1. Grids, utility computing
and a perspective on
the future of IT infrastructure
Washington Area CTO Forum
March 31, 2006
Nirav Kapadia
nhkapadia@gmail.com

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Outline
 Characterizing computing grids
 Grids as intended versus what we see today
 Common types of grids today
 Putting computing grids to work
 Types of problems addressed by today’s grids
 Operational considerations in deploying a grid
 A perspective on the future of IT infrastructure
 Cost pressures and technology commoditization
 Grid and utility computing: the technology enablers

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Grids came about from a need for
large scale, collaborative computing
 Scale is measured in terms of users, nodes,
organizations, geography, and heterogeneity
 A grid in the strict sense of the word involves a large
number of heterogeneous, shared resources
 Collaboration is measured in terms of resource
sharing and interoperability
 A key characteristic is the ability to manage across
organizational boundaries

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Systems for large scale, collaborative
computing must meet key criteria
Group A
Scalable with users and resources
Support for heterogeneity
Group B
Support for interoperability
Scalable with geographical distances
Group C
Fully distributed (federated) architecture
Ability to compartmentalize along
organizational boundaries
Strictdefinitionofcomputinggrid
Broaddefinitionof
computinggrid

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Many commercial grid solutions only
meet the broad definition of a grid
 Cluster management systems
 Typically harness clusters of dedicated servers
 Examples include Platform LSF, Sun Grid Engine
 CPU-scavenging “master-slave” applications
 Typically take advantage of idle desktop cycles
 Examples include SETI@Home, distributed.net

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Many commercial grid solutions only
meet the broad definition of a grid
 Application-specific, custom-built grids
 Typically built around a key business function
 Examples include Acxiom, Oracle offerings

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Today, solutions that meet the strict
definition of a grid have to be “built”
 Grid solutions based on the Globus toolkit
 Several vendors have Globus based offerings
 Univa Corp is commercializing Globus
 Other grid solutions in academia and research
 Most are custom-built and target a specific problem
 Typically not appropriate for commercial use (today)

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Key takeaways
 A grid is a distributed computing system that
enables large scale, collaborative computing
 Scalable across a large number of diverse and
geographically dispersed resources
 Many commercial “grid solutions” of today do
not meet the strict definition of a grid
 Limited ability to manage policies and resources
across administrative boundaries

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Outline
 Characterizing computing grids
 Grids as intended versus what we see today
 Common types of grids today
 Putting computing grids to work
 Types of problems addressed by today’s grids
 Operational considerations in deploying a grid
 A perspective on the future of IT infrastructure
 Cost pressures and technology commoditization
 Grid and utility computing: the technology enablers

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Even today’s grids can benefit users
with large scale computing needs
 High throughput computing (HTC)
 Many independent (non-communicating) tasks
 Large problems that break up into manageable,
independent tasks
 High performance computing (HPC)
 Large problem that is not decomposable into
manageable, independent tasks

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High throughput computing is
common in business environments
 Large, legacy applications are best served by
cluster management systems
 Compute-intensive apps are preferable but a mix of
compute- and data-intensive apps are manageable
 Customizable apps that work on small slices of
data work well with CPU-scavenging grids
 Apps must be compute-intensive and preferably run
within a sandbox

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High performance computing is
seen more in targeted environments
 Applications involving multiple, communicating
tasks are typically require custom designed grid
environments
 Examples include Oracle grid offering and some test
beds built with Globus
 Other examples include distributed computing
platforms such as PVM and MPI

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So… you’re ready to deploy a grid
computing environment…
 As with any other technology, there are several
operational considerations…
 Resources on the grid – dedicated or shared?
 Access management – who needs access to what?
 Data management – how does data get to the grid?
 Security model employed by the grid

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Resources on the grid –
should they be dedicated or shared?
Cluster Mgmt Systems
 Cluster management
systems work best with
dedicated resources
 Condor – from the U of
Wisconsin – is a notable
exception, but not
commercially available
CPU-scavenging grids
 As the name implies,
resources are shared –
and typically involve
desktops
 A custom screen saver is
the most common
vehicle for running the
grid application

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Access management –
who needs (gets) access to what?
Cluster Mgmt Systems
 Option #1: jobs run in a
guest account
 Shared access across
jobs
 Option #2: accounts for
everyone on all
machines
 Homogeneous uid pool
highly recommended
 Logins typically disabled
CPU-scavenging grids
 Option #1: jobs run with
user’s privileges
 If downloaded by user
 Option #2: jobs run in
guest account
 If set up by administrator
 No direct remote user
access to desktop

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Data management –
how does data get to the apps?
Cluster Mgmt Systems
 Transfer user specified
files via ftp, scp, etc
 File staging for large data
 On demand file transfer
(system call traps)
 Shared file systems
CPU-scavenging grids
 Data embedded within
application or retrieved
via HTTP/Java call-
backs
 Limited data, typically no
files

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Security model –
user accountability is key today
Basicsystemandkernelsafeguards
Run Time
Environment
Application
Executable
Application
Generation
Application
Users
Unchanged
Binaries
Object Code
Modifications
Source Code
Modifications
Custom
Applications
Ideal Grid
Unix
LSF, PBS, SGE
Globus
Condor
Java, PCCs
distributed.net,
SETI@Home, etc
Access management (capability control)
Opportunities for subversion

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Key takeaways
 Today’s commercially available grid solutions
primarily target high throughput computing
 Cluster management systems and CPU-scavenging
grids are the most common
 Carefully consider the policy implications of
grids in terms of access and data management
 More of a concern for grids that span sub-nets or fire
walls

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Outline
 Characterizing computing grids
 Grids as intended versus what we see today
 Common types of grids today
 Putting computing grids to work
 Types of problems addressed by today’s grids
 Operational considerations in deploying a grid
 A perspective on the future of IT infrastructure
 Cost pressures and technology commoditization
 Grid and utility computing: the technology enablers

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Even as grids take hold, the
IT landscape is changing rapidly…
 Technology is rapidly being
commoditized
 Businesses are more willing
and able to shop for IT services
 In-house IT infrastructure is
increasingly seen as complex
and rigid
© Harvard Business Review

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IT infrastructure is already a
commodity from a business view
 Outsourcing is pervasive; and standards-
based, open systems are increasingly common
 Cost pressures will continue driving businesses to
streamline IT infrastructure
 More often than not, customized in-house IT
systems stand out for their cost and complexity
 Common off-the-shelf solutions provide more value
in the absence of direct competitive advantage

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In time, economics will drive IT
infrastructure out of the enterprise
 The technology enablers for this paradigm exist
today, but are still nascent
 (True) grids offer a way to manage computing
resources across organizational boundaries
 Utility computing solutions bring together grids, data
center automation, and virtualization

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The technology implications of these
changes are enormous
 Computing infrastructure needs to become
transparent to end users
 Users only interact with applications and data
 Policy management needs to be decoupled
from system management
 Cannot assume users can be held accountable
 Components of computing systems need to be
less tightly coupled
 CPU, OS, data, apps may all be in different, remote
locations

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A utility computing test bed at Purdue
showcases this paradigm
 Operating since 1995; now a joint development
effort between Purdue and U of Florida
 By 2001, allowed 3,000+ users from 30 countries to
run ~100 applications in a utility environment
 Extensively validated: ~400,000 runs (by 2001);
highly peaked usage profile
 Powers online simulations in the nanoHUB.org
portal for the nanotechnology community

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nanoHUB.org – remote access to
simulators and compute power
Cluster
TeraGrid
Condor-G
Globus
Condor-G
Globus
Internet
nanoHUB infrastructure
nanoHUB.org
Web site
Physical Machine
Virtual Machine
NMI Cluster
Slide courtesy of Gerhard Klimeck, Network for Computational Nanotechnology
Remote desktop (VNC)
Real users and real usage
>10,687 users

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Application
Repositories
Data Vaults
CPU Farms
Web
Portal
PUNCH Virtual Machine
Utility ServicesLocal Services
OS
Repositories
Custom computing
environment assembled
in real time
Inside nanoHUB.org

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In conclusion…
 Today’s commercially available grids provide a
valuable but narrow service
 More efficient computing in a closed environment;
limited support for cross-organizational sharing
 In time, grid and utility computing technologies
will move IT infrastructure out of the enterprise
 Virtualization and data center automation products
are visible precursors

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Questions? Comments?
Email: nhkapadia@gmail.com