An Overview of Cloud Computing Raghu Ramakrishnan Chief Scientist, Audience and Cloud Computing Research Fellow, Yahoo! - PowerPoint PPT Presentation

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An Overview of Cloud Computing Raghu Ramakrishnan Chief Scientist, Audience and Cloud Computing Research Fellow, Yahoo!
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An Overview of Cloud Computing Raghu Ramakrishnan Chief Scientist, Audience and Cloud Computing Research Fellow, Yahoo!

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  1. An Overview of Cloud ComputingRaghu RamakrishnanChief Scientist, Audience and Cloud ComputingResearch Fellow, Yahoo! Research Reflects many discussions with: Eric Baldeschwieler, Jay Kistler, Chuck Neerdaels, Shelton Shugar, and Raymie Stata and joint work with the Sherpa team, in particular: Brian Cooper, Utkarsh Srivastava, Adam Silberstein and Nick Puz in Y! Research Chuck Neerdaels, P.P. Suryanarayanan and many others in CCDI

  2. Yahoo! Research Raghu Ramakrishnan Brian Cooper Utkarsh Srivastava Adam Silberstein Nick Puz Rodrigo Fonseca CCDI Chuck Neerdaels P.P.S. Narayan Kevin Athey Toby Negrin Plus Dev/QA teams CCDI—Research Collaboration

  3. SCENARIOS Pie-in-the-sky

  4. Living in the Clouds • We want to start a new website, FredsList.com • Our site will provide listings of items for sale, jobs, etc. • As time goes on, we’ll add more features • And illustrate how more cloud capabilities (and corresponding infrastructure components) are used as needed • List of capabilities/components is illustrative, not exhaustive • Our cloud provides a “dataset” abstraction • FredsList doesn’t worry about the underlying components

  5. Step 1: Listings FredsList wants to store listings as (key, category, description) FredsList.com application DECLARE DATASET Listings AS ( ID String PRIMARY KEY, Category String, Description Text ) 5523442, childcare, Nanny available in San Jose 1234323, transportation, For sale: one bicycle, barely used 215534, wanted, Looking for issue 1 of Superman comic book Simple Web Service API’s Database Sherpa

  6. Step 2: Search FredsList’s customers quickly ask for keyword search FredsList.com application ALTER Listings SET Description SEARCHABLE “dvd’s” “bicycle” “nanny” Simple Web Service API’s Database Search Sherpa Vespa Messaging YMB

  7. Step 3: Photos FredsList decides to add photos to listings FredsList.com application ALTER Listings ADD Photo BLOB Simple Web Service API’s Storage Database Search Foreign key photo → listing MObStor Sherpa Vespa Messaging YMB

  8. Step 4: Data Analysis FredsList wants to analyze its listings to get statistics about category, do geocoding, etc. FredsList.com application ALTER Listings MAKE ANALYZABLE Hadoop program to generate fancy pages for listings Hadoop program to geocode data Pig query to analyze categories Simple Web Service API’s Storage Compute Database Search Foreign key photo → listing MObStor Grid Sherpa Vespa Messaging YMB Batch export

  9. Step 5: Performance FredsList wants to reduce its data access latency FredsList.com application ALTER Listings MAKE CACHEABLE Simple Web Service API’s Storage Compute Database Caching Search Foreign key photo → listing MObStor Grid Sherpa memcached Vespa Messaging YMB Batch export

  10. EYES TO THE SKIES Motherhood-and-Apple-Pie

  11. Why Clouds? • On-demand infrastructure to create a fundamental shift in the OE curve. Let’s us: • Do things we can’t do • Reduce time to market • Build more robustly, more efficiently, more globally, more completely, for a given budget • Cloud services should do heavy lifting of heavy-lifting of scaling & high-availability • Today, this is done at the app-level, which is not productive

  12. Requirements for Cloud Services • Multitenant. A cloud service must support multiple, organizationally distant customers. • Elasticity. Tenants should be able to negotiate and receive resources/QoS on-demand. • Resource Sharing. Ideally, spare cloud resources should be transparently applied when a tenant’s negotiated QoS is insufficient, e.g., due to spikes. • Horizontal scaling. It should be possible to add cloud capacity in small increments; this should be transparent to the tenants of the service. • Metering. A cloud service must support accounting that reasonably ascribes operational and capital expenditures to each of the tenants of the service. • Security. A cloud service should be secure in that tenants are not made vulnerable because of loopholes in the cloud. • Availability. A cloud service should be highly available. • Operability. A cloud service should be easy to operate, with few operators. Operating costs should scale linearly or better with the capacity of the service.

  13. Types of Cloud Services • Two kinds of cloud services: • Horizontal Cloud Services • Functionality enabling tenants to build applications or new services on top of the cloud • Functional Cloud Services • Functionality that is useful in and of itself to tenants. E.g., various SaaS instances, such as Saleforce.com; Google Analytics and Yahoo!’s IndexTools; Yahoo! properties aimed at end-users and small businesses, e.g., flickr, Groups, Mail, News, Shopping • Could be build on top of horizontal cloud services or from scratch • Yahoo! has been offering these for a long while (e.g., Mail for SMB, Groups, Flickr, BOSS, Ad exchanges)

  14. Horizontal Cloud Services • Horizontal cloudservices are foundations on which tenants build applications or new services. They should be: • Semantics-free. Must be "generic infrastructure,” and not tied to specific app-logic. • May provide the ability to inject application logic through well-defined APIs • Broadly applicable. Must be broadly applicable (i.e., it can't be intended for just one or two properties). • Fault-tolerant over commodity hardware. Must be built using inexpensive commodity hardware, and should mask component failures. • While each cloud service provides value, the power of the cloud paradigm will depend on a collection of well-chosen, loosely coupled services that collectively make it easy to quickly develop and operate innovative web applications.

  15. What’s in the Horizontal Cloud? Security Simple Web Service API’s Horizontal Cloud Services Provisioning & Virtualization e.g., EC2 Batch Storage & Processing e.g., Hadoop & Pig Operational Storage e.g., S3, MObStor, Sherpa Edge Content Services e.g., YCS, YCPI Other Services Messaging, Workflow, virtual DBs & Webserving ID & Account Management Shared Infrastructure Metering, Billing, Accounting Monitoring & QoS Common Approaches to QA, Production Engineering, Performance Engineering, Datacenter Management, and Optimization

  16. Yahoo! CCDI Thrust Areas • Fast Provisioning and Machine Virtualization: On demand, deliver a set of hosts imaged with desired software and configured against standard services • Multiple hosts may be multiplexed onto the same physical machine. • Batch Storage and Processing: Scalable data storage optimized for batch processing, together with computational capabilities • Operational Storage: Persistent storage that supports low-latency updates and flexible retrieval • Edge Content Services: Support for dealing with network topology, communication protocols, caching, and BCP Rest of today’s talk

  17. Hadoop: Batch Storage/Analysis Why is batch processing important? • Whether it’s • response-prediction for advertising • machine-learned relevance for Search, or • content optimization for audience, • data-intensive computing is increasingly central to everything Yahoo! does • Hadoop is central to addressing this need • Hadoop is a case-study in our cloud vision • Processes enormous amounts of data • Provides horizontal scaling and fault-tolerance for our users • Allows those users to focus on their app logic [Workflow] High-level query layer (Pig) Map-Reduce HDFS

  18. SHERPA To Help You Scale Your Mountains of Data

  19. The Yahoo! Storage Problem • Small records – 100KB or less • Structured records - tens, hundreds or thousands of fields • Extreme data scale - Tens of TB • Extreme request scale - Tens of thousands of requests/sec • Low latency globally - 20+ datacenters worldwide • High Availability - outages cost $millions • Variable usage patterns - as applications and users change 19

  20. The Sherpa Solution The next generation global-scale record store • Record-orientation: Routing, data storage optimized for low-latency record access • Scale out: Add machines to scale throughput (while keeping latency low) • Asynchrony: Pub-sub replication to far-flung datacenters to mask propagation delay • Consistency model: Reduce complexity of asynchrony for the application programmer • Cloud deployment model: Hosted, managed service to reduce app time-to-market and enable on demand scale and elasticity 20

  21. What is Sherpa? A 42342 E A 42342 E B 42521 W B 42521 W C 66354 W D 12352 E F 15677 E A 42342 E E 75656 C B 42521 W C 66354 W C 66354 W D 12352 E D 12352 E E 75656 C E 75656 C F 15677 E F 15677 E CREATE TABLE Parts ( ID VARCHAR, StockNumber INT, Status VARCHAR … ) Structured, flexible schema Geographic replication Parallel database Hosted, managed infrastructure 21

  22. A 42342 E A 42342 E A 42342 E B 42521 W B 42521 W B 42521 W C 66354 W C 66354 W C 66354 W D 12352 E D 12352 E D 12352 E E 75656 C E 75656 C E 75656 C F 15677 E F 15677 E F 15677 E What Will Sherpa Become? Indexes and views CREATE TABLE Parts ( ID VARCHAR, StockNumber INT, Status VARCHAR … ) Geographic replication Parallel database Structured, flexible schema Hosted, managed infrastructure

  23. Sherpa Design Goals Consistency Per-record guarantees Timeline model Option to relax if needed Multiple access paths Hash table, ordered table Primary, secondary access Hosted service Applications plug and play Share operational cost Scalability Thousands of machines Easy to add capacity Restrict query language to avoid costly queries Geographic replication Asynchronous replication around the globe Low-latency local access High availability and fault tolerance Automatically recover from failures Serve reads and writes despite failures 23

  24. Technology Elements Applications Tabular API PNUTS API • PNUTS • Query planning and execution • Index maintenance • Distributed infrastructure for tabular data • Data partitioning • Update consistency • Replication YCA: Authorization • YDOT FS • Ordered tables • YDHT FS • Hash tables • YMB • Pub/sub messaging • Zookeeper • Consistency service 24

  25. Data Manipulation Per-record operations Get Set Delete Multi-record operations Multiget Scan Getrange Web service (RESTful) API 25

  26. Tablets—Hash Table Name Description Price 0x0000 $12 Grape Grapes are good to eat $9 Limes are green Lime $1 Apple Apple is wisdom $900 Strawberry Strawberry shortcake 0x2AF3 $2 Orange Arrgh! Don’t get scurvy! $3 Avocado But at what price? Lemon How much did you pay for this lemon? $1 $14 Is this a vegetable? Tomato 0x911F $2 The perfect fruit Banana $8 Kiwi New Zealand 0xFFFF 26

  27. Tablets—Ordered Table Name Description Price A $1 Apple Apple is wisdom $3 Avocado But at what price? $2 Banana The perfect fruit $12 Grape Grapes are good to eat H $8 Kiwi New Zealand Lemon $1 How much did you pay for this lemon? Limes are green Lime $9 $2 Orange Arrgh! Don’t get scurvy! Q $900 Strawberry Strawberry shortcake $14 Is this a vegetable? Tomato Z 27

  28. Flexible Schema

  29. Detailed Architecture Remote regions Local region Clients REST API Routers YMB Tablet controller Storage units 29

  30. Tablet Splitting and Balancing Storage unit Tablet Each storage unit has many tablets (horizontal partitions of the table) Storage unit may become a hotspot Tablets may grow over time Overfull tablets split Shed load by moving tablets to other servers 30

  31. QUERY PROCESSING 31

  32. Accessing Data Record for key k Get key k Record for key k 1 2 3 4 Get key k SU SU SU 32

  33. Bulk Read {k1, k2, … kn} Get k1 Get k2 Get k3 Scatter/ gather server 1 2 SU SU SU 33

  34. Storage unit 1 Canteloupe Storage unit 3 Lime Storage unit 2 Strawberry Storage unit 1 Grapefruit…Pear? Grapefruit…Lime? Storage unit 1 Canteloupe Storage unit 3 Lime Storage unit 2 Strawberry Storage unit 1 Lime…Pear? Router Storage unit 1 Storage unit 2 Storage unit 3 Range Queries in YDOT • Clustered, ordered retrieval of records Apple Avocado Banana Blueberry Canteloupe Grape Kiwi Lemon Lime Mango Orange Strawberry Tomato Watermelon Apple Avocado Banana Blueberry Strawberry Tomato Watermelon Lime Mango Orange Canteloupe Grape Kiwi Lemon

  35. Updates Write key k SU SU SU 6 5 2 4 1 8 7 3 Sequence # for key k Write key k Routers Message brokers Write key k Sequence # for key k SUCCESS Write key k 35

  36. ASYNCHRONOUS REPLICATION AND CONSISTENCY 36

  37. Asynchronous Replication 37

  38. Goal: make it easier for applications to reason about updates and cope with asynchrony What happens to a record with primary key “Brian”? Consistency Model Record inserted Delete Update Update Update Update Update Update Update v. 2 v. 5 v. 1 v. 3 v. 4 v. 6 v. 7 v. 8 Time Time Generation 1 38

  39. Consistency Model Read Stale version Current version Stale version v. 2 v. 5 v. 1 v. 3 v. 4 v. 6 v. 7 v. 8 Time Generation 1 39

  40. Consistency Model Read up-to-date Stale version Current version Stale version v. 2 v. 5 v. 1 v. 3 v. 4 v. 6 v. 7 v. 8 Time Generation 1 40

  41. Consistency Model Read ≥ v.6 Stale version Current version Stale version v. 2 v. 5 v. 1 v. 3 v. 4 v. 6 v. 7 v. 8 Time Generation 1 41

  42. Consistency Model Write Stale version Current version Stale version v. 2 v. 5 v. 1 v. 3 v. 4 v. 6 v. 7 v. 8 Time Generation 1 42

  43. Consistency Model Write if = v.7 ERROR Stale version Current version Stale version v. 2 v. 5 v. 1 v. 3 v. 4 v. 6 v. 7 v. 8 Time Generation 1 43

  44. Consistency Model Mechanism: per record mastership Write if = v.7 ERROR Stale version Current version Stale version v. 2 v. 5 v. 1 v. 3 v. 4 v. 6 v. 7 v. 8 Time Generation 1 44

  45. Mastering A 42342 E B 42521 W C 66354 W D 12352 E E 75656 C F 15677 E A 42342 E B 42521 W Tablet master C 66354 W D 12352 E E 75656 C F 15677 E A 42342 E B 42521 W C 66354 W D 12352 E E 75656 C F 15677 E 46

  46. Bulk Insert/Update/Replace • Client feeds records to bulk manager • Bulk loader transfers records to SU’s in batches • Bypass routers and message brokers • Efficient import into storage unit Client Bulk manager Source Data

  47. Bulk Load in YDOT • YDOT bulk inserts can cause performance hotspots • Solution: preallocate tablets

  48. Index Maintenance • How to have lots of interesting indexes, without killing performance? • Solution: Asynchrony! • Indexes updated asynchronously when base table updated Planned functionality

  49. SHERPAIN CONTEXT 50

  50. MObStor Yahoo!’s next-generation globally replicated, virtualized media object storage service Better provisioning, easy migration, replication, better BCP, and performance New features (Evergreen URLs, CDN integration, REST API, …) The object metadata problem addressed using Sherpa, though MObStor is focused on blob storage. 51