. The document also covers the differences between public, private, and academic clouds, and factors to consider in choosing between outsourcing to a public cloud or operating your own private cloud.
This document discusses the key aspects of cloud computing. It begins by outlining the massive scale of today's clouds, with companies like Facebook, Microsoft, and Amazon operating clouds with tens or hundreds of thousands of servers. It then describes the main characteristics of cloud computing, including on-demand access in a pay-as-you-go model, the data-intensive nature of workloads involving terabytes and petabytes of data, and new programming paradigms like MapReduce. The document also discusses the differences between public, private, and academic clouds, and how clouds have built upon previous distributed systems like timesharing networks from the 1960s-70s.
Here are the key points about using a public cloud from your startup's perspective:
- Public clouds allow you to avoid large upfront investments in hardware/infrastructure and only pay for what you use. This reduces costs and allows funds to be focused on developing your product/service.
- Public clouds provide massive scale and flexibility to scale up or down as needed. This is important for a new startup dealing with uncertainty.
- Relying on public clouds removes the burden of managing infrastructure/hardware yourself. You can focus on your core business instead of IT operations.
- Downsides include losing some control over the infrastructure and being reliant on a third party cloud provider. You also have less customization options compared to
The Internet of Behavior or IoB is a concept that brings together the best of data analysis, behavioural analysis, and technology along with ...The Internet of Things has played an explosive role in the technological revolution of today, which has led to the creation of smart systems and environments. But the truth is, although we hear the term a lot, we may not fully understand what the Internet of behavior is capable of, and also how we can be a part of it.
Cloud computing refers to applications and services delivered over the Internet. It allows users to access files, personal data, and applications from any device with Internet access. Key benefits include reduced costs, flexibility, and scalability. Challenges include availability of data if a provider fails, data lock-in to specific providers, and performance unpredictability. However, opportunities exist through standardization, encryption, and improved virtualization support. The future of cloud computing is promising with many companies investing heavily and forecasts of widespread adoption over the next few years.
Cloud computing is a type of Internet-based computing that provides shared computer processing resources and data to computers and other devices on demand. It is a model for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources (e.g., computer networks, servers, storage, applications and services),
An Introduction to Data Intensive ComputingCollin Bennett
This document provides an introduction to data intensive computing. It discusses how data and computing are growing exponentially due to improvements in instruments and technologies. This is creating new paradigms of data intensive science and computing. The document then discusses how cloud computing models like utility clouds (e.g. Amazon) and data clouds are facilitating data intensive computing by providing scalable resources and platforms for storing, managing and processing large amounts of data. Key concepts covered include virtualization, infrastructure as a service (IaaS), and MapReduce as a programming model for distributed computing on big data.
Cloud Spotting 2017: An overview of cloud computingPatrice Kerremans
An overview of cloud computing I taught to students. With a strong bias towards Amazon Web Services (AWS). Some examples are included as well as an overview of the most important AWS services.
Introduction to Cloud Computing and Big Datawaheed751
This document provides an introduction to cloud computing and big data. It defines cloud computing as a model for providing scalable computing resources over the internet with minimal management. The key characteristics of cloud computing include on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. Dynamic provisioning allows cloud resources to scale up and down based on demand. This helps solve the problems of underutilization and overload in traditional systems with static capacity. The document also discusses how dynamic provisioning can be used in multi-tier web applications running on cloud infrastructure.
The document provides an overview of using Amazon Web Services (AWS) for high-performance computing (HPC) clusters. It discusses how AWS enables scientists to build HPC clusters on demand that can scale up and down based on workload needs. Specific solutions and services mentioned include Alces Flight for launching ready-to-compute HPC clusters on AWS in minutes, the AWS Spot Market for accessing spare computing capacity at low costs, and examples of using AWS for scientific workloads like satellite image analysis and computational fluid design simulations.
This document provides an overview of cloud computing. It begins with Gartner's definition of cloud computing as delivering elastic IT capabilities as a service using internet technologies. The document then discusses the history of cloud computing from John McCarthy's vision in 1961 to modern cloud services from Amazon, Google, and Microsoft. It explores how cloud computing solves the scalability problem by allowing resources to rapidly scale up or down as needed. The document outlines the key cloud service models of IaaS, PaaS, and SaaS and provides examples. It also shares success stories of companies like Zynga and Netflix leveraging cloud computing. Finally, it discusses potential cost savings and benefits of cloud computing through case studies.
This document provides an overview of cloud computing concepts including:
- The key characteristics of cloud computing including on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service.
- The roots of cloud computing in technologies like virtualization, distributed computing, web services, and utility computing.
- The different service models of cloud computing including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).
Course 3 : Types of data and opportunities by Nikolaos DeligiannisBetacowork
This document discusses big data and opportunities related to different types of data. It covers challenges of big data including volume, velocity, variety and veracity. It also discusses value that can be extracted from data. The document outlines static versus real-time data and structured versus unstructured data. Examples of applying machine learning techniques like regression, classification, clustering and dimensionality reduction are provided. The introduction to cloud computing discusses public, private and hybrid clouds and features of cloud infrastructure.
Presentation on the topic "Cloud Computing"..
properly and briefly explained all the contents of the topic..
backgrounds and photos used makes it look cool ,accurate and to the point..
Here are the key considerations in choosing between public and private clouds for a new service/company:
- Public clouds like AWS provide massive scalability and flexibility with no upfront investment, allowing you to focus resources on your core product. However, you lose some control and security over your infrastructure.
- Private clouds give you more control and security over your infrastructure but require managing and maintaining servers. Upfront investment is needed to set up hardware. Scaling can be more difficult than public clouds.
- A hybrid approach using a public cloud for non-critical loads and a private cloud for sensitive workloads may strike the best balance of cost, control and flexibility for a new company.
- Consider your security, data ownership and compliance
This document discusses the key aspects of cloud computing. It begins by outlining the massive scale of today's clouds, with companies like Facebook, Microsoft, and Amazon operating clouds with tens or hundreds of thousands of servers. It then discusses the main characteristics of cloud computing, including on-demand access in a pay-as-you-go model, the data-intensive nature of workloads involving terabytes or petabytes of data, and new programming paradigms like MapReduce. The document also covers the differences between public, private, and academic clouds, and factors to consider in choosing between outsourcing to a public cloud or running your own private cloud.
L2-3.FA19descriptions lead to more readersOlajide Kuku
descriptions lead to more readersdescriptions lead to more readersdescriptions lead to more readersdescriptions lead to more readersdescriptions lead to more readersdescriptions lead to more readers
Here are the key considerations in choosing between public and private clouds for a new service/company:
- Public clouds like AWS provide massive scalability and flexibility with no upfront investment, which is attractive for new startups. You pay only for the resources you use so costs scale with your business. However, you have less control and security may be a concern if handling sensitive data.
- Private clouds run within your own data center so give you more control and security, but require upfront investment in hardware and ongoing maintenance costs. Scaling can be more difficult as your infrastructure is fixed.
- A hybrid approach using a public cloud for initial development and growth, then transitioning to a private cloud later as the business scales, provides
This document discusses the key aspects of cloud computing. It begins by outlining the massive scale of today's clouds, with companies like Facebook, Microsoft, and Amazon operating clouds with tens or hundreds of thousands of servers. It then discusses the main characteristics of cloud computing, including on-demand access in a pay-as-you-go model, the data-intensive nature of workloads involving terabytes or petabytes of data, and new programming paradigms like MapReduce. The document also covers the differences between public, private, and academic clouds, and factors to consider in choosing between outsourcing to a public cloud or operating your own private cloud.
This document discusses the key aspects of cloud computing. It begins by outlining the massive scale of today's clouds, with companies like Facebook, Microsoft, and Amazon operating clouds with tens or hundreds of thousands of servers. It then describes the main characteristics of cloud computing, including on-demand access in a pay-as-you-go model, the data-intensive nature of workloads involving terabytes and petabytes of data, and new programming paradigms like MapReduce. The document also discusses the differences between public, private, and academic clouds, and how clouds have built upon previous distributed systems like timesharing networks from the 1960s-70s.
Here are the key points about using a public cloud from your startup's perspective:
- Public clouds allow you to avoid large upfront investments in hardware/infrastructure and only pay for what you use. This reduces costs and allows funds to be focused on developing your product/service.
- Public clouds provide massive scale and flexibility to scale up or down as needed. This is important for a new startup dealing with uncertainty.
- Relying on public clouds removes the burden of managing infrastructure/hardware yourself. You can focus on your core business instead of IT operations.
- Downsides include losing some control over the infrastructure and being reliant on a third party cloud provider. You also have less customization options compared to
The Internet of Behavior or IoB is a concept that brings together the best of data analysis, behavioural analysis, and technology along with ...The Internet of Things has played an explosive role in the technological revolution of today, which has led to the creation of smart systems and environments. But the truth is, although we hear the term a lot, we may not fully understand what the Internet of behavior is capable of, and also how we can be a part of it.
Cloud computing refers to applications and services delivered over the Internet. It allows users to access files, personal data, and applications from any device with Internet access. Key benefits include reduced costs, flexibility, and scalability. Challenges include availability of data if a provider fails, data lock-in to specific providers, and performance unpredictability. However, opportunities exist through standardization, encryption, and improved virtualization support. The future of cloud computing is promising with many companies investing heavily and forecasts of widespread adoption over the next few years.
Cloud computing is a type of Internet-based computing that provides shared computer processing resources and data to computers and other devices on demand. It is a model for enabling ubiquitous, on-demand access to a shared pool of configurable computing resources (e.g., computer networks, servers, storage, applications and services),
An Introduction to Data Intensive ComputingCollin Bennett
This document provides an introduction to data intensive computing. It discusses how data and computing are growing exponentially due to improvements in instruments and technologies. This is creating new paradigms of data intensive science and computing. The document then discusses how cloud computing models like utility clouds (e.g. Amazon) and data clouds are facilitating data intensive computing by providing scalable resources and platforms for storing, managing and processing large amounts of data. Key concepts covered include virtualization, infrastructure as a service (IaaS), and MapReduce as a programming model for distributed computing on big data.
Cloud Spotting 2017: An overview of cloud computingPatrice Kerremans
An overview of cloud computing I taught to students. With a strong bias towards Amazon Web Services (AWS). Some examples are included as well as an overview of the most important AWS services.
Introduction to Cloud Computing and Big Datawaheed751
This document provides an introduction to cloud computing and big data. It defines cloud computing as a model for providing scalable computing resources over the internet with minimal management. The key characteristics of cloud computing include on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. Dynamic provisioning allows cloud resources to scale up and down based on demand. This helps solve the problems of underutilization and overload in traditional systems with static capacity. The document also discusses how dynamic provisioning can be used in multi-tier web applications running on cloud infrastructure.
The document provides an overview of using Amazon Web Services (AWS) for high-performance computing (HPC) clusters. It discusses how AWS enables scientists to build HPC clusters on demand that can scale up and down based on workload needs. Specific solutions and services mentioned include Alces Flight for launching ready-to-compute HPC clusters on AWS in minutes, the AWS Spot Market for accessing spare computing capacity at low costs, and examples of using AWS for scientific workloads like satellite image analysis and computational fluid design simulations.
This document provides an overview of cloud computing. It begins with Gartner's definition of cloud computing as delivering elastic IT capabilities as a service using internet technologies. The document then discusses the history of cloud computing from John McCarthy's vision in 1961 to modern cloud services from Amazon, Google, and Microsoft. It explores how cloud computing solves the scalability problem by allowing resources to rapidly scale up or down as needed. The document outlines the key cloud service models of IaaS, PaaS, and SaaS and provides examples. It also shares success stories of companies like Zynga and Netflix leveraging cloud computing. Finally, it discusses potential cost savings and benefits of cloud computing through case studies.
This document provides an overview of cloud computing concepts including:
- The key characteristics of cloud computing including on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service.
- The roots of cloud computing in technologies like virtualization, distributed computing, web services, and utility computing.
- The different service models of cloud computing including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).
Course 3 : Types of data and opportunities by Nikolaos DeligiannisBetacowork
This document discusses big data and opportunities related to different types of data. It covers challenges of big data including volume, velocity, variety and veracity. It also discusses value that can be extracted from data. The document outlines static versus real-time data and structured versus unstructured data. Examples of applying machine learning techniques like regression, classification, clustering and dimensionality reduction are provided. The introduction to cloud computing discusses public, private and hybrid clouds and features of cloud infrastructure.
Presentation on the topic "Cloud Computing"..
properly and briefly explained all the contents of the topic..
backgrounds and photos used makes it look cool ,accurate and to the point..
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
2. The Hype!
• Forrester in 2010 – Cloud computing will go from
$40.7 billion in 2010 to $241 billion in 2020.
• Goldman Sachs says cloud computing will grow
at annual rate of 30% from 2013-2018
• Hadoop market to reach $20.8 B by by 2018:
Transparency Market Research
• Companies and even Federal/state governments
using cloud computing now: fbo.gov
2
3. Many Cloud Providers
• AWS: Amazon Web Services
– EC2: Elastic Compute Cloud
– S3: Simple Storage Service
– EBS: Elastic Block Storage
• Microsoft Azure
• Google Cloud/Compute Engine/AppEngine
• Rightscale, Salesforce, EMC, Gigaspaces, 10gen, Datastax,
Oracle, VMWare, Yahoo, Cloudera
• And many many more!
3
4. Two Categories of Clouds
• Can be either a (i) public cloud, or (ii) private cloud
• Private clouds are accessible only to company employees
• Public clouds provide service to any paying customer:
– Amazon S3 (Simple Storage Service): store arbitrary datasets, pay per GB-month
stored
• As of 2019: 0.4c-3 c per GB month
– Amazon EC2 (Elastic Compute Cloud): upload and run arbitrary OS images, pay
per CPU hour used
• As of 2019: 0.2 c per CPU hr to $7.2 per CPU hr (depending on strength)
– Google cloud: similar pricing as above
– Google AppEngine/Compute Engine: develop applications within their appengine
framework, upload data that will be imported into their format, and run
4
5. Customers Save Time and $$$
• Dave Power, Associate Information Consultant at Eli Lilly and
Company: “With AWS, Powers said, a new server can be up and
running in three minutes (it used to take Eli Lilly seven and a half
weeks to deploy a server internally) and a 64-node Linux cluster can
be online in five minutes (compared with three months internally). …
It's just shy of instantaneous.”
• Ingo Elfering, Vice President of Information Technology Strategy,
GlaxoSmithKline: “With Online Services, we are able to reduce our IT
operational costs by roughly 30% of what we’re spending”
• Jim Swartz, CIO, Sybase: “At Sybase, a private cloud of virtual servers
inside its datacenter has saved nearly $US2 million annually since
2006, Swartz says, because the company can share computing power
and storage resources across servers.”
• 100s of startups in Silicon Valley can harness large computing resources
without buying their own machines.
5
6. What is a Cloud?
• It’s a cluster!
• It’s a supercomputer!
• It’s a datastore!
• It’s superman!
• None of the above
• All of the above
• Cloud = Lots of storage + compute cycles nearby
6
7. What is a Cloud?
• A single-site cloud (aka “Datacenter”) consists
of
– Compute nodes (grouped into racks) (2)
– Switches, connecting the racks
– A network topology, e.g., hierarchical
– Storage (backend) nodes connected to the network
(3)
– Front-end for submitting jobs and receiving client
requests (1)
– (1-3: Often called “three-tier architecture”)
– Software Services
• A geographically distributed cloud consists of
– Multiple such sites
– Each site perhaps with a different structure and
services
7
9. “A Cloudy History of Time”
1940
1950
1960
1970
1980
1990
2000
Timesharing Companies
& Data Processing Industry
Grids
Peer to peer systems
Clusters
The first datacenters!
PCs
(not distributed!)
Clouds and datacenters
2012
9
10. “A Cloudy History of Time”
1940
1950
1960
1970
1980
1990
2000
2012 Clouds
Grids (1980s-2000s):
•GriPhyN (1970s-80s)
•Open Science Grid and Lambda Rail (2000s)
•Globus & other standards (1990s-2000s)
Timesharing Industry (1975):
•Market Share: Honeywell 34%, IBM 15%,
•Xerox 10%, CDC 10%, DEC 10%, UNIVAC 10%
•Honeywell 6000 & 635, IBM 370/168,
Xerox 940 & Sigma 9, DEC PDP-10, UNIVAC 1108
Data Processing Industry
- 1968: $70 M. 1978: $3.15 Billion
First large datacenters: ENIAC, ORDVAC, ILLIAC
Many used vacuum tubes and mechanical relays
Berkeley NOW Project
Supercomputers
Server Farms (e.g., Oceano)
P2P Systems (90s-00s)
•Many Millions of users
•Many GB per day
10
11. Trends: Technology
• Doubling Periods – storage: 12 mos, bandwidth: 9 mos,
and (what law is this?) cpu compute capacity: 18 mos
• Then and Now
– Bandwidth
• 1985: mostly 56Kbps links nationwide
• 2015: Tbps links widespread
– Disk capacity
• Today’s PCs have TBs, far more than a 1990 supercomputer
11
12. Trends: Users
• Then and Now
Biologists:
– 1990: were running small single-molecule
simulations
– Today: CERN’s Large Hadron Collider producing
many PB/year
12
13. Prophecies
• In 1965, MIT's Fernando Corbató and the other designers of
the Multics operating system envisioned a computer facility
operating “like a power company or water company”.
• Plug your thin client into the computing Utility and Play
your favorite Intensive Compute & Communicate
Application
– Have today’s clouds brought us closer to this reality? Think
about it.
13
14. Four Features New in Today’s
Clouds
I. Massive scale.
II. On-demand access: Pay-as-you-go, no upfront commitment.
– And anyone can access it
III. Data-intensive Nature: What was MBs has now become TBs, PBs and
XBs.
– Daily logs, forensics, Web data, etc.
– Humans have data numbness: Wikipedia (large) compressed is only about 10 GB!
IV. New Cloud Programming Paradigms: MapReduce/Hadoop,
NoSQL/Cassandra/MongoDB and many others.
– High in accessibility and ease of programmability
– Lots of open-source
Combination of one or more of these gives rise to novel and unsolved
distributed computing problems in cloud computing. 14
15. I. Massive Scale
• Facebook [GigaOm, 2012]
– 30K in 2009 -> 60K in 2010 -> 180K in 2012
• Microsoft [NYTimes, 2008]
– 150K machines
– Growth rate of 10K per month
– 80K total running Bing
– In 2013, Microsoft Cosmos had 110K machines (4 sites)
• Yahoo! [2009]:
– 100K
– Split into clusters of 4000
• AWS EC2 [Randy Bias, 2009]
– 40K machines
– 8 cores/machine
• eBay [2012]: 50K machines
• HP [2012]: 380K in 180 DCs
• Google [2011, Data Center Knowledge] : 900K
15
16. Quiz: Where is the World’s
Largest Datacenter?
• (2018) China Telecom. 10.7 Million sq. ft.
• (2017) “The Citadel” Nevada. 7.2 Million sq. ft.
• (2015) In Chicago!
• 350 East Cermak, Chicago, 1.1 MILLION sq. ft.
• Shared by many different “carriers”
• Critical to Chicago Mercantile Exchange
• See:
– https://www.gigabitmagazine.com/top10/top-10-biggest-data-centres-world
– https://www.racksolutions.com/news/data-center-news/top-10-largest-data-centers-world/
16
17. What does a datacenter look like
from inside?
• A virtual walk through a datacenter
• Reference: http://gigaom.com/cleantech/a-rare-look-
inside-facebooks-oregon-data-center-photos-video/
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19. Power
Off-site
On-site
•WUE = Annual Water Usage / IT Equipment Energy (L/kWh) – low is good
•PUE = Total facility Power / IT Equipment Power – low is good
(e.g., Google~1.1)
19
20. Cooling
Air sucked in from top (also, Bugzappers) Water purified
Water sprayed into air 15 motors per server bank
20
21. II. On-demand access: *aaS
Classification
On-demand: renting a cab vs. (previously) renting a car, or buying one. E.g.:
– AWS Elastic Compute Cloud (EC2): a few cents to a few $ per CPU hour
– AWS Simple Storage Service (S3): a few cents per GB-month
• HaaS: Hardware as a Service
– You get access to barebones hardware machines, do whatever you want with them,
Ex: Your own cluster
– Not always a good idea because of security risks
• IaaS: Infrastructure as a Service
– You get access to flexible computing and storage infrastructure. Virtualization is one
way of achieving this (cgroups, Kubernetes, Dockers, VMs,…). Often said to
subsume HaaS.
– Ex: Amazon Web Services (AWS: EC2 and S3), OpenStack, Eucalyptus, Rightscale,
Microsoft Azure, Google Cloud. 21
22. II. On-demand access: *aaS
Classification
• PaaS: Platform as a Service
– You get access to flexible computing and storage infrastructure,
coupled with a software platform (often tightly coupled)
– Ex: Google’s AppEngine (Python, Java, Go)
• SaaS: Software as a Service
– You get access to software services, when you need them. Often
said to subsume SOA (Service Oriented Architectures).
– Ex: Google docs, MS Office 365 Online
22
23. III. Data-intensive Computing
• Computation-Intensive Computing
– Example areas: MPI-based, High-performance computing, Grids
– Typically run on supercomputers (e.g., NCSA Blue Waters)
• Data-Intensive
– Typically store data at datacenters
– Use compute nodes nearby
– Compute nodes run computation services
• In data-intensive computing, the focus shifts from computation to the data:
CPU utilization no longer the most important resource metric, instead I/O is
(disk and/or network)
23
24. IV. New Cloud Programming
Paradigms
• Easy to write and run highly parallel programs in new cloud programming
paradigms:
– Google: MapReduce and Sawzall
– Amazon: Elastic MapReduce service (pay-as-you-go)
– Google (MapReduce)
• Indexing: a chain of 24 MapReduce jobs
• ~200K jobs processing 50PB/month (in 2006)
– Yahoo! (Hadoop + Pig)
• WebMap: a chain of several MapReduce jobs
• 300 TB of data, 10K cores, many tens of hours (~2008)
– Facebook (Hadoop + Hive)
• ~300TB total, adding 2TB/day (in 2008)
• 3K jobs processing 55TB/day
– Similar numbers from other companies, e.g., Yieldex, eharmony.com, etc.
– NoSQL: MySQL is an industry standard, but Cassandra is 2400 times faster! 24
25. Two Categories of Clouds
• Can be either a (i) public cloud, or (ii) private cloud
• Private clouds are accessible only to company employees
• Public clouds provide service to any paying customer
• You’re starting a new service/company: should you use a public cloud
or purchase your own private cloud?
25
26. Single site Cloud: to Outsource
or Own?
• Medium-sized organization: wishes to run a service for M months
– Service requires 128 servers (1024 cores) and 524 TB
– Same as UIUC CCT (Cloud Computing Testbed) cloud site (bought in 2009, now
decommissioned)
• Outsource (e.g., via AWS): monthly cost
– S3 costs: $0.12 per GB month. EC2 costs: $0.10 per CPU hour (costs from 2009)
– Storage = $ 0.12 X 524 X 1000 ~ $62 K
– Total = Storage + CPUs = $62 K + $0.10 X 1024 X 24 X 30 ~ $136 K
• Own: monthly cost
– Storage ~ $349 K / M
– Total ~ $ 1555 K / M + 7.5 K (includes 1 sysadmin / 100 nodes)
• using 0.45:0.4:0.15 split for hardware:power:network and 3 year
lifetime of hardware 26
27. Single site Cloud: to Outsource
or Own?
• Breakeven analysis: more preferable to own if:
- $349 K / M < $62 K (storage)
- $ 1555 K / M + 7.5 K < $136 K (overall)
Breakeven points
- M > 5.55 months (storage)
- M > 12 months (overall)
- As a result
- Startups use clouds a lot
- Cloud providers benefit monetarily most from storage
27
30. Public Research Clouds
• Accessible to researchers with a qualifying grant
• Chameleon Cloud: https://www.chameleoncloud.org/
• HaaS
• OpenStack (~AWS)
• CloudLab: https://www.cloudlab.us/
• Build your own cloud on their hardware
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31. Summary
• Clouds build on many previous generations of distributed
systems
• Especially the timesharing and data processing industry of
the 1960-70s.
• Need to identify unique aspects of a problem to classify it as
a new cloud computing problem
– Scale, On-demand access, data-intensive, new programming
• Otherwise, the solutions to your problem may already exist!
• Next: Mapreduce!
31