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Distributed Systems Foundations

Distributed Systems Foundations. Lecture 0. Evolution of computing history. Main Frame with terminals Network of PCs & Workstations . Client-Server Now, moving forward to Large cloud . Cloud Reality: Data Centers. Cloud Computing: Why Now?. Experience with very large datacenters

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Distributed Systems Foundations

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  1. Distributed Systems Foundations Lecture 0

  2. Evolution of computing history • Main Frame with terminals • Network of PCs & Workstations. • Client-Server • Now, moving forward to Large cloud. CS 271

  3. Cloud Reality: Data Centers CS 271

  4. Cloud Computing: Why Now? • Experience with very large datacenters • Unprecedented economies of scale • Transfer of risk • Technology factors • Pervasive broadband Internet • Maturity in Virtualization Technology • Business factors • Minimal capital expenditure • Pay-as-you-go billing model CS 271

  5. Economics of Cloud Users • Pay by use instead of provisioning for peak Capacity Resources Resources Capacity Demand Demand Time Time Static data center Data center in the cloud Unused resources CS 271 Slide Credits: Berkeley RAD Lab

  6. Economics of Cloud Users • Risk of over-provisioning: underutilization Unused resources Capacity Resources Demand Time Static data center CS 271 Slide Credits: Berkeley RAD Lab

  7. Economics of Cloud Users • Heavy penalty for under-provisioning Resources Resources Resources Capacity Capacity Capacity Lost revenue Demand Demand Demand 2 2 2 3 3 3 1 1 1 Time (days) Time (days) Time (days) Lost users CS 271 Slide Credits: Berkeley RAD Lab

  8. Cloud Properties • Commodity hardware • Large Scale • Elasticity CS 271

  9. Elasticity in the Cloud Client Site Client Site Client Site Load Balancer (Proxy) App Server App Server App Server App Server App Server CS 271

  10. Why does this work? • As long as requests are stateless, we can add more resources, thus providing: • Scale • Elasticity CS 271

  11. But, most services need DATA! • Challenges: • How to scale with the increasing amounts of data • Where to store the data • Accessing data on multiple sites • Failures CS 271

  12. Need • Fault-tolerance: • Replication • Large scale data: • Partition data across multiple servers • Multiple Servers: • Time: clocks • Coordination: mutual exclusion, leader election • Consensus:Byzatine agreement, Paxos, etc • Distributed state • P2P CS 271

  13. Explosive Data Growth • Wikipedia has over 3.5 million pages. • Flickrmembers uploaded over 5 billion photos • You Tube has 35 hours of videos uploaded each minute • “more video uploaded to YouTube in the past two months than there would have been if ABC, CBS, and NBC had been airing 24/7 since 1948!” Gartner 2010 CS 271

  14. Twitter 6thB’day: March 21, 2012 • First tweet: "inviting coworkers“, 2006 • a record of 6,939 tweets per second immediately after Japan quake on March 11 2011, and 177 million tweets rest of the day. • It took three years, two months and a day for Twitter to get to one billion tweets. • Twitter now averages 140 million tweets a day. CS 271

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