Cloud Computing and High Performance Networking

1. Cloud Computing and High Performance Networking David Irwin Computer Science Department University of Massachusetts, Amherst

2. Cloud Computing • Wikipedia: “Internet-based computing, whereby shared resources, software and information are provided to computers and other devices on-demand, like the electricity grid”

3. Cloud Computing • Shared resources == lots of computers in data centers • Benefit from “Economy of Scale” • Cost per unit falls as scale increases • I.e., like Costco but for computers and software services

4. Outline • Virtualized Data Centers • The foundation for cloud computing • Hardware Virtualization • The foundation for virtualized data centers • Public/private Cloud Computing • Relevance to education • Shared testbeds • NSF’s GENI prototype

5. Data Center Overview • Large Server and Storage Farms • Used by enterprises to run server applications • Used by Internet companies • Google, Facebook, YouTube, Amazon • Size varies depending on needs • Architecture • Traditional: applications run on physical servers • Manual mapping of applications to servers • IT admins deal with “change” • Modern: virtualized data centers • Application runs inside of virtual servers; VM mapped to physical servers • Provides flexibility in mapping from virtual to physical resources

6. Virtualized Data Center • Simplifies resource management • Application started from preconfigured VM images, e.g., virtual appliances • Virtualization layer permits resource allocations to vary dynamically • Migrate VMs between physical machines with no down-time • Workload management • Internet applications  dynamic workloads • How much capacity to allocate to applications? • Traditional approach: IT admins estimate peak workloads and provision sufficient servers • Flash crowd  react manually by adding capacity • Time scale of hours: lost revenue, bad publicity for application

7. Dynamic Provisioning • Track workload and dynamically provision capacity • Monitor  Predict  Provision • Predictive versus reactive provisioning • Predictive: predict future workload and provision • Reactive: react whenever capacity falls short of demand • Traditional data centers: bring up a new server • Borrow from free pool or reclaim under-used server • Virtualized data center: exploit virtualization to speed up application startup time

8. Outline • Virtualized Data Centers • The foundation for cloud computing • Hardware Virtualization • The foundation for virtualized data centers • Public/private Cloud Computing • Relevance to education • Shared testbeds • NSF’s GENI prototype

9. Virtual machines are hotHeadlines from August 2007 VMware IPO: $19.1 billion Xen sale: $500 million

10. Traditional OS Structure App App App App Operating System Host Machine

11. OS abstractions Applications OS “Kernel library” Virtual Memory Threads Virtual addrs Physical mem Syst calls I/O devices Instructions CPU Hardware What are the interfaces and the resources? What is being virtualized?

12. OS abstractions Applications OS “Kernel library” Virtual Memory Threads Virtual addrs Physical mem Syst calls I/O devices Instructions CPU Hardware What are the interfaces and the resources? What is being virtualized?

13. Virtual Machine Structure Guest App Guest App Guest App Guest OS Guest OS Guest OS Virtual Machine Monitor (Hypervisor) Host Machine

14. Why are VMs useful? Code reuse Can run old operating systems + apps on new hardware Original purpose of VMs by IBM in the 60s Encapsulation Can put entire state of an “application” in one thing Move it, restore it, copy it, etc Isolation, security All interactions with hardware are mediated Hypervisor can keep one VM from affecting another Hypervisor cannot be corrupted by guest operating systems

15. Encapsulation Say I want to suspend/restore an application I decide to write the process memory to disk I reboot my kernel and restart the process Will this work? No, application state is spread out in many places Application might involve multiple processes Applications have state in the kernel (lost on reboot) (e.g. open files, locks, process ids, driver states, etc)

16. Encapsulation Virtual machines capture all of this state Can suspend/restore an application On same machine between boots On different machines Very useful in server farms As we discussed earlier

17. Examples Full Virtualization Run any OS; expose full x86 ISA E.g., VMware, Xen on HVM Para-virtualization Run any (slightly modified) OS; expose (slightly modified) x86 ISA E.g, Xen OS-level virtualization Run multiple copies of same OS E.g, VServers, User-mode Linux

18. Outline • Virtualized Data Centers • The foundation for cloud computing • Hardware Virtualization • The foundation for virtualized data centers • Public/private Cloud Computing • Relevance to education • Shared testbeds • NSF’s GENI prototype

19. Types of Cloud Computing • Implementations at different levels of abstraction • Analagous to a software stack: hardware OSapplication • Lower-level == more difficult to use + more freedom to innovate • Software-as-a-Service • E.g., Gmail, Google Calendar • Platform-as-a-Service • E.g., Azure, AppEngine • Infrastructure-as-a-Service • E.g., Amazon EC2, S3, EBS

20. Types of Cloud Computing • Implementations at different levels of abstraction • Analagous to a software stack: hardware OSapplication • Lower-level == more difficult to use + more freedom to innovate • Software-as-a-Service • E.g., Gmail, Google Calendar • Platform-as-a-Service • E.g., Azure, AppEngine • Infrastructure-as-a-Service • E.g., Amazon EC2, S3, EBS Focus of much of this talk. Good for classroom, since it provides most freedom to innovate

21. Cloud Computing Benefits • Low upfront capital expenditure • No need to buy, power, cool, or maintain hardware • Cheaper for small or short-term operations • E.g., educators teaching project-based courses • Predictable costs • Flat fees for usage, e.g., $/hour • Nice for fixed budgets: $250 class computing budget • Computing budgets are hard to predict • Flexible pricing plans • On-demand: pay $0.10 every hour, quit at anytime • Spot: use computers when price <=$0.10/hour • Reserved: reserve computers for long period at $0.05/hour

22. Example: Amazon EC2

23. Types of Amazon Resources • Elastic Compute Cloud (EC2) ($0.085/hour) • Elastic IPs • AutoScaling • CloudWatch ($0.015/hour) • Elastic MapReduce • Elastic LoadBalancing • Simple Storage Service (S3) ($0.15/GB-month) • Elastic Block Store (EBS) ($0.10/GB-month) • Elastic Snapshots • SimpleDB • Prices are continuing to fall as usage increases…..

24. Educators and Cloud Computing • Excellent platforms for experimentation • Don’t care if students “break” things • Give root access to machine • Install arbitrary software • Students isolated from other students/users • Enables distributed application projects • Can access many machines for short time-period • E.g., Class of 20 working in pairs developing an application that runs over 5 machines  need 50 computers! • Costs $500 for a two week assignment (via Amazon Spot Pricing) • Also can use “free” testbeds, e.g., PlanetLab, GENI

25. Importance of Distributed Apps • Internet services use many computers to serve Internet users • E.g., Google, Facebook, YouTube, Yahoo, Microsoft • Services may use thousands of computers running at multiple data centers throughout the world • Importance of these applications is still increasing at a rapid rate • Important for students to learn how to develop distributed applications in this environment • Traditionally a difficult task, since access is expensive

26. Private Clouds • If you already own a lot of mostly idle machines you can install private cloud software • Make your own infrastructure look like a cloud • Don’t get the low cost benefits….. • ….but maybe make your infrastructure more flexible or usable • Good for class project maintenance if you have the time to invest to learn and install software • Example: Eucalyptus, emulates Amazon EC2 interfaces

27. Outline • Virtualized Data Centers • The foundation for cloud computing • Hardware Virtualization • The foundation for virtualized data centers • Public/private Cloud Computing • Relevance to education • Shared testbeds • NSF’s GENI prototype

28. Shared Testbeds • Run by universities and companies to experiment with new research prototypes • Often are “free”: may need to contribute a few computers • Formed from donations by participants • Not as stable as commercial clouds • Focus on one or more characteristics • Compute isolation • Network isolation • Geographic diversity • Hardware diversity • Examples: PlanetLab, Emulab, NSF’s Global Environment for Network Innovations (GENI)

29. PlanetLab • Get access to machines hosted around the world • Experiment with global network services • No resource isolation • Network presence but little compute power • Success led to GENI

30. Status • GENI connects diverse testbeds together • Many components/link types • Routers, edge nodes, wireless, wired, storage, sensors, fiber-optic, etc. • Prototyping started last year (http://geni.net) • Hasn’t been built, but isn’t vaporware • Existing systems form foundation • 80 projects clustered around 4 “control frameworks” • PlanetLab, Emulab, Orca, Orbit • 4 projects at UMass-Amherst! • Run by BBN (Cambridge); also at UMass-Lowell and Williams

31. Embedding GENI Overview Experiments (Guests occupying slices) Sliverable GENI Substrate (Contributing domains/Aggregates) Observatory Wind tunnel Petri dish