This is a talk I gave in 2009 introducing cloud computing. It is a bit dated now.

1. An Introduction to Cloud Computing Robert Grossman December 8, 2009

2. Part 1 Introduction 2

3. What is a Cloud? Clouds provide elastic, on-demand resources or services over a network, often the Internet, with the scale and reliability of a data center. The NIST definition has become standard. Cloud architectures are not new. What is new: Scale Ease of use Pricing model. 3

4. 4 Scale is new.

7. Two Types of Clouds On-demand resources & services over a network at the scale of a data center On-demand, elastic computing instances (IaaS) IaaS: Amazon EC2, S3, etc.; Eucalyptus supports many Web 2.0 applications/users Large data clouds (Large Data PaaS) GFS/MapReduce/Bigtable, Hadoop, Sector, … Manage and compute with large data (say 100+ TB) 7

8. Ease of use – With Google’s GFS & MapReduce, it is simple to compute with 10 terabytes of data over 100 nodes. With Amazon’s AMIs, it is simple to respond to a surge of 100 additional web servers. 8

9. Cloud Architectures – How Do You Fill a Data Center? on-demand computing capacity App App App App App on-demand computing instances Cloud Data Services (BigTable, etc.) Quasi-relational Data Services App App Cloud Compute Services (MapReduce & Generalizations) App App … App App App Cloud Storage Services

10. Varieties of Clouds Architectural Model Computing Instances vs Computing Capacity Economic Model Elastic, usage based pricing, lease/own, … Management Model Private vs Public; Single vs Multiple Tenant; … Programming Model Queue Service, MPI, MapReduce, Distributed UDF 10 Computing instances vs computing capacity Private internal vspublic external Elastic, usage-based pricing or not All combinations occur.

11. Payment Models Buying racks, containers and data centers Leasing racks containers and data centers Utility based computing (pay as you go) Moves cap ex to op ex Handle surge requirements (use 1000 servers for 1 hour vs 1 server for 1000 hours) 11

12. Management Models Public, private and hybrid models Single tenant vs multiple tenant (shared vs non-shared hardware) Owned vs leased Manage yourself vs outsource management All combinations are possible 12

14. Azure services

15. MapReduce

17. Instances, Services & Frameworks 15 Hadoop DFS & MapReduce Google AppEngine Microsoft Azure Force.com VMWare Vmotion… many instances Amazon’s SQS Azure Services Amazon’s EC2 single instance S3 instance (IaaS) service framework (PaaS) operating system

18. Part 2. Cloud Computing Industry “Cloud computing has become the center of investment and innovation.”Nicholas Carr, 2009 IDC Directions 16 Cloud computing is approaching the top of the Gartner hype cycle.

19. Cloud Computing Eco-System No agreed upon terminology Vendors supporting data centers Vendors providing cloud apps & services to end users Vendors supporting the industry i.e. those developing cloud applications and services for themselves or to sell to end users Communities developing software, standards, benchmarks, etc. 17

20. Cloud Computing Ecosystem 18 Consumers of Software as a Service Providers of Software as a Service Data Centers Consumers of Cloud Services Providers of Cloud Services Berkeley RAD Report on cloud computing divides industry into these layers.

21. Transition Taking Place A hand full of players are building multiple data centers a year and improving with each one. This includes Google, Microsoft, Yahoo, … A data center today costs $200 M – $400+ M Berkeley RAD Report points out analogy with semiconductor industry as companies stopped building their own Fabs and starting leasing Fabs from others as Fabs approached $1B 19

22. Data Center Operating Systems 20 … … VM 50,000 VM 1 VM 1 VM 5 Data Center Operating System workstation Data center services include: VM management services, business continuity services, security services, power management services, etc.

23. Building Data Centers Sun’s Modular Data Center (MD) Formerly Project Blackbox Containers used by Google, Microsoft & others Data center consists of 10-60+ containers. 21

24. Mindmeister Map of Cloud Computing Dupont’sMindmeister Map divides the industry: IaaS, PaaS, Management, Community http://www.mindmeister.com/maps/show_public/15936058 22

25. Part 3 Virtualization 23

26. Virtualization Virtualization separates logical infrastructure from the underlying physical resources to decrease time to make changes, improve flexibility, improve utilization and reduce costs Example - server virtualization. Use one physical server to support multiple logical virtual machines (VMs), which are sometimes called logical partitions (LPARs) Technology pioneered by IBM in 1960s to better utilize mainframes 24

27. Idea Dates Back to the 1960s 25 App App App CMS CMS MVS IBM VM/370 IBM Mainframe Native (Full) Virtualization Examples: Vmware ESX

28. Two Types of Virtualization 26 Apps Apps Unmodified Guest OS 1 Unmodified Guest OS 2 Modified Guest OS 1 Modified Guest OS 2 Hyperviser Hyperviser Physical Hardware Physical Hardware Native (Full) Virtualization Examples: Vmware ESX Para Virtualization Examples: Xen Using the hypervisor, each guest OS sees its own independent copy of the CPU, memory, IO, etc.

29. Four Key Properties Partitioning: run multiple VMs on one physical server; one VM doesn’t know about the others Isolation: security isolation is at the hardware level. Encapsulation: entire state of the machine can be copied to files and moved around Hardware abstraction: provision and migrate VM to another server 27

30. Managing Virtual Machines Provision VM Schedule VM Monitor VM Self-service portal for VM 28

31. Part 4 Technical differences between clouds for data intensive computing, databases and supercomputers 29

32. Supercomputer Center Model or Data Center Model

33. What Resource is Managed? Scarce processors wait for data Manage cycles wait for an opening in the queue scatter the data to the processors and gather the results Persistent data wait for queries Manage data persistent data waits for queries computation done locally results returned Supercomputer Center Model (local) HPC Grid (distributed) Data Center 2.0 Model Distributed 2.0 Data Centers

34. Databases vs Data Clouds Trading functionality for scalability. 32

35. Trading Functionality for Scalability 33

36. Not Everyone Agrees David J. DeWitt and Michael Stonebraker, MapReduce: A Major Step Backwards, Database Column, Jane 17, 2008 34

37. Part 5. Standards Efforts 35 Train gauge in Russia is 1520 mm Train gauge in China is 1435 mm How can a cloud application move from one cloud storage service to another? Change of gauge at Ussuriisk (near Vladivostok) at the Chinese –Russian border

38. Standards Efforts for Clouds Distributed Management Task Force (DMTF) Storage Network Industrial Association (SNIA) Cloud Computing Interoperability Forum (CCIF) Open Cloud Consortium (OCC) Open Grid Forum (OGF) Plus several others… 36