Master's Thesis - climateprediction.net: A Cloudy Approach

Outline Problem Background Computing Infrastructure Migration Storage Central Control System Conclusions

climateprediction.net: A Cloudy ApproachMaster in High Performance Computing

Master’s Thesis

Diego Perez Montes

advised by

Tomas Fernandez PenaJuan Antonio Anel Cabanelas

July 1, 2014

Diego Perez Montes climateprediction.net: A Cloudy Approach


1 Problem BackgroundCurrent InfrastructureProblem Description

2 Computing Infrastructure MigrationMeasuring the Problem...Infrastructure Redesign

3 Storage

4 Central Control SystemBackend ComponentsDashboardRunning the Simulation

5 Conclusions



Motivation

Solve a real problem, useful for someone and that can beexpanded in further works.

Apply what I’ve learned in the Master courses.

I do love large infrastructure problems (and this is a big one!).



Current Infrastructure

First of all: How does the project currently work?



Current Infrastructure

Figure : BOINC: High Level Architecture and Workflow



Problem Description

So, what is the problem then?

The need of execution of a new model (HadGEM)

The resources requirements are higher (Hardware: Computingand Storage).

The current BOINC workunit processing time is 7-9 days , thiswants to be reduced.

Heterogeneous and unpredictable environment:

Can’t manage resources on-demand.Execution time can’t be properly measured.Processed data is missing.



Problem Description

So, what is the problem then?

Need to establish metrics on the project.

Rationalization of costs (how much does a simulation reallycost?)



Project Objectives

How is it going to be solved?

Conversion to an Infrastructure as a Service (Iaas) in theCloud (Amazon Web Services AWS: EC2 for Computing andS3 for Storage).

Creation of a new abstraction layer, the Central ControlSystem:

Infrastructure and resources management.Creation of metrics and statistics.

Free Software.

Fully documented.



Measuring the Problem...

Unknown problem real size and how is it going to behaveinto the new environment with the new parametrization.

Initial data from the current infrastructure over BOINC(Computing point of view):

A workunit takes in average from 7 to 9 days to be processed.

A full simulation is (minimum) 36,000 workunits into sectionsof 6,000.




Initial considerations:

Models used on the tests: weather@homeUK floods and andweather@home Australia New Zealand (full and regional:HaDAM3P and HadRM3P)

Two representative systems (on EC2) were selected and 10consecutive executions were done.




System #1: Moderate CPU

CPU: 2 x Xeon E5-2650

MEM: 8GB (4GB/Core)

GPU: No

Workunit Time: 7.32 days

Workunit Cost: USD 4.464

Full Simulation Cost: USD 160,704




System #2: Intensive CPU&GPU

CPU: 16 x Xeon X5570

MEM: 24GB (1.5GB/Core)

GPU: 2 x Tesla M2050

Workunit Time: 1.99 days

Workunit Cost: USD 100.966

Full Simulation Cost: USD 3,634,776




Figure : Workunit Processing Time ComparissonDiego Perez Montes climateprediction.net: A Cloudy Approach


How much does it really cost?

Figure : Simulation Price ComparissonDiego Perez Montes climateprediction.net: A Cloudy Approach


Going IaaS

Figure : Proposed Infrastructure



Going IaaS

Steps:1 Template an instance:

Install Operating System (Amazon Linux 2014.03.1 64bit)Configure network and firewall.Configure local storage: 16GBInstall and configure BOINC to use climateprediction.netInstall local client (Simulation Terminator)

2 Contextualize and scale.



Storage

Every simulation (36,000 workunits) outputs 3.6 TB of data.

There are not enough resources (disk space) on the currentsystems.

Figure : Shared Storage Architecture



Architecture

Figure : Central System Architecture



Backend Components

Simple Scheduler: Runs and configures simulation with givenparameters (start/stop instances).

Reaper: Releases resources (terminates instances) when theyare powered off.

RESTful API: Gives access to configure and run simulations.



API

RESTful API

Get simulation status.

Get metric/statistic data.

Set/modify simulation parameters (number of workernodes/instances).

Stop simulation.



Dashboard

Figure : Dashboard Interface



Running the Simulation

[Overview of a Live System]



Conclusions

Objectives Achieved

Computing and Storage successfully migrated to the Cloud(EC2 and S3).

Simulations were executed, showing that running the model inthe cloud is possible.

Development and a Central System (scheduler anddashboard).

Got costs and metrics of the project.



Conclusions

What’s Next?

Migrate BOINC server.

More control/interaction with clients so the scheduler can beimproved (and give a full SaaS layer).

Costs: “warm up“ stage to dynamically recalculate price.



Thanks!



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Engineering

Master's Thesis - climateprediction.net: A Cloudy Approach