Evolution of Cloud Computing.

Evolution of Cloud Computing.

Cloud Computing is one of the latest trends in application deployment and web integration, which not only reduces costs but also gives many functionalities. The main purpose of cloud computing is to give access to data centres to many users. Users can also access data from a remote server. ex: AWS, Azure, Google Cloud. 

What is Cloud?!!!

  • Cloud is nothing but a server that we access over the internet, it contains a large amount of data such as text files, video, audio, images, docs, pdfs, and so on. 

  • Advances in virtualization make it possible to see the growth of Internet clouds as a new computing paradigm.
  • It is just like developing software for millions to use as a service rather than distributing software to run on their PCs. 

Evolution Of Cloud:

  • In 1984, John Gage Sun Microsystems gave the slogan, “The network is the computer.”

  • In 2008, David Patterson UC Berkeley said, “The data centre is the computer.”

  • Rajkumar Buyya of Melbourne University simply said: “The cloud is the computer.” 

  • Some people view clouds as grids or clusters with changes through virtualization since clouds are anticipated to process huge data sets generated by the traditional Internet, social networks, and the future IoT.

  • A single-site cloud (known as a “Datacenter”) consists of 

  1. Compute nodes (grouped into racks).
  2. Switches, connecting the racks.
  3. A network topology, e.g., hierarchical.
  4. Storage (backend) nodes are connected to the network.
  5. Front-end for submitting jobs and receiving client requests. 
  6. Software Services.  

What is meant by Distributed Computing?!!

  • A distributed system consists of multiple autonomous computers, having their own memory, communicating through message passing.

What is meant by Cloud Computing?!!

  • Clouds can be built with physical or virtualized resources over large data centres that are distributed systems. Cloud computing is also considered to be a form of utility computing or service computing.

Major Categories of Cloud:

  • Can be either an (i) public cloud or (ii) private cloud.

  • Private clouds -are accessible only to company employees. 

  • Public clouds - provide a service to any paying customer:

  1. Amazon S3 (Simple Storage Service): store arbitrary datasets, pay per GB-month stored. 
  2. Amazon EC2 (Elastic Compute Cloud): upload and run arbitrary OS images, pay per CPU hour used. 
  3. Google App Engine/Compute Engine: develop applications within their App Engine framework, upload data that will be imported into their format, and run.

The Trend toward Utility Computing

  • Aim towards automatic operations that can be self-organized to support dynamic discovery. Major computing paradigms are composable with QoS and SLAs.
  • Plug your thin client into the computing Utility and play intensive computing and Communicate Applications.
  •  Utility computing focuses on a business model in which customers receive computing resources from a paid service provider.
  • All grid/cloud platforms are regarded as utility service providers.

Features of Today's Clouds

  • Massive scale: Very large data centres contain tens of thousands, sometimes hundreds of thousands of servers, and you can run your computation across as many servers as you want and as many servers as your application will scale.
  • On-demand access: Pay-as-you-go, no upfront commitment.
  • Data-intensive Nature: What were MBs has now become TBs, PBs, and XBs.
  • New Cloud Programming Paradigms: MapReduce/Hadoop, NoSQL/Cassandra/MongoDB, and many others.

Demand services

  • On-demand: renting vs. 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: Get access to barebones hardware machines, and do whatever you want with them, Ex: Your own cluster Not always a good idea because of security risks.
  • PaaS: Platform as a Service: Get access to flexible computing and storage infrastructure, coupled with a software platform (often tightly coupled) Ex: Google’s App Engine (Python, Java, Go).
  • SaaS: Software as a Service: Get access to software services, when you need them. subsume SOA (Service Oriented Architecture). Ex: Google docs, MS Office on demand.

Data-intensive Computing

  • Computation-Intensive Computing: Example areas: MPI-based, High-performance computing, Grids Typically run-on supercomputers EX: NCSA Blue Waters.
  • Data-Intensive: Typically store data at data centers Use compute nodes nearby Compute nodes run computation services.
  • In data-intensive computing, the focus shifts from computation to the data
  • CPU utilization is no longer the most important resource metric, instead I/O is disk and/or network.

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, and many tens of hours (~2008) 
  • Facebook (Hadoop + Hive): ~300TB total, adding 2TB/day (in 2008), 3K jobs processing 55TB/day 
  • NoSQL: MySQL is an industry standard, but Cassandra is 2400 times faster


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