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Lecture 2: Introduction to Cloud Computing

Lecture 2: Introduction to Cloud Computing. Xiaowei Yang (Duke University). Roadmap. What is Cloud Computing? Why now, not then? Classes of Cloud Computing Cloud Computing Economics: why does it make sense? Obstacles and (research) opportunities. What is cloud computing.

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Lecture 2: Introduction to Cloud Computing

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  1. Lecture 2: Introduction to Cloud Computing Xiaowei Yang (Duke University)

  2. Roadmap • What is Cloud Computing? • Why now, not then? • Classes of Cloud Computing • Cloud Computing Economics: why does it make sense? • Obstacles and (research) opportunities

  3. What is cloud computing • Applications run on clouds (Software as a Service) • Hardware and system software in the datacenters that provide the services • An old concept: computing as a utility • No need to purchase your hardware • Pay-as-you-go

  4. Cloud Computing = SaaS + UtilityComputing – PrivateClouds • Private • A business’s internal datacenters • No public access • Name a few companies that own private clouds • Public • Pay-as-you-go public services • Name a few public cloud providers

  5. Who’s whom

  6. Is Cloud Computing Win-Win? • SaaS advantages to providers • Simple management and maintanence • Centralized control over versioning • SaaS Advantages to users • Always on service • Easy data sharing and collaboration • Robust data storage • Simple management • …

  7. Advantages of utility computing to users • On demand scaling (elasticity) • No up-front commitment • Pay-as-you-go reduces provisioning risk

  8. Examples • When Animotomade its service available via Facebook, it experienced a demand surge that resulted in growing from 50 servers to 3500 servers in three days. … After the peak subsided, traffic fell to a level that was well below the peak. • With traditional computing  buy servers  idle servers • With cloud computing  pay during peaks  release afterwards

  9. Incentives for cloud providers • Making money • Wholesale (10,000s) at a larger scale is 5-7 times cheaper than retail at a medium size (100s - 1000s) • Resource multiplexing • Leveraging existing investment • Companies may already build private clouds for other businesses • Defend a franchise • Migrating existing customers to a cloud

  10. Attacking an incumbent • Google vs MS • Leveraging customer relationships • E.g. IBM • Preserving relationships by offering a branded cloud computing service • Becoming a platform • More customers  more $$

  11. Why now? • Two enablers: • New business model: pay-as-you-go with no contract • Intel Computing Service in 2000-2001 required a contract and longer-term use and failed • Customers do not like commitment • New applications • Mobile + cloud • Parallel batch processing: tons of data today • Analytics • Compute-intensive desktop applications

  12. Classes of Utility Computing • Infrastructure as a service (IaaS) • Thin API, close to bare metal • Virtual machines with customized guest OSes • Applications run on virtual machines using OS APIs • E.g. Amazon EC2 • Platform as a service (PaaS) • Sandbox environment with specific platform APIs • E.g. Google AppEngine • A mixture of both • Microsoft Azure

  13. Economic benefits • Elasticity • Peak demand: 500 servers • Average demand: 300 servers • Q: when does it make sense to use a cloud?

  14. Reducing underprovisioning risk • Poor performance turns customers away

  15. Real world examples • Target uses AWS • Other retailers use it during holiday seasons

  16. Rule of Thumb • UserHourscloud x (revenue – Costcloud) >= UserHoursdatacenter * (revenue – Costdatacenter/Utilization) • Why Costdatacenter/Utilization? • Do UserHourscloud == UserHoursdatacenter

  17. Comparing costs

  18. When not to use a cloud? • Utilization = 100% • Shipping large amount of data

  19. Obstacles and Opportunities • Availability • Single point of failure • Mega-Cloud to improve reliability • Elasticity to defend against DoS attacks • Ex. 500,000 bots at $0.03 per bot, 1GB/s attack traffic • Victim: $360 per hour in bandwidth and $100 of computation, (500 bots per instance) • Attack must last long (>32 hours) • Make bots detectable

  20. Obstacles and Opportunities • Data Lock-in • Not a pure technical problem • Marketing strategy •  Standardarization • Data confidentiality and auditability • Technical challenge • Encryption would help

  21. Obstacles and Opportunities • Data transfer bottlenecks • Need creative solutions • FedEx • Keep data local to a cloud • Cheap long haul bandwidth by reducing high-end router cost • 2/3 of bandwidth cost is from routers

  22. Obstacles and Opportunities • Performance variation caused by I/O sharing • More research

  23. Obstacles and opportunities • Scalable storage • Research to build scalable storage systems • Bugs • Debuggers, tracers • Scaling quickly • Research • Reputation fate sharing • Spammers used EC2 • All services sharing their IP addresses got blacklisted • Research

  24. Obstacles and opportunities • Software licensing • Not pure technical challenges • Commercial software’s licensing model not good for utility computing • One time purchase vs pay-as-you-go • Opportunities • New licensing models • New sales models • Open source software!

  25. Summary • What is cloud computing • SaaS + Utility Computing – Private Cloud • Enablers • Business models • New applications • Advantages • Economic benefits • Challenges and opportunities • Technical • Non-technical

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