![I. Massive Scale
• Facebook [GigaOm, 2012]
– 30K in 2009 -> 60K in 2010 -> 180K in 2012
• Microsoft [NYTimes, 2008]
– 150K machines
– Growth rate of 10K per month
– 80K total running Bing
– In 2013, Microsoft Cosmos had 110K machines (4 sites)
• Yahoo! [2009]:
– 100K
– Split into clusters of 4000
• AWS EC2 [Randy Bias, 2009]
– 40K machines
– 8 cores/machine
• eBay [2012]: 50K machines
• HP [2012]: 380K in 180 DCs
• Google [2011, Data Center Knowledge] : 900K
17](https://image.slidesharecdn.com/introductiontocloudcomputing-240214014050-4cd01262/85/Introduction-To-Cloud-Computing-ppt-17-320.jpg)
Introduction To Cloud Computing.ppt
- 1. CS 425 / ECE 428 Distributed Systems Fall 2019 Indranil Gupta (Indy) Lecture 2-3: Introduction to Cloud Computing All slides © IG 1
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- 3. The Hype! • Forrester in 2010 – Cloud computing will go from $40.7 billion in 2010 to $241 billion in 2020. • Goldman Sachs says cloud computing will grow at annual rate of 30% from 2013-2018 • Hadoop market to reach $20.8 B by by 2018: Transparency Market Research • Companies and even Federal/state governments using cloud computing now: fbo.gov 3
- 4. Many Cloud Providers • AWS: Amazon Web Services – EC2: Elastic Compute Cloud – S3: Simple Storage Service – EBS: Elastic Block Storage • Microsoft Azure • Google Cloud/Compute Engine/AppEngine • Rightscale, Salesforce, EMC, Gigaspaces, 10gen, Datastax, Oracle, VMWare, Yahoo, Cloudera • And many many more! 4
- 5. Two Categories of Clouds • Can be either a (i) public cloud, or (ii) private cloud • Private clouds are accessible only to company employees • Public clouds provide service to any paying customer: – Amazon S3 (Simple Storage Service): store arbitrary datasets, pay per GB-month stored • As of 2019: 0.4c-3 c per GB month – Amazon EC2 (Elastic Compute Cloud): upload and run arbitrary OS images, pay per CPU hour used • As of 2019: 0.2 c per CPU hr to $7.2 per CPU hr (depending on strength) – Google cloud: similar pricing as above – Google AppEngine/Compute Engine: develop applications within their appengine framework, upload data that will be imported into their format, and run 5
- 6. Customers Save Time and $$$ • Dave Power, Associate Information Consultant at Eli Lilly and Company: “With AWS, Powers said, a new server can be up and running in three minutes (it used to take Eli Lilly seven and a half weeks to deploy a server internally) and a 64-node Linux cluster can be online in five minutes (compared with three months internally). … It's just shy of instantaneous.” • Ingo Elfering, Vice President of Information Technology Strategy, GlaxoSmithKline: “With Online Services, we are able to reduce our IT operational costs by roughly 30% of what we’re spending” • Jim Swartz, CIO, Sybase: “At Sybase, a private cloud of virtual servers inside its datacenter has saved nearly $US2 million annually since 2006, Swartz says, because the company can share computing power and storage resources across servers.” • 100s of startups in Silicon Valley can harness large computing resources without buying their own machines. 6
- 7. But what exactly IS a cloud? 7
- 8. What is a Cloud? • It’s a cluster! • It’s a supercomputer! • It’s a datastore! • It’s superman! • None of the above • All of the above • Cloud = Lots of storage + compute cycles nearby 8
- 9. What is a Cloud? • A single-site cloud (aka “Datacenter”) consists of – Compute nodes (grouped into racks) (2) – Switches, connecting the racks – A network topology, e.g., hierarchical – Storage (backend) nodes connected to the network (3) – Front-end for submitting jobs and receiving client requests (1) – (1-3: Often called “three-tier architecture”) – Software Services • A geographically distributed cloud consists of – Multiple such sites – Each site perhaps with a different structure and services 9
- 10. A Sample Cloud Topology So then, what is a cluster? 10
- 11. “A Cloudy History of Time” 1940 1950 1960 1970 1980 1990 2000 Timesharing Companies & Data Processing Industry Grids Peer to peer systems Clusters The first datacenters! PCs (not distributed!) Clouds and datacenters 2012 11
- 12. “A Cloudy History of Time” 1940 1950 1960 1970 1980 1990 2000 2012 Clouds Grids (1980s-2000s): •GriPhyN (1970s-80s) •Open Science Grid and Lambda Rail (2000s) •Globus & other standards (1990s-2000s) Timesharing Industry (1975): •Market Share: Honeywell 34%, IBM 15%, •Xerox 10%, CDC 10%, DEC 10%, UNIVAC 10% •Honeywell 6000 & 635, IBM 370/168, Xerox 940 & Sigma 9, DEC PDP-10, UNIVAC 1108 Data Processing Industry - 1968: $70 M. 1978: $3.15 Billion First large datacenters: ENIAC, ORDVAC, ILLIAC Many used vacuum tubes and mechanical relays Berkeley NOW Project Supercomputers Server Farms (e.g., Oceano) P2P Systems (90s-00s) •Many Millions of users •Many GB per day 12
- 13. Trends: Technology • Doubling Periods – storage: 12 mos, bandwidth: 9 mos, and (what law is this?) cpu compute capacity: 18 mos • Then and Now – Bandwidth • 1985: mostly 56Kbps links nationwide • 2015: Tbps links widespread – Disk capacity • Today’s PCs have TBs, far more than a 1990 supercomputer 13
- 14. Trends: Users • Then and Now Biologists: – 1990: were running small single-molecule simulations – Today: CERN’s Large Hadron Collider producing many PB/year 14
- 15. Prophecies • In 1965, MIT's Fernando Corbató and the other designers of the Multics operating system envisioned a computer facility operating “like a power company or water company”. • Plug your thin client into the computing Utility and Play your favorite Intensive Compute & Communicate Application – Have today’s clouds brought us closer to this reality? Think about it. 15
- 16. Four Features New in Today’s Clouds I. Massive scale. II. On-demand access: Pay-as-you-go, no upfront commitment. – And anyone can access it III. Data-intensive Nature: What was MBs has now become TBs, PBs and XBs. – Daily logs, forensics, Web data, etc. – Humans have data numbness: Wikipedia (large) compressed is only about 10 GB! IV. New Cloud Programming Paradigms: MapReduce/Hadoop, NoSQL/Cassandra/MongoDB and many others. – High in accessibility and ease of programmability – Lots of open-source Combination of one or more of these gives rise to novel and unsolved distributed computing problems in cloud computing. 16
- 17. I. Massive Scale • Facebook [GigaOm, 2012] – 30K in 2009 -> 60K in 2010 -> 180K in 2012 • Microsoft [NYTimes, 2008] – 150K machines – Growth rate of 10K per month – 80K total running Bing – In 2013, Microsoft Cosmos had 110K machines (4 sites) • Yahoo! [2009]: – 100K – Split into clusters of 4000 • AWS EC2 [Randy Bias, 2009] – 40K machines – 8 cores/machine • eBay [2012]: 50K machines • HP [2012]: 380K in 180 DCs • Google [2011, Data Center Knowledge] : 900K 17
- 18. Quiz: Where is the World’s Largest Datacenter? 18
- 19. Quiz: Where is the World’s Largest Datacenter? • (2018) China Telecom. 10.7 Million sq. ft. • (2017) “The Citadel” Nevada. 7.2 Million sq. ft. • (2015) In Chicago! • 350 East Cermak, Chicago, 1.1 MILLION sq. ft. • Shared by many different “carriers” • Critical to Chicago Mercantile Exchange • See: – https://www.gigabitmagazine.com/top10/top-10-biggest-data-centres-world – https://www.racksolutions.com/news/data-center-news/top-10-largest-data-centers-world/ 19
- 20. What does a datacenter look like from inside? • A virtual walk through a datacenter • Reference: http://gigaom.com/cleantech/a-rare-look- inside-facebooks-oregon-data-center-photos-video/ 20
- 21. Servers Front Back In Some highly secure (e.g., financial info)21
- 22. Power Off-site On-site •WUE = Annual Water Usage / IT Equipment Energy (L/kWh) – low is good •PUE = Total facility Power / IT Equipment Power – low is good (e.g., Google~1.1) 22
- 23. Cooling Air sucked in from top (also, Bugzappers) Water purified Water sprayed into air 15 motors per server bank 23
- 24. Extra - Fun Videos to Watch • Microsoft GFS Datacenter Tour (Youtube) – http://www.youtube.com/watch?v=hOxA1l1pQIw • Timelapse of a Datacenter Construction on the Inside (Fortune 500 company) – http://www.youtube.com/watch?v=ujO-xNvXj3g 24
- 25. II. On-demand access: *aaS Classification On-demand: renting a cab vs. (previously) renting a car, or 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 – You get access to barebones hardware machines, do whatever you want with them, Ex: Your own cluster – Not always a good idea because of security risks • IaaS: Infrastructure as a Service – You get access to flexible computing and storage infrastructure. Virtualization is one way of achieving this (cgroups, Kubernetes, Dockers, VMs,…). Often said to subsume HaaS. – Ex: Amazon Web Services (AWS: EC2 and S3), OpenStack, Eucalyptus, Rightscale, Microsoft Azure, Google Cloud. 25
- 26. II. On-demand access: *aaS Classification • PaaS: Platform as a Service – You get access to flexible computing and storage infrastructure, coupled with a software platform (often tightly coupled) – Ex: Google’s AppEngine (Python, Java, Go) • SaaS: Software as a Service – You get access to software services, when you need them. Often said to subsume SOA (Service Oriented Architectures). – Ex: Google docs, MS Office 365 Online 26
- 27. III. Data-intensive Computing • Computation-Intensive Computing – Example areas: MPI-based, High-performance computing, Grids – Typically run on supercomputers (e.g., NCSA Blue Waters) • Data-Intensive – Typically store data at datacenters – Use compute nodes nearby – Compute nodes run computation services • In data-intensive computing, the focus shifts from computation to the data: CPU utilization no longer the most important resource metric, instead I/O is (disk and/or network) 27
- 28. IV. New Cloud Programming Paradigms • Easy to write and run highly parallel programs in 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, many tens of hours (~2008) – Facebook (Hadoop + Hive) • ~300TB total, adding 2TB/day (in 2008) • 3K jobs processing 55TB/day – Similar numbers from other companies, e.g., Yieldex, eharmony.com, etc. – NoSQL: MySQL is an industry standard, but Cassandra is 2400 times faster! 28
- 29. Two Categories of Clouds • Can be either a (i) public cloud, or (ii) private cloud • Private clouds are accessible only to company employees • Public clouds provide service to any paying customer • You’re starting a new service/company: should you use a public cloud or purchase your own private cloud? 29
- 30. Single site Cloud: to Outsource or Own? • Medium-sized organization: wishes to run a service for M months – Service requires 128 servers (1024 cores) and 524 TB – Same as UIUC CCT (Cloud Computing Testbed) cloud site (bought in 2009, now decommissioned) • Outsource (e.g., via AWS): monthly cost – S3 costs: $0.12 per GB month. EC2 costs: $0.10 per CPU hour (costs from 2009) – Storage = $ 0.12 X 524 X 1000 ~ $62 K – Total = Storage + CPUs = $62 K + $0.10 X 1024 X 24 X 30 ~ $136 K • Own: monthly cost – Storage ~ $349 K / M – Total ~ $ 1555 K / M + 7.5 K (includes 1 sysadmin / 100 nodes) • using 0.45:0.4:0.15 split for hardware:power:network and 3 year lifetime of hardware 30
- 31. Single site Cloud: to Outsource or Own? • Breakeven analysis: more preferable to own if: - $349 K / M < $62 K (storage) - $ 1555 K / M + 7.5 K < $136 K (overall) Breakeven points - M > 5.55 months (storage) - M > 12 months (overall) - As a result - Startups use clouds a lot - Cloud providers benefit monetarily most from storage 31
- 32. Academic Clouds: Emulab • A community resource open to researchers in academia and industry. Very widely used by researchers everywhere today. • https://www.emulab.net/ • A cluster, with currently ~500 servers • Founded and owned by University of Utah (led by Late Prof. Jay Lepreau) • As a user, you can: – Grab a set of machines for your experiment – You get root-level (sudo) access to these machines – You can specify a network topology for your cluster – You can emulate any topology All images © Emulab 32
- 33. • A community resource open to researchers in academia and industry • http://www.planet-lab.org/ • Currently, ~ 1077 nodes at ~500 sites across the world • Founded at Princeton University (led by Prof. Larry Peterson), but owned in a federated manner by the sites • Node: Dedicated server that runs components of PlanetLab services. • Site: A location, e.g., UIUC, that hosts a number of nodes. • Sliver: Virtual division of each node. Currently, uses VMs, but it could also other technology. Needed for timesharing across users. • Slice: A spatial cut-up of the PL nodes. Per user. A slice is a way of giving each user (Unix-shell like) access to a subset of PL machines, selected by the user. A slice consists of multiple slivers, one at each component node. • Thus, PlanetLab allows you to run real world-wide experiments. • Many services have been deployed atop it, used by millions (not just researchers): Application-level DNS services, Monitoring services, CoralCDN, etc. • PlanetLab is basis for NSF GENI https://www.geni.net/ All images © PlanetLab 33
- 34. Public Research Clouds • Accessible to researchers with a qualifying grant • Chameleon Cloud: https://www.chameleoncloud.org/ • HaaS • OpenStack (~AWS) • CloudLab: https://www.cloudlab.us/ • Build your own cloud on their hardware 34
- 35. Summary • Clouds build on many previous generations of distributed systems • Especially the timesharing and data processing industry of the 1960-70s. • Need to identify unique aspects of a problem to classify it as a new cloud computing problem – Scale, On-demand access, data-intensive, new programming • Otherwise, the solutions to your problem may already exist! • Next: Mapreduce! 35