Next Generation Cloud Computing: Advanced Services, Architecture, - - PowerPoint PPT Presentation

Next Generation Cloud Computing: Advanced Services, Architecture, and Technologies

Joe Mambretti, Director, (j-mambretti@northwestern.edu) International Center for Advanced Internet Research (www.icair.org) Northwestern University Director, Metropolitan Research and Education Network (www.mren.org) Partner, StarLight/STAR TAP, PI-OMNINet (www.icair.org/omninet)

Technische Universit Carolo-Wilhelmina zu Braunschweig Braunschweig, July 1-3, 2009

• Creation and Early Implementation of Advanced Networking

Technologies - The Next Generation Internet All Optical Networks, Terascale Networks, Networks for Petascale Science

• Advanced Applications, Middleware, Large-Scale Infrastructure, NG

Optical Networks and Testbeds, Public Policy Studies and Forums Related to NG Networks

• Three Major Areas of Activity: a) Basic Research b) Design and

Implementation of Prototypes c) Operations of Specialized Communication Facilities (e.g., StarLight)

Accelerating Leading Edge Innovation and Enhanced Global Communications through Advanced Internet Technologies, in Partnership with the Global Community Introduction to iCAIR:

Invisible Nodes, Elements, Hierarchical, Centrally Controlled, Fairly Static

Traditional Provider Services: Invisible, Static Resources, Centralized Management, Highly Layered Distributed Programmable Resources, Dynamic Services, Visible & Accessible Resources, Integrated As Required, Non-Layered

Limited Services, Functionality, Flexibility Unlimited Services, Functionality, Flexibility

Paradigm Shift – Ubiquitous Services Based on Large Scale Distributed Facility vs Isolated Services Based on Separate Component Resources

A Next Generation Architecture: Distributed Facility Enabling Many Types Network/Services

Commodity Internet

Environment: VO Environment: International Gaming Fabric Environment: Control Plane TransLight Environment: Real Org Environment: Sensors Environment: Intelligent Power Grid Control Environment: Real Org1 Environment: Large Scale System Control Environment: Global App Environment: Financial Org Environment: Gov Agency Environment: RFIDNet Environment: Bio Org Environment: Lab Environment: Real Org2

SensorNet FinancialNet HPCNet MediaGridNet R&DNet RFIDNet BioNet PrivNet GovNet1 MedNet

Cloud Context (1)

• In General, Clouds Are A Means To Support Large Scale Computing

and Data Capabilities for Distributed On-Demand Resources Using Data Networks (WANs)

• There Are Many Different Types of Clouds
• Some Are Oriented Toward Services (e.g., Web 2.0 Based)
• Some Are Oriented Toward Resources

– For Example, On-Demand Computing Instances Using Infrastructure As A Service Techniques (IaaS, Amazon EC2, S3, etc., Eucalyptus)

• Some provide Large Scale On-Demand Computing Capacity (G

FS/MapReduce/Bigtable, Hadoop, Sector, etc

• Some Support Public Services Provided By Global Corporations,

Some Support Private Enterprises Through External Resources, Some Support Private Organizations Through Internal Resources – and There Are Many Variations and Hybrids

5

Cloud Context (2)

• To Date, Clouds Have Been Successful
• However, Current Clouds Have Limitations
• For Example, They Do Not Necessarily Provide

Optimal Performance

• Also, Current Clouds Are Oriented Toward

Providing Support For Many Billions of Small Data Over Commodity “Best Effort” Routed Networks

• Current Clouds Do Not Provide Optimal Support for

Large Capacity Data Flows and Extremely Large Amounts of Individual Data Components

• They Have Not Been Integrated Into Next

Generation Networking Capabilities

• They Do Not Handle Specialized Data Well – e.g.,

Digital Media

Cloud Context (3)

• If Clouds Are Successful, Why Improve Them

Despite Limitations?

• They Are Successful for Today’s Consumer and

Enterprise Services –

• However, They Will Not Meet Future Challenges

Using Current Architectures and Technologies

• Illustration – And Also Motivation For Improving

the State-of-the-Art – Large Scale Science

• Why Large Scale Science?
• Large Scale Science Provides a Looking Glass Into

the Future

A Scientific Perspective

• Scientific Research Requires The Resolution of

Extremely Complex Problems

• Scientific Research Requires The Design And

Creation Of Specialized Tools

• Increasingly, These Tools Are Being Created

Using Digital Technologies

• Because of the Complexity and Scale of Major

Scientific Problems, Many Areas of Research Encounter Technical Barriers Years Before They Are Recognized By Other Domains

• Technical Solutions That Are Created Later

Migrate To Wider Communities

• Can Large Scale Science Use Clouds? Not Until

the Limitations Described Earlier Are Addressed

Motivation: Data-Intensive Science & Engineering-e-Science Community Resources

ATLAS

Sloan Digital Sky Survey

LHC

ALMA

Magnetic Fusion Energy

Source: DOE Source: DOE New Sources Of Power

Spallation Neutron Source (SNS) at ORNL

Source: DOE

USGS Images 10,000 Times More Data than Landsat7

Shane DeGross, Telesis

USGS

Landsat7 Imagery 100 Foot Resolution Draped on elevation data New USGS Aerial Imagery At 6-inch Resolution

Source: EVL, UIC

Today’s Aerial Imaging is >500,000 Times More Detailed than Landsat7

30 meter pixels 4 centimeter pixels

Shane DeGross Laurie Cooper, SDSU

SDSU Campus

Source: Eric Frost, SDSU

iGrid 2005 UCSD

4K Digital Media Ultra High Definition Digital Communications

• NTT, Japan

NTT’s digital communications using SHD transmits extra-high-quality, digital, full-color, full motion images. 4k pixels horizontal, 2k vertical 4 * HDTV – 24 * DVD .

www.onlab.ntt.co.jp/en/mn/shd

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Vrije je Unive versiteit, rsiteit, NL www.cct.lsu.edu/Visualization/iGrid2005 http://sitola.fi.muni.cz/sitola/igrid/

• Interactive visualization coupled with

computing resources and data storage archives over optical networks enhance the study of complex problems, such as the modeling of black holes and other sources of gravitational waves.

• HD video teleconferencing is used to

stream the generated images in real time from Baton Rouge to Brno and other locations

High-Performance Digital Media

For Interactive Remote Visualization (2006)

OptIPuter JuxtaView Software for Viewing High Resolution BioImages on Tiled Displays

30 Million Pixel Display NCMIR Lab UCSD

Source: David Lee, Jason Leigh, EVL, UIC

Components Comprising Environment

• Overall Architecture
• Compute Nodes
• Storage Performance
• Network Architecture, Protocols,

Performance and Technology Note=>Ultra High Performance Networks Can Make Remote Data Appear To Be Local

• Proof of Concept – Large Scale

Testbed/Prototype

• Integration With Emerging Technologies, e.g.,

Massive Multicore, FPGAs, Customized Integrated Components, etc.

Architecture (1)

Storage Compute Data

Super-Computer Model:

• Expensive
• IO is a bottleneck

Alternative Model:

• Inexpensive,
• Parallel data IO
• Examples: Hadoop
• Sphere/Sector

Source: NCDM, UIC

Architecture (2)

Parallel/Distributed Programming With MPI, etc.:

• Flexible and powerful.
• BUT Very Complicated
• No Data Locality

Sector/Sphere Model:

• Very Simple to Apply UDF to

All Data in Parallel;

• Exploits Data Locality
• Limited to Certain Data

Parallel Applications.

Source: NCDM, UIC

What is Sector/Sphere?

• Sector/Sphere = Wide Area Cloud Providing

On-Demand Computing Capacity

• Sector: Distributed Storage System
• Sphere: Run-time Middleware Applies User

Defined Functions (UDF) to Sector Datasets.

• Open Source Software, GPL/LGPL, written in

C++.

• Initiated 2006, Current Version 1.19
• http://sector.sf.net

Source: NCDM, UIC

Sector

• Sector: Provides Long Term Persistent Storage to

Large Datasets Managed as Distributed Indexed Files.

• File Segments Are Placed Throughout Distributed

Storage Managed by Sector.

• Sector Generally Replicates Data To
• Ensure Longevity,
• Decrease the Latency When Retrieving It,
• Provide Opportunities for Parallelism.
• Sector is Designed to Take Advantage of Wide Area

High Performance Networks When Available.

• Sector Can Address Issues Of Extremely Large Data Sets,

Including Very Large Scale Science Data Sets

Source: NCDM, UIC

Sphere

• Sphere: Designed To Execute User Defined Functions

(UDF) In Parallel Using a Stream Processing Pattern for The Data That Is Managed By Sector

• UDFs Are Applied To Every Data Record In a Data Set

Managed by Sector

• Each Data Segment Is Processed Independently

Providing a Natural Parallelism

• The Sector/Sphere Design Results in Allowing Data

To Be Frequently Processed in Place Without Moving It

• If Data Must be Moved, It Can Be Transported Over High

Performance Channels With High Performance Protocols

Source: NCDM, UIC

Comparing Hadoop and Sector

Hadoop Sector Storage Cloud Block-based file system File-based Programming Model MapReduce MapReduce& UDF Protocol TCP UDP-based protocol (UDT) Replication At time of writing Periodically Security Within 6 months Security (HIPAA) Language Java C++

23

Proof of Concept: Large Scale Testbed/Prototype

• Theories Must Be Proven in Practice Using Real Facilities
• Questions: Can This Concept Scale?

Will It Work At Extreme Scales? Can High Performance Be Achieved?

• Lab Modeling and Simulation Cannot Substitute for

Real Empirical Studies

• Experimental Research testing Is Required
• - Using Real World Large Scale Facilities
• Distributed Environments and Infrastructure
• With National Science Foundation Funding A Research

Consortium (NCDM, iCAIR, Et Al) Has Created:

• An International Scale TeraFlow Testbed Using NLR and

the Global Lambda Integrated Facility (GLIF)

• A National Scale Open Cloud Testbed, Based on the NLR

Source: NLR

Teraflow 1 & 2 Testbed

Teraflow Network is Built Over the National Lambda Rail and GLIF

Seoul Asia EU GLIF

StarLight – “By Researchers For Researchers”

Abbott Hall, Northwestern University’s Chicago downtown campus View from StarLight

StarLight is an experimental

• ptical infrastructure and

proving ground for network services optimized for high-performance applications GE+2.5+10GE Exchange Soon: Multiple 10GEs Over Optics – World’s “Largest” 10GE Exchange First of a Kind Enabling Interoperability At L1, L2, L3

iCAIR: Founding Partner of the Global Lambda Integrated Facility Available Advanced Customizable Network Resources

Visualization courtesy of Bob Patterson, NCSA; data compilation by Maxine Brown, UIC.

www.glif.is

GLIF is a consortium of institutions, organizations, consortia and country National Research & Education Networks who voluntarily share optical networking resources and expertise to develop the Global LambdaGrid for the advancement of scientific collaboration and discovery.

Enables the Creation of Distributed Virtual Environments

Open Cloud Testbed – 2009

6 Locations

• 8 racks
• 256 Nodes
• 1024 Cores
• 10+ Gb/s

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MREN CENIC Dragon

• Hadoop
• Sector/Sphere
• Thrift
• Eucalyptus

C-Wave

Example: Sorting a TeraByte

• Data is Split Into Multiple Small Files,

Scattered On All Nodes

• Stage 1: On Each Node, an SPE Scans

Local Files, Sends Each Record To a “Bucket File” On a Remote Node According To The key, So That All Buckets Are Sorted.

• Stage 2: On eEach Destination Node, an

SPE Sorts All Data Inside Each Bucket.

Source: NCDM, UIC

TeraSort Using Sector & Data-Parallel UDFs

• n Open Cloud Testbed

10-byte 90-byte Key Value 10-bit Bucket-0 Bucket-1 Bucket-1023 0-1023 Stage 1: Hash based on the first 10 bits Bucket-0 Bucket-1 Bucket-1023 Stage 2: Sort each bucket

• n local node

Binary Record 100 bytes Source: NCDM, UIC

Performance Results: TeraSort on Open Cloud Testbed

Data Size Sphere Hadoop (3 replicas) Hadoop (1 replica) UIC 300GB 1265 2889 2252 UIC + StarLight 600GB 1361 2896 2617 UIC + StarLight + Calit2 900GB 1430 4341 3069 UIC + StarLight + Calit2 + JHU 1200GB 1526 6675 3702

Run time: seconds Sector v1.16 vs Hadoop 0.17 Source: NCDM, UIC

Terasort on Open Cloud Testbed

Source: NCDM, UIC

Testbed Demonstrations With National Science Foundation at the Annual Conference of The American Association for the Advancement of Science February 2009

Using An Optical Fiber Extension from StarLight/GLIF

NSF Director Former NSF Director NCDM Director

Future: On-Going Expansion

• Expansion Using More Resources
• Additional Enhancements
• Integration With Emerging Technologies
• Expansion Across National Fabrics
• Expansion Across International Fabrics,

Using GLIF, StarLight

• Additional Communities

For More Information

• iCAIR: www.icair.org
• NCDM: www.ncdm.uic.edu
• Open Cloud Testbed:

www.opencloudconsortium.org

• Sector: sector.sourceforget.net