Carroll Bartow Paulding Douglas Coweta Cobb Cherokee Fulton Fayette Clayton DeKalb Spalding Forsyth Henry Gwinnett Rockdale Hall Newton Barrow Walton 4/10/2012 Cloud Computing On demand use of remote computer resources Examples: Yahoo Mail Google Apps (such as Calendar, Docs, etc) Cube Cloud Controller Amazon Elastic Compute Cloud (Ec2) Key characteristics of cloud computing: Scalable Fee structure usually on demand or subscription based Supports multiple users/instances 1 3 Topics Is Cloud Computing the way to go? ARC ABM Review Configuration Basics Setting up the ARC Cloud Based ABM Hardware Configuration Software Configuration Run Times Inputs and Outputs Client User Interface Future Work Questions 2 ARC ABM Review 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 ARC model implemented with Cube based networks, assignments and skimming, GUI and ancillary models (external model, truck model, etc) Population Size: 1.7 million households in 2005, 2.7 million in 2030 Uses Java Parallel Processing Framework and Cube Cluster to thread and distribute work across multiple machines 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 4 1
ARC ABM System Architecture Select and Configure Machine Instances Standard Instances (1.7GB RAM, 1 Core, 160GB HD, 32bit) Large Instances (7.5GB RAM, 4 Core, 850GB HD, 64bit) Extra Large Instances (15GB RAM, 8 Core, 1690GB HD, 64bit) High Memory Extra Large Instances (17GB RAM, 6.5 Core, 420GB HD, 64bit) High Memory Double Extra Large Instances (34GB RAM, 13 Core, 850GB HD, 64bit) High Memory Quadruple Extra Large Instances (68GB RAM, 26 Core, 1690GB HD, 64bit) Cluster Compute Quadruple Extra Large Instances (23GB RAM, 33.5 Core, 1690GB HD, 64bit, 10GBit Ethernet) Windows Server or various Linux versions Various database options and web server options as well Estimate overall price tag: http://aws.amazon.com/calculator 5 7 Configuration Basics Goal: Setup the ABM in the cloud and see how it performs Two basic approaches to the cloud based setup: System is open so the user can configure and use custom remote machines Example: rent a few instances (i.e. remote machines) from Amazon E2C and configure them as needed Advantage: flexibility Disadvantage: complexity System is configured ahead of time and a client user interface is developed that sits between the user and the remote machines Example: Cube Cloud Controller, which wraps a user interface around Amazon EC2 and allows the user to upload files, run models, and get results Advantage: ease of use Disadvantage: lack of flexibility On Demand Instance Pricing 6 8 2
Reserved Instance Pricing 9 Manage your instances with Management Console Remote desktop to instances and install Java and Cube We used Amazon EC2 instances configured by Citilabs so they already had a Cube site license (no hardware key version) installed on them However, most modeling packages require a hardware key This is not possible with Amazon EC2 Possible future solutions to this include: Remote license checkout Purchasing a one time use key for each model run instead of purchasing an unlimited use hardware key The cloud machines are only used for running the model, and network editing is still done on a local machine 11 ARC ABM Example Computing Cost (which does not include data transfer costs) Configure 3 High Memory Double Extra Large Instances On Demand 16 hour model run $60 Reserved Instances for 3 years Initial fee $12,000 16 hour model run $23 Need to run the model 324 times in 3 years to justify initial fee 10 Run Times Two runs tested at this point: 3 machines with 6 feedback loops 6 machines with 6 feedback loops The 3 machine cloud run time is comparable to the ARC cluster run time The move from 3to 6 machines resulted in a ~35% reduction in run times More test runs are planned, including with more instances, as well as with some performance tuning Run Times (hh:mm) Iteration 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 12 3
Another Appproach More model runs (32 cores machines, 64, 128, 256, 512): Increase in computing instances resulted in significant nonlinear reductions in the ABM run time. Doubling the number of cores from 32 to 64 reduced the run time by 37 percent. Doubling it again reduced the run time relative to the 32 core run by 55 percent. The 256 and 512 core runs show little improvement beyond the 128 core run. ARC ABM computing power sweet spot is somewhere around 128 cores. Inputs and Outputs ARC ABM Example Data Transfer Cost 250MB for zipped complete model setup $0.025 However, only key inputs for a scenario are likely to be uploaded so the cost is usually less 15 GB of output for a compute model run $2.25 However, only key outputs are likely to be downloaded so the cost is usually less 13 15 Inputs and Outputs Separate cost to transfer data in and out of Amazon s Ec2 cloud Client User Interface Amazon EC2 is designed for software developers so it is not really that easy to use As a result, a client web based user interface would need to be developed that allows the user to easily use the cloud based ABM An example client user interface is Cube Cloud Controller, which allows users to: Upload model inputs Run and manage scenarios Download model outputs 14 16 4
Future Work Experiment with Cube Cloud Controller for managing runs in the cloud Ease of use Cost Multiple user capabilities Develop system to allow multiple users to run the ABM simultaneously or in a more coordinated fashion we need a cluster is busy light ARC review the costs of in house runs versus cloud runs and develop a work program that is most efficient for the agency 17 5