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

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

Roger BargaArchitect, Cloud Computing Futures GroupMicrosoft Research (MSR)

Cloud Computing – A Microsoft Research Perspective

Contributors to this presentation include

Dan Reed, Dennis Gannon, Navendu Jain, and Tony Hey (MSR)

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



eXtreme Computing, MSR

CCF

CISGFS

Rethink the nature of computing at extreme scale, from alternative, quantum computing models, through the transformative effects of manycore parallelism on programming systems and architectures, through massive cloud computing infrastructure designs.

eXtreme Computing DivisionDan Reed, CVP Microsoft Research

ab initio research and development on cloud hardware and software infrastructure. Investigate cloud computing for research empowerment with worldwide government & academic partnerships.

Cloud Computing Futures Group




Talk Outline

• Data Center landscape• Rise of the cloud computing platform.• Data intensive research, role of cloud computingKey takeaways… • Data centers and HPC, like twins separated at birth [Dan Reed]

• Data centers evolving at a blistering pace, driven by economics

• The Application Model for Cloud Computing Is Evolving• Economic landscape increasingly favors ‘pay as you go’• There are many obstacles, but economic forces will dominate

the obstacles• Emergence of the Fourth Paradigm, synergistic with cloud

computing




HPC and Clouds – Select Comparisons

Node and system architectures Communication fabricStorage systems and analyticsPhysical plant and operationsReliability and resilienceProgramming models




HPC Node ArchitectureMoore’s “Law” favored commodity systems

Specialized processors and systems faltered“Killer micros” and industry standard blades ledInexpensive clusters now dominate

www.top500.org




HPC InterconnectsEthernet for low end (cost sensitive)High end expectations

{Nearly} flat networks and very large switchesOperating system bypass for low latency (microseconds)

www.top500.org




Modern Data Center Network

InternetInternetCR CR

AR AR AR AR…

SSLB LB

Data CenterLayer 3

Internet

SS

A AA …

SS

A AA …

…

Layer 2

Key:• CR (L3 Border Router)• AR (L3 Access Router)• S (L2 Switch)• LB (Load Balancer)• A (20 Server Rack/TOR)

Source: Albert Greenberg and Cisco

GigE

10 GigE

Monsoon network with Valiant routing




HPC Storage SystemsLocal disk

Scratch or non-existent

Secondary storageSAN and parallel file systemsHundreds of TBs (at most)

Tertiary storageTape robot(s)3-5 GB/s bandwidth

www.nersc.gov

~60 PB capacity




I/O Implications and ScaleTypical HPC scenario

MPI computationDomain decompositionSAN-based parallel file systemPeriodic checkpoints

Scaling challengesSystem MTBF approaching zeroCheckpoint frequency increasingI/O demand becoming intolerable

ImplicationsUnlikely to extend to exascaleLoosely consistent models required

0

20

40

60

80

100

120

140

0.9999

0.99999

0.999999

Slide by Dan Reed




Cloud/HPC Hardware Comparison

Predominate differencesNetwork architecture and SAN storageEfficient virtualization

Attribute HPC Cloud

Processor High-end x86 x86

Memory 1-8 GB 8 GB+

Local Disk Scratch only Permanent storage

SAN Storage Common Rare

Tertiary Storage Common Rare

Interconnect Infiniband or 10 GigE 1 GigE/10GigE

Network Flat Hierarchical

Physical Plant Traditional Optimized

Efficient

Virtualization




Virtualization as EnablerEMULATION OF EXISTING APPS

Resource utilization pool concrete resourcesDecouples concrete resources enables migrationExtend existing abstractions e.g. LUN expansion

ENABLEMENT OF NEW SERVICES

Hardware via existing ISA, memory mapped ports, etc.Storage via SCSI LUN or other disk interfaceApplication via underlying API




HPC Physical PlantFacilities

Co-located with operating institutionStandard raised floor and CRAC unitsLimited UPS supportTypically constrained to 3-5 MW

Designed as Lab showpiecesLBL

LANL ORNLANL

38,640 cores

150,152 cores 163,840 cores ~130,000 cores




The Data Center LandscapeRange in size from “edge” facilities to megascale.Unprecedented economies of scaleApproximate costs for a medium

size center (1000 servers) and large, 50K server center.

Each data center is 11.5 times

the size of a football field

Technology Cost in Medium-sized Data Center

Cost in Very 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

7.1James Hamilton, LADIS ‘08

Economies of ScaleElectricity

Put Datacenters at Cheap Power

Network

Put Datacenters on Main Trunks

OperationsStandardize

and Automate Ops

HardwareContainerized

Low-Cost Servers




Modern Data Center: Containers Separating Concerns




Data Center Design IssuesWhere are the costs? Mid-sized facility (20 containers)

Cost of power ($/kwh): $0.07Cost of facility: $200,000,000 (amortize 15 years)Number of Servers: 50,000 (3 year life) @$2K eachPower critical load 15MWPower Usage Effectiveness (PUE) 1.7

Observe:Fully burdened cost of power =power consumed + cost of cooling and power distribution infrastructureAs cost of servers drops and power costs rise, power will dominate all other costs.

$2,997,090$1,296,9

02

$1,042,440

$284,686

ServersPower & Cooling InfrastructurePowerOther Infra-structure

Monthly Costs

3yr server & 15 yr infrastructure amortization




PowerEPA released a report saying:

In 2006 data centers used 61 Terawatt-hours of powerTotal power bill: $4.5 billion7 GW peak load (15 power plants)This was 1.5 % of all US electrical energy use.Expected to double by 2011.

Power accounts for 30% of Data Center costsOnly 20%-30% CPU utilizationCauses: Uneven app fit, demand varies, over-provisioning, etc.

A deeper look and a few ideas ….




Power and Cooling Is Expensive!

• Infrastructure for power & cooling cost a lot…• Infrastructure PLUS Energy > Server Costs

Since 2001• Infrastructure Alone > Server Costs Since

2004• Energy Alone > Server Cost Since 2008

•Cost Effective to discard energy inefficient servers• Power Savings Infrastructure Savings!

Like Airlines Retiring Fuel-Guzzling Airplanes




What can we do about power costs?Data Centers use 1.5% of US electricity

$4.5 billion annually7 GW peak load (15 power plants)44.4 million mt CO2 (0.8% emissions)

Rethink EnvironmentalsRun them in a wider rage of conditionsChristian Belady’s “In Tent” data center experiment.

Rethink UPSGoogle’s battery per server.

Rethink ArchitectureIntel Atom and power states.Marlowe Project




Marlowe & the Big SleepAdaptive Resource Management

Monitor the data center and its apps.Use rules engine & fuzzy logic to control resourcesfor most current workloadsSpare capacity availableSleep/hibernate 3 – 4 watts (vs. 28 – 36 watts for Atom servers)5 – 45 sec. to reactivate server

Created by Navendu Jain, CJ Williams, Dan Reed and Jim Larus




Server

Capacity

Generation 1

Data Center Collocation

Rack

Density and Sustainability

Generation 2

Containers

ScalabilityThousands of Servers

Generation 3 Generation 4 (future)Modular Data Center

Right Time to Market,Lower TCO (PUE)

Scalable Data Centers

Pre-Assembled Components

Microsoft’s Data Center Evolution

Deployment Scale Unit

Data Center Evolution




What is a "cloud computing"?

“…data as a service…”

“…software as a service…”

“cloud computing journal reports that…”

“…everything as a service...”




Cloud Computing

Using a remote data center to manage scalable, reliable, on-demand access to application services and data.

Scalable means• Possibly millions of simultaneous users of

app.• Exploiting thousand-fold parallelism in the

app.

Reliable means on-demand means 5 “nines” available right now

Three New Aspects to Cloud ComputingIllusion of infinite computing resources available on demand

Elimination of an upfront commitment

Ability to pay for use of computing resources on a short-term basis as needed




Platform Extension to the Cloud is a Continuum

Server Hosted Server

Cloud Fabric

Windows (or Linux) Hosted OS Compute Fabric

DB Server Hosted DB Server Storage Fabric

…… …… ……

What You’ve Been Using So Far

• Hosted version of what you have been using so far• Requires few changes if any

to what you know and do

• New capabilities• New cost structure• Requires embracing a

specific app model




Spectrum of Application Models




Azure 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 Programming Model




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

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

Because it can communicate with a fabric agent on every computer, it’s also aware of every Windows Azure application in this fabric

The Azure Fabric




RolesScalable, Fault Tolerant, Stateless

Roles are a mostly stateless process running in a Windows Server 2008 VM on one or more cores Web Roles provide web service

access to app 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 instances can scale

with load.

A Scalable architecture is critical to take advantage of scalable infrastructure

• Queues decouple different parts of app, making it easier to scale app parts independently;

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

• Queues mask faults in worker roles.




The simplest way to store data in Azure storage is to use blobs• A blob contains binary data, up to 50GB

Each table holds some number of entities. An entity contains zero or more properties

SQL Data Services – provide the SQL data platform in the cloud

Storage Blobs, Tables and Queues, and a full relational database

Blobs can be big—up to 50 gigabytes each

They can also have associated metadata




Back to the Future (again….)Mid 1980's: The invention of client/server databases

Data locked up in mainframe DBsClosed & monolithic trust boundaryPCs?: Spreadsheets and terminal emulationNetworks – lots of them: DECNet, IPX, SNA, Banyan Vines, TCP/IP

Client / Server database challengesHad to invent: network abstraction layer, formats, protocolsHad to consider: latency, concurrency controlHad to move: trust boundaryWound up with only 60% of the incumbent's capability…could have been easily dismissed as a failure

End resultData was made accessible where it could be used in a new wayClient / Server databases are now viewed as being tremendously successful




Data in a Cloud Services World

Cloud database service challengesSame as Client / Server DBMS shift

Formats, protocols, authentication, authorization, latency, trust boundary

Will not do 100% of what client / server databases can do

Cloud database service capabilitiesData boundary moves from corporate LAN to internetUtility DBMS for cloud applicationsExpect new capabilities, new value proposition




Cloud Platform: Strategic Differentiator and Economics

Com

peti

tive A

dvanta

ge

Strategic

Motive: Competitive Advantage

Utilitarian

Motive: Resource & Cost Optimization

Innovation introduced by third firm

Innovation introduced by second firm

Innovation introduced by first firm

Time

Competitive advantageAND economics




The Economics of Elasticity – by the numbers…

Assume Our Service

Peaks at 500 Servers at Noon

Trough Requires 100 Servers at Midnight

Average Utilization Is 300 Servers

Actual Utilization

300 × 24 = 7200Server Hours / Day

ProvisionedResources

500 × 24 = 12000Servers Hours / Day

Pay as You Go Break-Even Point

12000 = 7200 × 1.667

Cheaper When Pay as You Go Servers Are Less than 1.667 Times Purchased Servers

Elasticity May Be More Cost-Effective Even with a Higher Per-Hour Charge!

E-Commerce Peaks December

Photo-Sharing Peaks January

Takes Weeks to Acquire and Install Equipment

Example of Elasticity

Seasonal Demands Require Significant Provisioning

This Example Underestimates the Benefits of Elasticity




Research Funding

1. Have good idea

2. Write proposal

3. Wait 6 months4. If successful, wait 3

months to get $$$

5. Install Computers

6. Start Work

Science Start-ups

1. Have good idea

2. Write Business Plan

3. Ask VCs to fund

4. If successful...

5. Install Computers

6. Start Work

Cloud Computing Model

1. Have good idea

2. Grab nodes from Cloud provider

3. Start Work

4. Pay for what you actually used

The Cloud Empowers the Long Tail of Research

Slide compliments of Paul Watson, University of Newcastle (UK)

Poised to reach a broad class of new users




Thousand years ago – Experimental Science

Description of natural phenomena

Last few hundred years – Theoretical Science

Newton’s Laws, Maxwell’s Equations…

Last few decades – Computational Science

Simulation of complex phenomena

Today – Data-Intensive ScienceScientists overwhelmed with data sets from a variety of different sources

Data captured by instruments, sensor networksData generated by simulationsData generated by computational models

Emergence of a Fourth Research Paradigm

With thanks to Jim Gray

Astronomy was 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 Survey combined with an image of the moon, synthesized within the WorldWide Telescope



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


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

Science ExamplePhyloD as an Azure Service

Cover of PLoS Biology

November 2008

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

• PhyloD was developed by Microsoft Research and has been highly impactful

• Small but important group of researchers

100’s of HIV and HepC researchers actively use it

1000’s of research communities rely on results




Metagenomics Atop Azure

MetagenomicsEcosystem characterization

Map Reduce-styleParallel BLAST50 roles, speedup 45100 roles, speedup 94

BLAST user selects DBs and input sequence

BlastWeb Role

InputSplitterWorker

Role

BLASTExecutionWorker Role #n…

.

CombinerWorker Role

GenomeDB 1

GenomeDB K

BLAST DBConfiguration

Azure Blob Storage

BLASTExecutionWorker Role #1

Basic Map-Reduce - 2 GB database per worker - 500 MB input file.




Reference Data on AzureOcean Science data on Azure SDS-relational

Two terabytes of coastal and model data

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

Storing select seismic data on Azure, NSF funded consortium that collects and distributes global seismological data.• Data sets requested by researchers

worldwide• Includes HD videos, seismograms, images,

data from major seismic events.




TakeawaysCloud Computing: Apps Delivered as Services over the Internet and the Datacenter Hardware and Software Providing Them

Software as a Service: Application Services Delivered over the InternetUtility Computing: Virtualized Hardware and Compute Resources Delivered over the Internet

The Economics Are Changing towards Cloud ComputingBig Datacenters Offer Big Economies of ScaleCloud Computing Transfers Risks Away from the Application Providers

The Application Model for Cloud Computing Is EvolvingAdvantages to Being “Close to the Metal” versus Advantages to Higher LevelApplications Typically Cannot Port TransparentlyJust Because the Infrastructure Is Scalable Doesn’t Mean the App Is!!

There Are Many Obstacles to Ubiquitous Cloud ComputingTechnical Obstacles to Adoption and GrowthPolicy and Business Obstacles to Adoption

The Economic Forces Will Dominate the ObstaclesThere’s Too Much to Gain… It Will Grow!




Q &

A

Roger BargaArchitect, Cloud Computing Futures GroupMicrosoft Research (MSR)

Cloud Computing for Research



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