Programming the Cloud Cloud Services for Science

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Programming the Cloud Cloud Services for Science

Tony HeyCorporate Vice President

Microsoft External Research

http://creativecommons.org/licenses/by/3.0/us/


Tony Hey – An Introduction

Commander of the British Empire


http://www.rcuk.ac.uk/


Worldwide External Research

Core Computer Science

Earth, Energy &Environment

Education & Scholarly

Communication

Health & Wellbeing

Advanced Research Tools and Services

Community and Geographic Outreach



1. Thousand years ago – Experimental Science– Description of natural phenomena

2. Last few hundred years – Theoretical Science– Newton’s Laws, Maxwell’s Equations…

3. Last few decades – Computational Science– Simulation of complex phenomena

4. Today – Data-Intensive Science– Scientists overwhelmed with data sets

from many different sources • Data captured by instruments• Data generated by simulations• Data generated by sensor networks

eScience is the set of tools and technologiesto support data federation and collaboration• For analysis and data mining• For data visualization and exploration• For scholarly communication and dissemination

Emergence of a Fourth Research Paradigm

(With thanks to Jim Gray)With thanks to Jim Gray

Astronomy has been one of the first disciplines to embrace data-intensive science with the Virtual Observatory (VO), enabling highly efficient access to data and analysis tools at a centralized site. The image shows the Pleiades star cluster form the Digitized Sky Surveycombined with an image of the moon, synthesized within the WorldWide Telescope service.

Science must move from data to information to knowledge


http://www.sciencemag.org/cgi/content/full/323/5919/1297/F1


Accelerating time to insightwith Advanced Research Tools and Services

Our goal is to accelerate research by collaborating with academic communities to use computer science research technologies

We also aim to help scientists spend less time on IT issues and more time on discovery by creating open tools and services based on Microsoft platforms and productivity software

Data Acquisition

and Modeling

Collaboration and

VisualizationAnalysis and Data Mining

Disseminate and Share

Archiving and Preservation



What is Cloud Computing?A Definition: – Cloud Computing means using a remote data center to

manage scalable, reliable, on-demand access to applications

– Providing Applications and Infrastructure over the Internet– Scalable means:• Possibly millions of simultaneous users of the app.• Exploiting thousand-fold parallelism in the app.

– Reliable means on-demand; 5 “nines” available right now– Applications span the continuum from client to the cloud

Three New Aspects to Cloud Computing:– Illusion of infinite computing resources available on

demand– Elimination of an upfront commitment by cloud users– Ability to pay for use of computing resources on a short-

term basis as needed



The Data Center LandscapeRange in size from “edge” facilities to mega scale.Unprecedented economies of scaleApproximate costs for a small size center

(1K servers) and a larger, 50K server center.

Each data center is 11.5 times

the size of a football field

Technology Cost in small-sized Data Center

Cost in Large Data Center

Ratio

Network $95 per Mbps/month

$13 per Mbps/month

7.1

Storage $2.20 per GB/month

$0.40 per GB/month

5.7

Administration ~140 servers/Administrator

>1000 Servers/Administrator

7.1

Data courtesy of James Hamilton



Conquering complexity– Building racks of servers & complex cooling

systems all separately is not efficient.– Package and deploy into bigger units– 3 Sockets: Power, Cooling, Bandwidth

Advances in Data Center Deployment


This work is licensed under a Creative Commons Attribution 3.0 United States License.This work is licensed under a Creative Commons Attribution 3.0 United States License.

Containers: Separating Concerns




• A Supercomputer is designed to scale a single application for a single user. – Optimized for peak

performance of hardware– Batch operation is not “on-

demand”– Reliability is secondary

• If MPI fails, application crashes

• Build check-pointing into application

– Most Data Center applications run continuously (as services)

• Most Cloud Applications are immediate, scalable and persistent

• The Cloud is also a platform for massive data analysis– Not a replacement for leading

edge supercomputers

• The Programming model must support scalability in two dimensions– Thousands of simultaneous users

of the same applications– Applications that require

thousands of cores for each use

Why is this not just the same as Supercomputing?

* Dan Reed



Programming the Cloud

Infrastructure as a Service (IaaS)Provide a way to host virtual machines on demand

Platform as a Service (PaaS)You write an Application to Cloud APIs and the platform manages and scales it for you.

Software as a Service (SaaS)Delivery of software to the desktop from the Cloud

Infrastructure as a Service

Platform as a Service

Software as a Service

Azure™ Services Platform



A Spectrum of Application Models

Microsoft Azure

.NET CLR/Windows Only

Choice of LanguageSome Auto

Failover/ Scale (but needs declarative

application properties)

Google App Engine

Traditional Web Apps

Auto Scaling and Provisioning

Force.ComSalesForce Biz Apps

Auto Scaling and Provisioning

Amazon AWSVMs Look Like

HardwareNo Limit on App

ModelUser Must Implement

Scalability and Failover

Constraints in the App Model More Constrained

Less Constrained

Automated Management Services More Automation

Less Automation




Azure Abstract Programming Model

Azure Services (storage)

Load

Balancer

Public Internet

Worker Role(s)

Front-endWeb Role

Switches

Highly-availableFabric Controller

In-band communication – software control

Load-balancers

Abstract ProgrammingModel:



Roles: Scalable, Fault Tolerant, Stateless

Roles are mostly stateless processes running on a core

Web Roles provide web service access to the app by the users. Web roles generate tasks for worker roles

Worker Roles do “heavy lifting” and manage data in tables/blobs

Communication is through queues. The number of role instances should

dynamically scale with load

Scalability• Queue length directly reflects how well

backend processing is keeping up with overall workload

• Queues decouple different parts of the application, making it easier to scale parts of the application independently

• Flexible resource allocation, different priority queues and separation of backend servers to process different queues




• Consists of a (large) group of machines all managed by software called the fabric controller

• The fabric controller is replicated across a group of five to seven machines and owns all of the resources in the fabric

• Because it can communicate with a fabric agent on every computer, the controller is aware of every Windows Azure application running on the fabric

The Azure Fabric



• The simplest way to store data in Windows Azure storage is to use blobs– A blob contains binary data – A storage account can have one

or more containers, each of which holds one or more blobs

• Tables hold some number of entities. – An entity contains zero or more

properties

• Queues provide scalability– Queues provide a buffer to

absorb traffic bursts– Reduce the impact of individual

component failures

Azure Storage Blobs, Tables, Queues, and SQL Data Services (relational)

Blobs can be big—up to 50 gigabytes eachThey can also have associated metadata



Investigating the use of commercial Cloud services for scientific research

Example Applications:• PhyloD, computationally-intensive science

application that was not previously available as a service. Moving to the Cloud so researchers around the world can have access.

• MatLab, client application making use of Azure blob storage. Matlab could also be hosted in the Cloud, with the appropriate licensing.

• Excel, demonstrating seamless interaction between familiar client tools and the Cloud, where data is stored in Azure tables and Azure computations can be invoked for analysis. The data is from sensors distributed throughout one of our Data Centers.

Scientific Applications in the CloudSaaS for Science


http://images.google.com/imgres?imgurl=http://kom.aau.dk/~persa/matlab_pic.png&imgrefurl=http://www.freescience.info/Miscellanea.php&usg=__fhzMIzYf2Vt3mH8K0kXxZHgV5G4=&h=284&w=417&sz=120&hl=en&start=26&um=1&tbnid=T2d2feuFcjmhpM:&tbnh=85&tbnw=125&prev=/images?q=matlab&ndsp=21&hl=en&rls=com.microsoft:*:IE-SearchBox&rlz=1I7ADBF_en&sa=N&start=21&um=1


• Statistical tool used to analyze DNA of HIV from large studies of infected patients

• PhyloD was developed by Microsoft Research and has had highly impact

• Small but important group of researchers– 100’s of HIV and HepC researchers actively use it– 1000’s of research communities rely on these results

PhyloD as an Azure Service

• Typical job, 10 – 20 CPU hours with extreme jobs requiring 1K – 2K CPU hours– Very CPU efficient– Requires a large number of test runs for a given job (1 – 10M tests)– Highly compressed data per job ( ~100 KB per job)

Highlights Azure’s potential for agile deployment of science-related services that scale

Cover of PLoS Biology November 2008

Courtesy of Roger Barga



PhyloD as an Azure Service Web role copies input tree, predictor and target files to blob

storage, enqueues INITIAL work item and updates tracking tables.

Job Created

INITIAL Created

Blob Storage

Work Item Queue

Tracking Tables

(INITIAL)




PhyloD as an Azure Service Worker role copies the input files to its local storage, computes

p-values for a subset of the allele-codon pairs, copies the partial results back to blob storage and updates the tracking tables.




PhyloD as an Azure Service Web role serves the final results from blob storage and status

reports from tracking tables.




PhyloD Scalability on Azure Cloud

Workers Clock Duration

Total running time

Computational running time

25 0:12:00 2:19:39 1:49:4316 0:15:00 2:25:12 1:53:47

8 0:26:00 2:33:23 2:00:144 0:47:00 2:34:17 2:01:062 1:27:00 2:31:39 1:59:13




Project JUNIOR:Demonstrating the Value of Cloud Services for ScienceLed by Newcastle University (Prof. Watson) and supported by External Research.

Goal: Investigate applicability of Clouds for scientific research• Build a working prototype for a thin slice (use-cases in chemo-informatics)• Utilize Microsoft technologies to build science-related services• Investigating additional Scientific Cloud Services to raise abstraction level for applications



Reference Scientific Data Sets on Azure

24

Ocean Science data on Azure SDS-relational• Two terabytes of coastal and model data

Computational finance data on SDS-relational• BATS, daily tick data for stocks (10 years)• XBRL call report for banks (10,000 banks)

Currently working with IRIS to store select seismic data on Azure. IRIS consortium based in Seattle (NSF) collects and distributes global seismological data.• Data sets requested by researchers worldwide• Includes HD videos, seismograms, images, data from

major seismic events.

High research value worldwide, frequently used in academia and lecturesConsume in any language, any tool, any platform in S+S scenarios



• A knowledge ecosystem: – A richer authoring experience– An ecosystem of services– Semantic storage – Open, Collaborative,

Interoperable, and Automatic

• Data/information is inter-connected through machine-interpretable information (e.g. paper X is about star Y)

• Social networks are a special case of ‘data meshes’

A world where all data is linked …

Attribution: Chris Bizer


http://www4.wiwiss.fu-berlin.de/bizer/pub/lod-datasets_2009-03-27_colored.png

http://www4.wiwiss.fu-berlin.de/bizer/pub/lod-datasets_2009-03-27_colored.png


…and stored/processed/analyzed in the Cloud

scholarly communications

domain-specific services

The Microsoft Technical Computing mission to reduce time to scientific insights is exemplified by the June 13, 2007 release of a set of four free software tools designed to advance AIDS vaccine research. The code for the tools is available now via CodePlex, an online portal created by Microsoft in 2006 to foster collaborative software development projects and host shared source code. Microsoft researchers hope that the tools will help the worldwide scientific community take new strides toward an AIDS vaccine. See more.

instant messaging

identity

document store

blogs &social networking

mail

notification

searchbooks

citations

visualization and analysis services

storage/data services

computeservices

virtualization

Project management

Reference management

knowledge management

knowledge discovery

Vision of Future ResearchEnvironment with bothSoftware + Services



Additional Resourcesresearch.microsoft.com/en-us/collaboration/toolsDryad; DraydLINQ

Computational Biology ToolkitEnables and accelerates fundamental advances in biology

F#Collaboration with the academic and research community on F#’s typed functional and object-oriented programming on the .NET platform

Plug-ins for OfficeOntology Add-in for WordArticle Authoring Add-in for WordChem4Word – Chemistry Drawing in WordMicrosoft Electronic Journals ServiceOpen XML Document Viewer

Software Engineering ToolsSpec#: Program verifier for C# extended with design by contract VCC: Program verifier for Concurrent C PEX: automatic unit testing tool for .NET CHESS: Unit testing tools for concurrent Win32 executable and .NET

Windows Azure: http://www.windowsazure.com


http://www.windowsazure.com/




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Programming the Cloud Cloud Services for Science

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