Cloud Computing and High Performance Networking

UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTS ASSACHUSETTS AAMHERST • MHERST • Department of Computer Science Department of Computer Science • 2008 • 2008

Cloud Computing and High Performance Networking

David Irwin

Computer Science DepartmentUniversity of Massachusetts, Amherst


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”


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


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


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


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


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


Outline






Virtual machines are hotHeadlines from August 2007

VMware IPO: $19.1 billion

Xen sale: $500 million


Traditional OS Structure

Host Machine

Operating System

App App App App


OS abstractions

HardwareHardware

OSOS

ApplicationsApplications

What are the interfaces and the resources?What is being virtualized?

InstructionsCPU

Virtual addrsPhysical mem

Syst callsI/O devices


OS abstractions

HardwareHardware

OSOS

ApplicationsApplications

What are the interfaces and the resources?What is being virtualized?

InstructionsCPU

Virtual addrsPhysical mem

Syst callsI/O devices


Virtual Machine Structure

Host Machine

Virtual Machine Monitor (Hypervisor)

Guest OS

GuestApp

GuestApp

Guest OS Guest OS

GuestApp


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


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)


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


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


Outline






Types of Cloud Computing

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

Implementations at different levels of abstraction Analagous to a software stack: hardware OSapplication Lower-level == more difficult to use + more freedom to

innovate


Types of Cloud Computing

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

Implementations at different levels of abstraction Analagous to a software stack: hardware OSapplication Lower-level == more difficult to use + more freedom to

innovate

Focus of much of this talk.Good for classroom, since it provides most

freedom to innovate


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


Example: Amazon EC2


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…..


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


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


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


Outline






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)


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


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


GENI Overview

Sliverable GENI Substrate(Contributing domains/Aggregates)

Wind tunnel

Experiments(Guests occupying slices)

Embedding

Petri dish

Observatory

Documents

Cloud Computing and High Performance Networking