1
Topics
yIs Cloud Computing the
way to go?
yARC ABM Review
yConfiguration Basics
ySetting up the ARC
Cloud‐Based ABM
yHardware Configuration
ySoftware Configuration
yRun Times
yInputs and Outputs
yClient User Interface
yFuture Work
yQuestions
Cloud Computing
yOn‐demand use of remote computer resources
yExamples:
yYahoo Mail
yGoogle Apps (such as Calendar, Docs, etc)
yCube Cloud Controller
yAmazon Elastic Compute Cloud (Ec2)
yKey characteristics of cloud computing:
yScalable
yFee structure usually on‐demand or subscription‐based
ySupports multiple users/instances
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ARC ABM Review
y Based on the CT‐RAMP family of ABMs developed, or
being developed, in Columbus, Atlanta, the San
Francisco Bay Area, San Diego, Phoenix, Chicago,
Miami, and other regions
y ARC model implemented with Cube‐based networks,
assignments and skimming, GUI and ancillary models
(external model, truck model, etc)
y Population Size: 1.7 million households in 2005, 2.7
million in 2030
y Uses Java Parallel Processing Framework and Cube
Cluster to thread and distribute work across multiple
machines
y Base year runs 3 feedback loops in 16 hours on the ARC
cluster using 3 Windows 64bit machines with 8
processors and 32 GB of RAM each
Hall
Fulton
Carroll
Bartow
Cobb
Coweta
Henry
Gwinnett
Walton
Cherokee
DeKalb
Newton
Paulding
Forsyth
Fayette
Douglas
Spalding
Barrow
Clayton
Rockdale
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ARC ABM System Architecture
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Configuration Basics
yGoal: Setup the ABM in the cloud and see how it performs
yTwo basic approaches to the cloud‐based setup:
y System is open so the user can configure and use custom remote
machines
y Example: rent a few instances (i.e. remote machines) from Amazon
E2C and configure them as needed
y Advantage: flexibility
y Disadvantage: complexity
y System is configured ahead of time and a client user interface is
developed that sits between the user and the remote machines
y Example: Cube Cloud Controller, which wraps a user interface around
Amazon EC2 and allows the user to upload files, run models, and get
results
y Advantage: ease‐of‐use
y Disadvantage: lack of flexibility
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Setting Up a Cloud‐Based ABM
y Select and Configure Machine Instances
y Standard Instances (1.7GB RAM, 1 Core, 160GB HD, 32bit)
y Large Instances (7.5GB RAM, 4 Core, 850GB HD, 64bit)
y Extra Large Instances (15GB RAM, 8 Core, 1690GB HD, 64bit)
y High‐Memory Extra Large Instances (17GB RAM, 6.5 Core, 420GB HD, 64bit)
y High‐Memory Double Extra Large Instances (34GB RAM, 13 Core, 850GB HD, 64bit)
y High‐Memory Quadruple Extra Large Instances (68GB RAM, 26 Core, 1690GB HD,
64bit)
y Cluster Compute Quadruple Extra Large Instances (23GB RAM, 33.5 Core, 1690GB
HD, 64bit, 10GBit Ethernet)
yWindows Server or various Linux versions
yVarious database options and web server options as well
yEstimate overall price tag: http://aws.amazon.com/calculator
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Setting Up a Cloud‐Based ABM
yOn‐Demand Instance Pricing
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Setting Up a Cloud‐Based ABM
yReserved Instance Pricing
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Setting Up a Cloud‐Based ABM
yARC ABM Example Computing Cost (which does not
include data transfer costs)
yConfigure 3 High‐Memory Double Extra Large Instances
yOn‐Demand
y 16 hour model run ‐ $60
yReserved Instances for 3 years
y Initial fee ‐ $12,000
y 16 hour model run ‐ $23
y Need to run the model 324 times in 3 years to justify initial fee
Setting Up a Cloud‐Based ABM
y Manage your instances with Management Console
y Remote desktop to instances and install Java and Cube
y We used Amazon EC2 instances configured by Citilabs so
they already had a Cube site license (no hardware key
version) installed on them
y However, most modeling packages require a hardware key
y This is not possible with Amazon EC2
y Possible future solutions to this include:
y Remote license checkout
y Purchasing a one time use key for each model run instead of
purchasing an unlimited use hardware key
y
The cloud machines are only used for running the
model, and network editing is still done on a local
machine
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Run Times
yTwo runs tested at this point:
y3 machines with 6 feedback loops
y6 machines with 6 feedback loops
Iteration Run Times (hh:mm)
3 machine 6 machine
1 3:11 3:03
2 6:11 2:50
3 7:38 4:07
4 7:16 4:37
5 7:32 5:04
6 11:37 7:30
Total Java Run Time 43:25 27:11
Total Model Run Time 48:10 31:25
y The 3 machine cloud run time is
comparable to the ARC cluster run
time
y The move from 3 to 6 machines
resulted in a ~35% reduction in
run times
y More test runs are planned,
including with more instances, as
well as with some performance
Another Appproach
yMore model runs (32‐cores machines, 64, 128, 256, 512):
yIncrease in computing instances resulted in significant non‐
linear reductions in the ABM run time.
yDoubling the number of cores from 32 to 64 reduced the run
time by 37 percent.
yDoubling it again reduced the run time relative to the 32 core
run by 55 percent.
yThe 256 and 512 core runs show little improvement beyond
the 128 core run.
yARC ABM computing power sweet spot is somewhere around
128 cores.
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Inputs and Outputs
ySeparate cost to transfer data “in” and “out” of
Amazon’s Ec2 cloud
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Inputs and Outputs
yARC ABM Example Data Transfer Cost
y250MB for zipped complete model setup – $0.025
y However, only key inputs for a scenario are likely to be
uploaded so the cost is usually less
y15 GB of output for a compute model run – $2.25
y However, only key outputs are likely to be downloaded so the
cost is usually less
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Client User Interface
yAmazon EC2 is designed for software developers so it
is not really that easy to use
yAs a result, a client web‐based user interface would
need to be developed that allows the user to easily use
the cloud‐based ABM
yAn example client user interface is Cube Cloud
Controller, which allows users to:
yUpload model inputs
yRun and manage scenarios
yDownload model outputs
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Future Work
yExperiment with Cube Cloud Controller for
managing runs in the cloud
yEase‐of‐use
yCost
yMultiple‐user capabilities
yDevelop system to allow multiple users to run
the ABM simultaneously or in a more
coordinated fashion ‐ we need a cluster is busy
light
yARC review the costs of in‐house runs versus
cloud‐runs and develop a work program that
is most efficient for the agency
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