An Overview Grid Computing and Applications Subject Code: 433-498 Rajkumar Buyya Grid Computing and Distributed Systems (GRIDS) Lab. The University of.

An Overview Grid Computing and Applications

Subject Code: 433-498

Rajkumar BuyyaGrid Computing and Distributed Systems

(GRIDS) Lab. The University of MelbourneMelbourne, Australiawww.gridbus.org

WW Grid

Overview

Computing platforms and how the Grid is different ?

Towards global (Grid) computing. Grid resource management and scheduling.

Application development challenges. Approaches to Grid computing.Grid applicationsGrid Projects in GRIDS Lab@ Melbourne Summary and conclusions

Major Networking and Computing Technologies

Introduction

1960 1970 1975 1980 1985 1990 1995 2000

Tec

hnol

ogie

s In

trod

uced

* ARPANET

* Email* Ethernet * TCP/IP

* IETF

* Internet Era * WWW Era

* Mosaic

* XML

* PC Clusters* Crays * MPPs

* Mainframes

* HTML* W3C

* P2P

* Grids

* XEROX PARC worm

CO

MPU

TIN

GN

ETW

OR

KIN

G

* Web Services

* Minicomputers * PCs

* WS Clusters

* PDAs* Workstations

* HTC

Internet: Past, Present, Future

TCP/IPHTML

MosaicXML

PHASE 1. Packet Switching Networks 2. The Internet is Born 3. The World Wide Web 4. with XML 5. The Grid

1969: 4 US Universities linked to form ARPANET TCP/IP becomes core protocolHTML hypertext system created1972: First e-mail program created Domain Name System created

IETF created (1986)CERN launch World Wide Web

1976: Robert Metcalfe develops Ethernet NCSA launch Mosaic interface

0

20

40

60

80

100

120

140

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

The 'Network Effect’The 'Network Effect’ kicks in, and the web kicks in, and the web

goes critical' goes critical'

Num

ber

of

host

s(m

illio

ns)

Internet and WWW Growth

1

10,000

100,000

1,000,000

10,000,000

1969 1970 1975 1980 1985 1990 1995 2000

10

100

1,000

4

Internet Hosts

WWW Servers

Installed base and Growth rate for telephone lines, mobile phones, &

Internet hosts - 1995

Installed, 1995 1994-95 Growth Rates (%)Income Group/ Phone Mobile Internet Phone Mobile InternetRegion Lines Phones Hosts Lines Phones Hosts

Lower Income 2.0 0.12 1.35 35.7 135.1 246.0

Lower- Middle 9.1 0.33 73.31 8.7 105.1 167.0

Upper - Middle 14.5 1.34 380.13 6.4 66.8 111.9

High 53.2 8.70 10749.23 3.6 55.6 97.0

Africa 1.7 0.09 69.14 7.9 60.5 81.4

Americas 29.0 5.17 8359.58 5.4 42.3 91.5

Asia 5.4 0.62 121.70 14.7 108.3 150.0

Europe 33.0 3.04 2732.24 3.6 59.5 112.2

Oceans 39.7 9.55 12845.55 4.0 85.7 88.8

World 12.1 1.56 1661.89 7.0 60.4 97.8

Source: ACM, Nov, 97 (phones, international telecommunication union, hosts, network Wizards

Internet as a delivery Vehicle

2100

2100 2100 2100 2100

2100 2100 2100 2100

Desktop SMPs or SuperComputers

LocalCluster

GlobalCluster/Grid

PERFORMANCE

Inter PlanetCluster/Grid ??

•Individual•Group•Department•Campus•State•National•Globe•Inter Planet•Universe

Administrative Barriers

EnterpriseCluster/Grid

?

Scalable HPC: Breaking Administrative Barriers

Why Grids ? Large Scale Exploration needs them—Killer

Applications. Solving grand challenge applications using

computer modeling, simulation and analysis

Life Sciences

CAD/CAM

Aerospace

Military ApplicationsDigital Biology Military ApplicationsMilitary Applications

Internet & Ecommerce

Cluster of Clusters - Hyperclusters

Scheduler

MasterDaemon

ExecutionDaemon

SubmitGraphicalControl

Clients

Cluster 2

Scheduler

MasterDaemon

ExecutionDaemon

SubmitGraphicalControl

Clients

Cluster 3

Scheduler

MasterDaemon

ExecutionDaemon

SubmitGraphicalControl

Clients

Cluster 1

LAN/WAN

Grid: Towards Internet Computing for (Coordinated) Resource Sharing

- Unification of geographically distributed resources

http://www.sun.com/hpc/

Grid enables:

Resource Sharing

Selection

Aggreation

What is Grid ?

A paradigm/infrastructure that enabling the sharing, selection, & aggregationof geographically distributed resources:

Computers – PCs, workstations, clusters, supercomputers, laptops, notebooks, mobile devices, PDA, etc;

Software – e.g., ASPs renting expensive special purpose applications on demand;

Catalogued data and databases – e.g. transparent access to human genome database;

Special devices/instruments – e.g., radio telescope – SETI@Home searching for life in galaxy.

People/collaborators.

[depending on their availability, capability, cost, and user QoS requirements]

for solving large-scale problems/applications.

Widearea

P2P/Grid Applications-Drivers Distributed HPC (Supercomputing):

Computational science. High-Capacity/Throughput Computing:

Large scale simulation/chip design & parameter studies. Content Sharing (free or paid)

Sharing digital contents among peers (e.g., Napster) Remote software access/renting services:

Application service provides (ASPs) & Web services. Data-intensive computing:

Drug Design, Particle Physics, Stock Prediction... On-demand, realtime computing:

Medical instrumentation & Mission Critical. Collaborative Computing:

Collaborative design, Data exploration, education. Service Oriented Computing (SOC):

Computing as Competitive Utility: New paradigm, new industries, and new business.

Building and Using Grids requires...

Services that make our systems Grid Ready! Security mechanisms that permit resources

to be accessed only by authorized users. (New) programming tools that make our

applications Grid Ready!. Tools that can translate the requirements of

an application into requirements for computers, networks, and storage.

Tools that perform resource discovery, trading, composition, scheduling and distribution of jobs and collects results.

A Typical Grid Computing Environment

Grid Resource Broker

Resource Broker

Application

Grid Information Service

Grid Resource Broker

databaseR2R3

RN

R1

R4

R5

R6

Grid Information Service

Issues in Grid Technology Development

Sources of Complexity in Resource Management for World Wide

Computing Size (large number of nodes, providers, consumers) Heterogeneity of resources (PCs, Workstatations, clusters, and

supercomputers) Heterogeneity of fabric management systems (single system image OS,

queuing systems, etc.) Heterogeneity of fabric management polices Heterogeneity of applications (scientific, engineering, and commerce) Heterogeneity of application requirements (CPU, I/O, memory, and/or

network intensive) Heterogeneity in demand patters Geographic distribution and different time zones Differing goals (producers and consumers have different objectives and

strategies) Unsecure and Unreliable environment

Traditional approaches to resource management are NOT useful for Grid ?

They use centralised policy that need complete state-information and common fabric management policy or decentralised consensus-based policy.

Due to too many heterogenous parameters in the Grid it is impossible to define:

system-wide performance matrix and common fabric management policy that is acceptable to all.

So, we propose the usage of “economics” paradigm for managing resources

proved successful in managing decentralization and heterogeneity that is present in human economies!

We can easy leverage proven Economic principles and techniques Easy to regulate demand and supply User-centric, scalable, adaptable, value-driven costing, etc. Offers incentive (money?) for being part of the grid!

Grid Resource Management systems need to ensure/provide:

Site autonomy. Heterogeneous resources and substrate:

Each resource can be different – SMPs, Clusters, Linux, UNIX, Windows, Intel, etc.

Resource owners have their own policies or scheduling mechanisms (Codine/Condor).

Extend policies, through resource brokers. Resource allocation/co-allocation Online control - can apps (Graphics) tolerate non-

availability of a resource and adapt themselves?

Grid RMS to support

Ack: Globus..

•Authentication (once).

•Specify (code, resources,

etc.).

•Discover resources.

•Negotiate authorization,

acceptable use, Cost, etc.

•Acquire resources.

•Schedule Jobs.

•Initiate computation.

•Steer computation.

•Access remote data-sets.

•Collaborate with results.

•Account for usage.

•Discover resources.

•Negotiate authorisation,

acceptable use, Cost, etc.

•Acquire resources.

•Schedule jobs.

•Initiate computation.

•Steer computation.

Domain 2

Domain 1

Local Resource Mgr

Resource Brokers

Application

Local Resource MgrLocal Resource Mgr

RSL

(RSL Specialization)

Information Service - MDS

Resource Co-allocators

Resource Management Architecture

Major Grid Projects and Initiatives

mix-and-match

Object-oriented

Internet/partial-P2P

Network enabled Solvers

Economy/Service-Oriented Grid Computing

Many Grid Projects & Initiatives

Australia Nimrod-G GridSim Virtual Lab Gridbus DISCWorld ..new coming up

Europe UNICORE MOL UK eScience Poland MC Broker EU Data Grid EuroGrid MetaMPI Dutch DAS XW, JaWSJapan Ninf DataFarm

Korea...N*Grid

USA Globus Legion OGSA Javelin AppLeS NASA IPG Condor-G Jxta NetSolve AccessGrid and many more...

Cycle Stealing & .com Initiatives Distributed.net SETI@Home, …. Entropia, UD, Parabon,….

Public Forums Global Grid Forum P2P Working Group IEEE TFCC Grid & CCGrid conferences

http://www.gridcomputing.com

Initiative Focus and Technologies DevelopedUNICORE The UNiform Interface to Computer Resources aims to deliver software that allows users

to submit jobs to remote high performance computing resources – www.fz-juelich.de/unicore

MOL Metacomputer OnLine is a toolbox for the coordinated use of WAN/LAN connected systems. MOL aims at utilizing multiple WAN-connected high performance systems for solving large-scale problems that are intractable on a single supercomputer – www.uni-paderborn.de/pc2/projects/mol

METODIS Metacomputing Tools for Distributed Systems – www.hlrs.de/structure/organisation/par/projects/metodis/

Globe Globe is a research project aiming to study and implement a powerful unifying paradigm for the construction of large-scale wide area distributed systems: distributed shared objects – www.cs.vu.nl/~steen/globe

Pozan Poznan Centre works on development of tools and methods for metacomputing - www.man.poznan.pl/metacomputing/

Date Grid This project aims to develop middleware and tools necessary for the data-intensive applications of high-energy physics – grid.web.cern.ch/grid

MetaMPI MetaMPI supports the coupling of heterogeneous MPI systems, thus allowing parallel applications developed using MPI to be run on Grids without alteration – www.lfbs.rwth-aachen.de/~martin/MetaMPICH/

DAS This is a wide-area distributed cluster, used for research on parallel and distributed computing by five Dutch universities – www.cs.vu.nl/das

JaWs JaWS is an economy-based computing model where both resource owners and programs using these resources place bids to a central marketplace that generates leases of use – roadrunner.ics.forth.gr

Initiative Focus and Technologies DevelopedGlobus This project is developing basic software infrastructure for computations that integrate

geographically distributed computational and information resources – www.globus.org

Legion Legion is an object-based metasystem. Legion supports transparent scheduling, data management, fault tolerance, site autonomy, and a wide range of security options – www.legion.virginia.edu

Javelin Javelin: Internet-based parallel computing using Java – www.cs.ucsb.edu/research/javelin/

AppLes This is an application-specific approach to scheduling individual parallel applications on production heterogeneous systems – www.infospheres.caltech.edu/

NASA IPG The Information Power Grid is a testbed that provides access to a Grid – a widely distributed network of high performance computers, stored data, instruments, and collaboration environments – www.ipg.nasa.gov

Condor This project aims is to develop, deploy, and evaluate mechanisms and policies that support high throughput computing (HTC) on large collections of distributed computing resources – www.cs.wisc.edu/condor/

Harness Harness builds on the concept of the virtual machine and explores dynamic capabilities beyond what PVM can supply. It focused on developing three key capabilities: Parallel plug-ins, Peer-to-peer distributed control, and multiple virtual machines – www.epm.ornl.org/harness

NetSolve NetSolve is a project that aims to bring together disparate computational resources connected by computer networks. It is a RPC based client/agent/server system that allows one to remotely access both hardware and software components – www.cs.utk.edu/netsolve/

Grid Port SDSCs Grid Port Toolkit generalises the HotPage infrastructure to develop a reusable portal toolkit –gridport.npaci.edu/

HotPage NPACI’s HotPage is a user portal that is designed to be a single point-of-access to computer resources – hotpage.npaci.edu/

Gateway Gateway offers a programming paradigm implemented over a virtual Web of accessible resources - www.npac.syr.edu/users/haupt/WebFlow/demo.html

Initiative Focus and Technologies Developed

Ninf Ninf allows users to access computational resources including hardware, software and scientific data distributed across a wide area network with an easy-to-use interface – ninf.etl.go.jp

Bricks Bricks is a performance evaluation system that allows analysis and comparison of various scheduling schemes on a typical high-performance global computing setting – matsu-www.is.titech.ac.jp/~takefusa/bricks

Initiative Focus and Technologies Developed

DISCWorld

An infrastructure for service-based metacomputing across LAN and WAN clusters. It allows remote users to login to this environment over the Web and request access to data, and also to invoke services or operations on the available data – dhpc.adelaide.edu.au/Projects/DISCWorld/

Nimrod/G & GRACE

A global scheduler (resource broker) for parametric computing over clusters or computational grids – www.dgs.monash.edu.au/~rajkumar/ecogrid

Many Testbeds ? & who pays ?

GUSTO

Legion TestbedNASA IPG

EcoGrid

Some GRID APPLICATIONS

Types of Grid Applications

Sequential – dusty deck codes.Data Parallel:

Synchronous – tightly coupled; Loosely synchronous.

Asynchronous: Irregular in time and space; Difficult to parallelise to exploit the massive

parallelism.

Embarrassingly Parallel.

Grid Applications-Drivers

Distributed HPC (Supercomputing): Computational science.

High-throughput computing: Large scale simulation/chip design & parameter studies.

Content Sharing Sharing digital contents among peers (e.g., Napster)

Remote software access/renting services: Application service provides (ASPs).

Data-intensive computing: Data mining, particle physics (CERN), Drug Design.

On-demand computing: Medical instrumentation & network-enabled solvers.

Collaborative: Collaborative design, data exploration, education.

P. Messina et al., Caltechhttp://www.globus.org/applications/

SF-Express distributed interactive simulation.

100K vehicles (2002 goal) using 13 computers, 1386 nodes, 9 sites.

Globus mechanisms for Resource allocation; Distributed startup; I/O and configuration; Security.

NCSAOrigin

CaltechExemplar

CEWESSP

MauiSP

Distributed Supercomputing (SF-Express/MPICH-G, Caltech)

SF-Express Architecture

Create synthetic, representations of interactive environments.

Scalability via interest management.

Starting point: MPI and socket

communication; Hand startup.

LocalSimulation

Router

InterestMgmt.

LocalSimulation

Route

r

InterestMgmt.

LocalSimulation

Route

r

InterestMgmt.

High Throughput Computing(parameter sweep applications)

A study involving exploration of possible scenarios - i.e., execution of the same program for various design alternatives (data).

It consists of large number of tasks (1000s). Generally, no inter-task communication (task farming). Large size data (MBytes+) files and I/O constraints A large class of application areas:

Parameter explorations and simulations (Monte Carlo); A large number of science, engineering, and commercial applications:

Astrophysics, Drug Design, NeroScience, Network simulation, structural engineering, automobiles crash simulation, aerospace modeling, financial risk analysis

Condor, Nimrod/G, DesignDrug@Home, SETI@Home, FOLD@Home, Distributed.net.

Ad Hoc Mobile Network Simulation

Ad Hoc Mobile Network Simulation: Network performance under different microware frequencies and different weather conditions – uses Nimrod.

Drug Design: Data Intensive Computing on Grid

It involves screening millions of chemical compounds (molecules) in the Chemical DataBase (CDB) to identify those having potential to serve as drug candidates.

Protein

Molecules

Chemical Databases(legacy, in .MOL2 format)

DesignDrug@Home Architecture

A Virtual Lab for “Molecular Modeling for Drug Design” on P2P Grid

“Screen 2K molecules in 30min. for $10”

Grid Market Directory

ResourceBroker

Grid Info. Service

GTS

GTS

GTS

GTS

“Give me list PDBs sourcesOf type aldrich_300?”

“serv

ice co

st?”

(GTS - Grid Trade Server)

PDB2

“get mol.10 from pdb1 & screen it.”

Data Replica Catalogue

“service providers?”

GTS

PDB1

“mol.10 please?”

“mol.5 please?”

(RB maps suitable Grid nodes and Protein DataBank)

MEG(MagnetoEncephaloGraphy) Data Analysis on the Grid: Brain

Activity Analysis

Life-electronics laboratory,AIST

Data Analysis

•Provision of expertise in the analysis of brain function•Provision of MEG analysis

Data Generation

Nimrod-G

64 sensors MEG

Results

Analysis All pairs (64x64) of MEG data by shifting the temporal region of MEG data over time: 0 to 29750: 64x64x29750 jobs

World-Wide Grid•[deadline, budget, optimization preference]

1

5

4

3

2

[Collaboration with Osaka University, Japan]

SETI@home: Search for Extraterrestrial Intelligence at

Home

Content Sharing – P2P

Collaborative Engineering

Components of an AG NodeComponents of an AG Node

Digital Video

Digital Video

Digital Audio

NETWORK

MixerControl

Computer

NTSC Video

RGB Video

Analog Audio

Video Capture

Computer

DisplayComputer

AudioCapture

Computer

EchoCanceller

Rick Stevens & Team, ANL

•Group to group interactions.•Human collaboration across distributed locations •Remote visualizations, virtual meeting, seminars,etc.•Uses grid technologies for secure communication etc.•May have interaction with scientific apps.

Access GRID: http://www-fp.mcs.anl.gov/fl/accessgrid/

Image-Renderinghttp://www.swin.edu.au/astronomy/pbourke/povray/

parallel

Parallelisation of Image Rendering

Image splitting (by rows, columns, and checker)

Each segment can be concurrently processed on different nodes and render image as segments are processed.

Scheduling (need load balancing)

Each row rendering takes different times depending on image nature – e.g, rendering rows across the sky take less time compared to those that intersect the interesting parts of the image.

Rending apps can be implemented using MPI, PVM, or p-study tools like Nimrod and schedule.

Data Intensive Computinge.g., CERN Data Grid initiative

CERN Large Hadron Collider - circular particle accelerator to be placed in 27 km long tunnel in

2005.

Conclude with a comparison with the Electrical Grid………..

Where we are ????

Alessandro Volta in Paris in 1801 inside French National Institute shows the

battery while in the presence of Napoleon I

Fresco by N. Cianfanelli (1841) (Zoological Section "La Specula" of National History Museum of Florence

University)

….and in the future, I imagine a worldwidePower (Electrical) Grid …...

What ?!?!This is a mad man…

Oh, monDieu !

2000 - 1801 = 199 Years

What will be the dominant Grid approach in the next future ??

”The Computational Grid” is analogous to

Electricity (Power) Grid and the vision is to offer a

(almost) dependable, consistent, pervasive, and

inexpensive access to high-end resources

irrespective their location of physical existence and

the location of access.

Trends

It is very difficult to predict the future and this is particular true in a field such as Information Technology

“I think there is a world market for about five computers.”Thomas J. Watson Sr., IBM Founder, 1943

Trends

The time is exciting but the way ahead may be hard and

long….!

Grid

The Grid Impact!

“The global computational grid is expected to drive the economy of

the 21st century similar to the electric power grid that drove the

economy of the 20th century”

Future Grid Scenarios

Access to any resources, for anyone, anywhere, anytime, from any platform – portal (super) computing .

Application access to resources from the wall socket!

Many applications provide solutions in real-time.

Choice of working: office vs home vs . . . Collaboratories for distributed teams. Monitoring and steering applications

through wireless devices (PDAs etc.).

Final Summary

There are currently a large number of projects and diverse range of emerging Grid developmental approaches being pursued.

These range from metacomputing frameworks to application testbeds, and from collaborative environments to batch submission mechanisms.

Conclusions

The HPC will be dominated by Peer-to-Peer Grid of clusters.

Adaptive, scalable, and easy to use Systems and End-User applications will be prominent.

Access electricity, internet, entertainment (music, movie,…), etc. from the wall socket!

An Economics –based Service Oriented Grid Computing computing needed for eventual success of Grids!

The impact of Grid on 21st century economy will be the same as electricity on 20th century economy.

Further Information

Books: High Performance Cluster Computing, V1,

V2, R.Buyya (Ed), Prentice Hall, 1999. The GRID, I. Foster and C. Kesselman

(Eds), Morgan-Kaufmann, 1999.

IEEE Task Force on Cluster Computing

http://www.ieeetfcc.org GRID Forums

www.gridforum.org, www.egrid.org

CCGRID 2001, www.ccgrid.org GRID Meeting - www.gridcomputing.org

Further Information

Cluster Computing Infoware: http://www.buyya.com/cluster/

Grid Computing Infoware: http://www.gridcomputing.com

IEEE DS Online - Grid Computing area:

http://computer.org/dsonline/gc

Millennium Compute Power Grid/Market Project

http://www.ComputePower.com

Thank You… Any ??

Thank You… Any ??