Cloud Computing and High
Performance Networking
David Irwin
Computer Science Department
University of Massachusetts, Amherst
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
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”
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Cloud Computing
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Shared resources == lots of computers in data centers
Benefit from “Economy of Scale”
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Cost per unit falls as scale increases
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I.e., like Costco but for computers and software services
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Outline
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Virtualized Data Centers
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Hardware Virtualization

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The foundation for virtualized data centers
Public/private Cloud Computing


The foundation for cloud computing
Relevance to education
Shared testbeds

NSF’s GENI prototype
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Data Center Overview
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Large Server and Storage Farms
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Used by enterprises to run server applications
Used by Internet companies
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Google, Facebook, YouTube, Amazon
Size varies depending on needs
Architecture
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Traditional: applications run on physical servers
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Manual mapping of applications to servers
IT admins deal with “change”
Modern: virtualized data centers
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Application runs inside of virtual servers; VM mapped to physical
servers
Provides flexibility in mapping from virtual to physical resources
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Virtualized Data Center
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Simplifies resource management
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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
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Internet applications  dynamic workloads
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How much capacity to allocate to applications?
Traditional approach: IT admins estimate peak
workloads and provision sufficient servers
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Flash crowd  react manually by adding capacity
Time scale of hours: lost revenue, bad publicity for application
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Dynamic Provisioning
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Track workload and dynamically provision capacity
Monitor  Predict  Provision
Predictive versus reactive provisioning
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Traditional data centers: bring up a new server
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Predictive: predict future workload and provision
Reactive: react whenever capacity falls short of demand
Borrow from free pool or reclaim under-used server
Virtualized data center: exploit virtualization to
speed up application startup time
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Outline

Virtualized Data Centers


Hardware Virtualization


The foundation for virtualized data centers
Public/private Cloud Computing


The foundation for cloud computing
Relevance to education
Shared testbeds

NSF’s GENI prototype
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Virtual machines are hot
Headlines from August 2007
VMware IPO: $19.1 billion
Xen sale: $500 million
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Traditional OS Structure
App
App
App
App
Operating System
Host Machine
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
OS abstractions
Applications
Threads
Instructions
CPU
Virtual
Memory
OS
Virtual addrs
Physical mem
“Kernel
library”
Syst calls
I/O devices
Hardware
What are the interfaces and the resources?
What is being virtualized?
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
OS abstractions
Applications
Threads
Instructions
CPU
Virtual
Memory
OS
Virtual addrs
Physical mem
“Kernel
library”
Syst calls
I/O devices
Hardware
What are the interfaces and the resources?
What is being virtualized?
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Virtual Machine Structure
Guest
App
Guest
App
Guest
App
Guest OS
Guest OS
Guest OS
Virtual Machine Monitor (Hypervisor)
Host Machine
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Why are VMs useful?
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Code reuse
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Encapsulation
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Can run old operating systems + apps on new hardware
Original purpose of VMs by IBM in the 60s
Can put entire state of an “application” in one thing
Move it, restore it, copy it, etc
Isolation, security
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All interactions with hardware are mediated
Hypervisor can keep one VM from affecting another
Hypervisor cannot be corrupted by guest operating systems
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Encapsulation
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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
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Will this work?
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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)
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Encapsulation
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Virtual machines capture all of this state
Can suspend/restore an application
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On same machine between boots
On different machines
Very useful in server farms
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As we discussed earlier
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Examples
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Full Virtualization
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Para-virtualization
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Run any OS; expose full x86 ISA
E.g., VMware, Xen on HVM
Run any (slightly modified) OS; expose (slightly modified) x86 ISA
E.g, Xen
OS-level virtualization
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Run multiple copies of same OS
E.g, VServers, User-mode Linux
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Outline

Virtualized Data Centers


Hardware Virtualization


The foundation for virtualized data centers
Public/private Cloud Computing


The foundation for cloud computing
Relevance to education
Shared testbeds

NSF’s GENI prototype
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Types of Cloud Computing
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Implementations at different levels of abstraction
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Software-as-a-Service
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E.g., Gmail, Google Calendar
Platform-as-a-Service
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Analagous to a software stack: hardware OSapplication
Lower-level == more difficult to use + more freedom to innovate
E.g., Azure, AppEngine
Infrastructure-as-a-Service

E.g., Amazon EC2, S3, EBS
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Types of Cloud Computing

Implementations at different levels of abstraction



Software-as-a-Service


E.g., Gmail, Google Calendar
Platform-as-a-Service


Analagous to a software stack: hardware OSapplication
Lower-level == more difficult to use + more freedom to innovate
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
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Cloud Computing Benefits
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Low upfront capital expenditure
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Predictable costs
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No need to buy, power, cool, or maintain hardware
Cheaper for small or short-term operations
E.g., educators teaching project-based courses
Flat fees for usage, e.g., $/hour
Nice for fixed budgets: $250 class computing budget
Computing budgets are hard to predict
Flexible pricing plans
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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
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Example: Amazon EC2
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Types of Amazon Resources
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Elastic Compute Cloud (EC2) ($0.085/hour)
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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)
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Elastic Snapshots
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SimpleDB
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Prices are continuing to fall as usage increases…..
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Educators and Cloud Computing
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Excellent platforms for experimentation
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Don’t care if students “break” things
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Give root access to machine
Install arbitrary software
Students isolated from other students/users
Enables distributed application projects
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Can access many machines for short time-period
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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
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Importance of Distributed Apps
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Internet services use many computers to serve
Internet users
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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
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Private Clouds
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If you already own a lot of mostly idle machines you
can install private cloud software
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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
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Example: Eucalyptus, emulates Amazon EC2 interfaces
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Outline

Virtualized Data Centers


Hardware Virtualization


The foundation for virtualized data centers
Public/private Cloud Computing


The foundation for cloud computing
Relevance to education
Shared testbeds

NSF’s GENI prototype
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Shared Testbeds

Run by universities and companies to experiment with
new research prototypes
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Often are “free”: may need to contribute a few computers
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Not as stable as commercial clouds
Focus on one or more characteristics
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Formed from donations by participants
Compute isolation
Network isolation
Geographic diversity
Hardware diversity
Examples: PlanetLab, Emulab, NSF’s Global
Environment for Network Innovations (GENI)
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
PlanetLab
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Get access to machines hosted around the world
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Experiment with global network services
No resource isolation
Network presence but little compute power
Success led to GENI
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
Status
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GENI connects diverse testbeds together
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Many components/link types
Routers, edge nodes, wireless, wired, storage,
sensors, fiber-optic, etc.
Prototyping started last year (http://geni.net)
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Hasn’t been built, but isn’t vaporware
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Existing systems form foundation
80 projects clustered around 4 “control frameworks”
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PlanetLab, Emulab, Orca, Orbit
4 projects at UMass-Amherst!
Run by BBN (Cambridge); also at UMass-Lowell and Williams
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008
GENI Overview
Experiments
(Guests occupying slices)
Sliverable GENI Substrate
(Contributing domains/Aggregates)
Observatory
Wind tunnel
Embedding
Petri dish
UNIVERSITY OF MASSACHUSETTS AMHERST • Department of Computer Science • 2008