Cloud Computing
Download as PPT, PDF0 likes548 views
This document provides an introduction to cloud computing and parallel/distributed processing through a lecture on the topic. It discusses web-scale problems involving large amounts of data, the use of large data centers to process this data, and different computing models like utility computing. It also introduces concepts like virtualization, MapReduce, and designing applications around patterns for parallelism. The overall goal of the lecture is to define cloud computing and discuss how techniques like MapReduce can help manage distributed and parallel processing of large datasets in the cloud.
Cloud Computing
- 1. Cloud Computing Lecture #1 What is Cloud Computing? (and an intro to parallel/distributed processing) Jimmy Lin The iSchool University of Maryland Wednesday, September 3, 2008 This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States See http://creativecommons.org/licenses/by-nc-sa/3.0/us/ for details Some material adapted from slides by Christophe Bisciglia, Aaron Kimball, & Sierra Michels-Slettvet, Google Distributed Computing Seminar, 2007 (licensed under Creation Commons Attribution 3.0 License)
- 3. The iSchool University of Maryland What is Cloud Computing? 1. Web-scale problems 2. Large data centers 3. Different models of computing 4. Highly-interactive Web applications
- 4. The iSchool University of Maryland 1. Web-Scale Problems Characteristics: Definitely data-intensive May also be processing intensive Examples: Crawling, indexing, searching, mining the Web “Post-genomics” life sciences research Other scientific data (physics, astronomers, etc.) Sensor networks Web 2.0 applications …
- 5. The iSchool University of Maryland How much data? Wayback Machine has 2 PB + 20 TB/month (2006) Google processes 20 PB a day (2008) “all words ever spoken by human beings” ~ 5 EB NOAA has ~1 PB climate data (2007) CERN’s LHC will generate 15 PB a year (2008) 640K ought to be enough for anybody.
- 6. Maximilien Brice, © CERN
- 7. Maximilien Brice, © CERN
- 8. The iSchool University of Maryland There’s nothing like more data! s/inspiration/data/g; (Banko and Brill, ACL 2001) (Brants et al., EMNLP 2007)
- 9. The iSchool University of Maryland What to do with more data? Answering factoid questions Pattern matching on the Web Works amazingly well Learning relations Start with seed instances Search for patterns on the Web Using patterns to find more instances Who shot Abraham Lincoln? → X shot Abraham Lincoln Birthday-of(Mozart, 1756) Birthday-of(Einstein, 1879) Wolfgang Amadeus Mozart (1756 - 1791) Einstein was born in 1879 PERSON (DATE – PERSON was born in DATE (Brill et al., TREC 2001; Lin, ACM TOIS 2007) (Agichtein and Gravano, DL 2000; Ravichandran and Hovy, ACL 2002; … )
- 10. The iSchool University of Maryland 2. Large Data Centers Web-scale problems? Throw more machines at it! Clear trend: centralization of computing resources in large data centers Necessary ingredients: fiber, juice, and space What do Oregon, Iceland, and abandoned mines have in common? Important Issues: Redundancy Efficiency Utilization Management
- 11. Source: Harper’s (Feb, 2008)
- 12. Maximilien Brice, © CERN
- 13. The iSchool University of Maryland Key Technology: Virtualization Hardware Operating System App App App Traditional Stack Hardware OS App App App Hypervisor OS OS Virtualized Stack
- 14. The iSchool University of Maryland 3. Different Computing Models Utility computing Why buy machines when you can rent cycles? Examples: Amazon’s EC2, GoGrid, AppNexus Platform as a Service (PaaS) Give me nice API and take care of the implementation Example: Google App Engine Software as a Service (SaaS) Just run it for me! Example: Gmail “Why do it yourself if you can pay someone to do it for you?”
- 15. The iSchool University of Maryland 4. Web Applications A mistake on top of a hack built on sand held together by duct tape? What is the nature of software applications? From the desktop to the browser SaaS == Web-based applications Examples: Google Maps, Facebook How do we deliver highly-interactive Web-based applications? AJAX (asynchronous JavaScript and XML) For better, or for worse…
- 16. The iSchool University of Maryland What is the course about? MapReduce: the “back-end” of cloud computing Batch-oriented processing of large datasets Ajax: the “front-end” of cloud computing Highly-interactive Web-based applications Computing “in the clouds” Amazon’s EC2/S3 as an example of utility computing
- 17. The iSchool University of Maryland Amazon Web Services Elastic Compute Cloud (EC2) Rent computing resources by the hour Basic unit of accounting = instance-hour Additional costs for bandwidth Simple Storage Service (S3) Persistent storage Charge by the GB/month Additional costs for bandwidth You’ll be using EC2/S3 for course assignments!
- 18. The iSchool University of Maryland This course is not for you… If you’re not genuinely interested in the topic If you’re not ready to do a lot of programming If you’re not open to thinking about computing in new ways If you can’t cope with uncertainly, unpredictability, poor documentation, and immature software If you can’t put in the time Otherwise, this will be a richly rewarding course!
- 20. The iSchool University of Maryland Cloud Computing Zen Don’t get frustrated (take a deep breath)… This is bleeding edge technology Those W$*#T@F! moments Be patient… This is the second first time I’ve taught this course Be flexible… There will be unanticipated issues along the way Be constructive… Tell me how I can make everyone’s experience better
- 25. The iSchool University of Maryland Things to go over… Course schedule Assignments and deliverables Amazon EC2/S3
- 26. The iSchool University of Maryland Web-Scale Problems? Don’t hold your breath: Biocomputing Nanocomputing Quantum computing … It all boils down to… Divide-and-conquer Throwing more hardware at the problem Simple to understand… a lifetime to master…
- 27. The iSchool University of Maryland Divide and Conquer “Work” w1 w2 w3 r1 r2 r3 “Result” “worker” “worker” “worker” Partition Combine
- 28. The iSchool University of Maryland Different Workers Different threads in the same core Different cores in the same CPU Different CPUs in a multi-processor system Different machines in a distributed system
- 29. The iSchool University of Maryland Choices, Choices, Choices Commodity vs. “exotic” hardware Number of machines vs. processor vs. cores Bandwidth of memory vs. disk vs. network Different programming models
- 30. The iSchool University of Maryland Flynn’s Taxonomy Instructions Single (SI) Multiple (MI) Data Multiple(M SISD Single-threaded process MISD Pipeline architecture SIMD Vector Processing MIMD Multi-threaded Programming Single(SD)
- 31. The iSchool University of Maryland SISD D D D D D D D Processor Instructions
- 32. The iSchool University of Maryland SIMD D0 Processor Instructions D0D0 D0 D0 D0 D1 D2 D3 D4 … Dn D1 D2 D3 D4 … Dn D1 D2 D3 D4 … Dn D1 D2 D3 D4 … Dn D1 D2 D3 D4 … Dn D1 D2 D3 D4 … Dn D1 D2 D3 D4 … Dn D0
- 33. The iSchool University of Maryland MIMD D D D D D D D Processor Instructions D D D D D D D Processor Instructions
- 34. The iSchool University of Maryland Memory Typology: Shared Memory Processor Processor Processor Processor
- 35. The iSchool University of Maryland Memory Typology: Distributed MemoryProcessor MemoryProcessor MemoryProcessor MemoryProcessor Network
- 36. The iSchool University of Maryland Memory Typology: Hybrid Memory Processor Network Processor Memory Processor Processor Memory Processor Processor Memory Processor Processor
- 37. The iSchool University of Maryland Parallelization Problems How do we assign work units to workers? What if we have more work units than workers? What if workers need to share partial results? How do we aggregate partial results? How do we know all the workers have finished? What if workers die? What is the common theme of all of these problems?
- 38. The iSchool University of Maryland General Theme? Parallelization problems arise from: Communication between workers Access to shared resources (e.g., data) Thus, we need a synchronization system! This is tricky: Finding bugs is hard Solving bugs is even harder
- 39. The iSchool University of Maryland Managing Multiple Workers Difficult because (Often) don’t know the order in which workers run (Often) don’t know where the workers are running (Often) don’t know when workers interrupt each other Thus, we need: Semaphores (lock, unlock) Conditional variables (wait, notify, broadcast) Barriers Still, lots of problems: Deadlock, livelock, race conditions, ... Moral of the story: be careful! Even trickier if the workers are on different machines
- 40. The iSchool University of Maryland Patterns for Parallelism Parallel computing has been around for decades Here are some “design patterns” …
- 41. The iSchool University of Maryland Master/Slaves slaves master
- 42. The iSchool University of Maryland Producer/Consumer Flow CP P P C C CP P P C C
- 43. The iSchool University of Maryland Work Queues CP P P C C shared queue W W W W W
- 44. The iSchool University of Maryland Rubber Meets Road From patterns to implementation: pthreads, OpenMP for multi-threaded programming MPI for clustering computing … The reality: Lots of one-off solutions, custom code Write you own dedicated library, then program with it Burden on the programmer to explicitly manage everything MapReduce to the rescue! (for next time)