1. GRID COMPUTING

GRID COMPUTING

Sandeep Kumar PooniaHead Of Dept. CS/IT

B.E., M.Tech., UGC-NET

LM-IAENG, LM-IACSIT,LM-CSTA, LM-AIRCC, LM-SCIEI, AM-UACEE

WHY GRID COMPUTING?

40% Mainframes are idle

90% Unix servers are idle

95% PC servers are idle

0-15% Mainframes are idle in peak-hour

70% PC servers are idle in peak-hour

Source: “Grid Computing” Dr Daron G Green

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OUTLINE

Introduction to Grid Computing

Methods of Grid computing

Grid Middleware

Grid Architecture

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ELECTRICAL POWER GRID

ANALOGY

Electrical power

grid

users (or electrical

appliances) get access to

electricity through wall

sockets with no care or

consideration for where or

how the electricity is

actually generated.

“The power grid” links

together power plants of

many different kinds

The Grid users (or client applications) gain

access to computing resources (processors, storage, data, applications, and so on) as needed with little or no knowledge of where those resources are located or what the underlying technologies, hardware, operating system, and so on are

"the Grid" links together computing resources (PCs, workstations, servers, storage elements) and provides the mechanism needed to access them.

Sandeep Kumar Poonia

WHY NEED GRID COMPUTING?

Core networking technology now accelerates at a much

faster rate than advances in microprocessor speeds

Exploiting under utilized resources

Parallel CPU capacity

Virtual resources and virtual organizations for

collaboration

Access to additional resources

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WHO NEEDS GRID COMPUTING?

Not just computer scientists…

scientists “hit the wall” when faced with situations: The amount of data they need is huge and the data is stored in

different institutions.

The amount of similar calculations the scientist has to do is huge.

Other areas: Government

Business

Education

Industrial design

……

LIVING IN AN EXPONENTIAL WORLD

(1) COMPUTING & SENSORS

Moore‘s Law: transistor count doubles each 18 months

Magnetohydro-dynamicsstar formation

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LIVING IN AN EXPONENTIAL WORLD:

(2) STORAGE

Storage density doubles every 12 months

Dramatic growth in online data (1 petabyte =

1000 terabyte = 1,000,000 gigabyte)

2000 ~0.5 petabyte

2005 ~10 petabytes

2010 ~100 petabytes

2015 ~1000 petabytes?

Transforming entire disciplines in physical and,

increasingly, biological sciences; humanities

next?

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DATA INTENSIVE PHYSICAL SCIENCES

High energy & nuclear physics

Including new experiments at CERN

Gravity wave searches

LIGO, GEO, VIRGO

Time-dependent 3-D systems (simulation, data)

Earth Observation, climate modeling

Geophysics, earthquake modeling

Fluids, aerodynamic design

Pollutant dispersal scenarios

Astronomy: Digital sky surveys

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ONGOING ASTRONOMICAL MEGA-SURVEYS

Large number of new surveys

Multi-TB in size, 100M objects or larger

In databases

Individual archives planned and under way

Multi-wavelength view of the sky

> 13 wavelength coverage within 5 years

Impressive early discoveries

Finding exotic objects by unusual colors

L,T dwarfs, high redshift quasars

Finding objects by time variability

Gravitational micro-lensing

MACHO

2MASS

SDSS

DPOSS

GSC-II

COBE MAP

NVSS

FIRST

GALEX

ROSAT

OGLE

...

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COMING FLOODS OF ASTRONOMY DATA

The planned Large Synoptic Survey Telescope

will produce over 10 petabytes per year by 2008!

All-sky survey every few days, so will have fine-grain

time series for the first time

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DATA INTENSIVE BIOLOGY AND MEDICINE

Medical data

X-Ray, mammography data, etc. (many petabytes)

Digitizing patient records

X-ray crystallography

Molecular genomics and related disciplines

Human Genome, other genome databases

Proteomics (protein structure, activities, …)

Protein interactions, drug delivery

Virtual Population Laboratory (proposed)

Simulate likely spread of disease outbreaks

Brain scans (3-D, time dependent)

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And comparisons must bemade among many

We need to get to one micron to know location of every cell. We’re just now starting to get to 10 microns – Grids will help get us there and further

A BRAIN

IS A LOT

OF DATA!(MARK ELLISMAN, UCSD)

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Fastest virtual supercomputersSandeep K

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As of April 2013, Folding@home – 11.4 x86-equivalent(5.8 "native") PFLOPS.As of March 2013, BOINC – processing on average 9.2PFLOPS.As of April 2010, MilkyWay@Home computes at over1.6 PFLOPS, with a large amount of this work coming fromGPUs.As of April 2010, SETI@Home computes data averagesmore than 730 TFLOPS.As of April 2010, Einstein@Home is crunching more than210 TFLOPS.As of June 2011, GIMPS is sustaining 61 TFLOPS.

HOW GRID COMPUTING WORKS

Super computer, Big mainframe…

Idol timeIdol CPU

Idol CPUIdol time

Source: “The Evolving Computing Model: Grid Computing” Michael Teyssedre

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HOW GRID COMPUTING WORKS

Virtual machineVirtual CPU…

Idol timeIdol CPU

Idol CPUIdol time

Source: “The Evolving Computing Model: Grid Computing” Michael Teyssedre

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HOW GRID COMPUTING WORKS

Grid Computing

0% idol0% idol

0% idol0% idol

Source: “The Evolving Computing Model: Grid Computing” Michael Teyssedre

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GRID ARCHITECTURE

Autonomous, globally distributed computers/clusters

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WHAT IS A GRID? Many definitions exist in the literature

Early defs: Foster and Kesselman, 1998

―A computational grid is a hardware and software

infrastructure that provides dependable, consistent,

pervasive, and inexpensive access to high-end

computational facilities‖

Kleinrock 1969:

―We will probably see the spread of ‗computer utilities‘,

which, like present electric and telephone utilities, will

service individual homes and offices across the country.‖

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3-POINT CHECKLIST (FOSTER 2002)

1. Coordinates resources not subject to

centralized control

2. Uses standard, open, general purpose protocols

and interfaces

3. Deliver nontrivial qualities of service

• e.g., response time, throughput, availability,

security

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DEFINITION

Grid computing is…

A distributed computing system

Where a group of computers are connected

To create and work as one large virtual

computing power, storage, database, application,

and service

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DEFINITION

Grid computing…

Allows a group of computers to share the system

securely and

Optimizes their collective resources to meet

required workloads

By using open standards

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GRID COMPUTINGGrid computing is a form of distributed computing whereby a "super and virtual computer" is composed of a cluster of networked, loosely coupled computers, acting in concert to perform very large tasks.

Grid computing (Foster and Kesselman, 1999) is a growing technology that facilitates the executions of large-scale resource intensive applications on geographically distributed computing resources.

Facilitates flexible, secure, coordinated large scale resource sharing among dynamic collections of individuals, institutions, and resource

Enable communities (―virtual organizations‖) to share geographically distributed resources as they pursue common goals

Ian Foster and Carl Kesselman

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A COMPARISON

SERIAL

Fetch/Store

Compute

PARALLEL

Fetch/Store

Compute/

communicate

Cooperative game

GRID

Fetch/Store

Discovery of Resources

Interaction with remote

application

Authentication /

Authorization

Security

Compute/Communicate

Etc

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DISTRIBUTED COMPUTING VS. GRID

Grid is an evolution of distributed computing

Dynamic

Geographically independent

Built around standards

Internet backbone

Distributed computing is an ―older term‖

Typically built around proprietary software and network

Tightly couples systems/organization

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WEB VS.

GRID

Web

Uniform naming access to documents

Grid - Uniform, high performance access to computational

resources

Colleges/R&D

Labs

Software

Catalogs

Sensor nets

http://

http://

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IS THE WORLD WIDE WEB A

GRID ?

Seamless naming? Yes

Uniform security and Authentication? No

Information Service? Yes or No

Co-Scheduling? No

Accounting & Authorization ? No

User Services? No

Event Services? No

Is the Browser a Global Shell ? No

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WHAT DOES THE WORLD WIDE WEB BRING TO

THE GRID ?

Uniform Naming

A seamless, scalable information service

A powerful new meta-data language: XML

XML will be standard language for

describing information in the grid

SOAP – simple object access protocol

Uses XML for encoding. HTML for protocol

SOAP may become a standard RPC

mechanism for Grid services

Uses XML for encoding. HTML for protocol

Portal Ideas

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THE ULTIMATE GOAL

In future I will not know or care

where my application will be

executed as I will acquire and pay

to use these resources as I need

them

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WHY GRIDS? Large-scale science and engineering are done

through the interaction of people, heterogeneous

computing resources, information systems, and

instruments, all of which are geographically and

organizationally dispersed.

The overall motivation for ―Grids‖ is to facilitate

the routine interactions of these resources in order

to support large-scale science and Engineering.

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AN EXAMPLE VIRTUAL ORGANIZATION:

CERN‘S LARGE HADRON COLLIDER

1800 Physicists, 150 Institutes, 32 Countries

100 PB of data by 2010; 50,000 CPUs?

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GRID COMMUNITIES & APPLICATIONS:

DATA GRIDS FOR HIGH ENERGY PHYSICS

Tier2 Centre ~1 TIPS

Online System

Offline Processor Farm

~20 TIPS

CERN Computer Centre

FermiLab ~4 TIPSFrance Regional Centre

Italy Regional Centre

Germany Regional Centre

InstituteInstituteInstituteInstitute ~0.25TIPS

Physicist workstations

~100 MBytes/sec

~100 MBytes/sec

~622 Mbits/sec

~1 MBytes/sec

There is a “bunch crossing” every 25 nsecs.

There are 100 “triggers” per second

Each triggered event is ~1 MByte in size

Physicists work on analysis “channels”.

Each institute will have ~10 physicists working on one or more channels; data for these channels should be cached by the institute server

Physics data cache

~PBytes/sec

~622 Mbits/sec or Air Freight (deprecated)

Tier2 Centre ~1 TIPS

Tier2 Centre ~1 TIPS

Tier2 Centre ~1 TIPS

Caltech ~1 TIPS

~622 Mbits/sec

Tier 0

Tier 1

Tier 2

Tier 4

1 TIPS is approximately 25,000

SpecInt95 equivalents

www.griphyn.org www.ppdg.net www.eu-datagrid.org

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INTELLIGENT INFRASTRUCTURE:

DISTRIBUTED SERVERS AND SERVICES

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Early 90s

Gigabit testbeds, metacomputing

Mid to late 90s

Early experiments (e.g., I-WAY), academic software projects (e.g., Globus, Legion), application experiments

2002

Dozens of application communities & projects

Major infrastructure deployments

Significant technology base (esp. Globus ToolkitTM)

Growing industrial interest

Global Grid Forum: ~500 people, 20+ countries

THE GRID:

A BRIEF HISTORY

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HOW IT EVOLVES

Utility computing

Service grid

Data grid

Processing grid

VirtualizationService-orientedOpen standard

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EARLY ADOPTERS

Academic

Big science

Life science

Nuclear engineering

Simulation…

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MARKET POTENTIAL

Financial services:

risk management and compliance

Automotive:

acceleration of product development

Petroleum:

discovery of oils

Source: “Perspectives on grid: Grid computing - next-generation distributed computing" Matt Haynos, 01/27/04

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Criteria for a Grid:

Coordinates resources that are not subject to

centralized control.

Uses standard, open, general-purpose protocols

and interfaces.

Delivers nontrivial qualities of service.e.g., response time, throughput, availability, security

Benefits

Exploit Underutilized resources

Resource load Balancing

Virtualize resources across an enterpriseData Grids, Compute Grids

Enable collaboration for virtual organizations

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WHY DO WE NEED GRIDS?

Many large-scale problems cannot be solved by a

single computer

Globally distributed data and resources

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GRID APPLICATIONSData and computationally intensive applications:

This technology has been applied to computationally-intensive scientific, mathematical, and academic problemslike drug discovery, economic forecasting, seismic analysisback office data processing in support of e-commerce

A chemist may utilize hundreds of processors to screenthousands of compounds per hour.

Teams of engineers worldwide pool resources to analyze terabytes of structural data.

Meteorologists seek to visualize and analyze petabytes of climate data with enormous computational demands.

Resource sharing

Computers, storage, sensors, networks, …

Sharing always conditional: issues of trust, policy, negotiation, payment, …

Coordinated problem solving

distributed data analysis, computation, collaboration, …

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GRID TOPOLOGIES

• Intragrid

– Local grid within an organisation

– Trust based on personal contracts

• Extragrid

– Resources of a consortium of organisations

connected through a (Virtual) Private Network

– Trust based on Business to Business contracts

• Intergrid

– Global sharing of resources through the internet

– Trust based on certification

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COMPUTATIONAL GRID

―A computational grid is a hardware and software infrastructure

that provides dependable, consistent, pervasive, and inexpensive

access to high-end computational capabilities.‖

‖The Grid: Blueprint for a New Computing Infrastructure‖,

Kesselman & Foster

Example : Science Grid (US Department of Energy)

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DATA GRID

A data grid is a grid computing system that deals with data — the controlled sharing and management of large amounts of distributed data.

Data Grid is the storage component of a grid environment. Scientific and engineering applications require access to large amounts of data, and often this data is widely distributed. A data grid provides seamless access to the local or remote data required to complete compute intensive calculations.

Example :

Biomedical informatics Research Network (BIRN),

the Southern California earthquake Center (SCEC).

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BACKGROUND: RELATED

TECHNOLOGIES

Cluster computing

Peer-to-peer computing

Internet computing

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CLUSTER COMPUTING

Idea: put some PCs together and get them to

communicate

Cheaper to build than a mainframe

supercomputer

Different sizes of clusters

Scalable – can grow a cluster by adding more PCs

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CLUSTER ARCHITECTURE

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PEER-TO-PEER COMPUTING

Connect to other computers

Can access files from any computer on the

network

Allows data sharing without going through

central server

Decentralized approach also useful for Grid

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PEER TO PEER ARCHITECTURE

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METHODS OF GRID COMPUTING

Distributed Supercomputing

High-Throughput Computing

On-Demand Computing

Data-Intensive Computing

Collaborative Computing

Logistical Networking

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DISTRIBUTED SUPERCOMPUTING

Combining multiple high-capacity resources on a computational grid into a single, virtual distributed supercomputer.

Tackle problems that cannot be solved on a single system.

Examples: climate modeling, computational chemistry

Challenges include: Scheduling scarce and expensive resources

Scalability of protocols and algorithms

Maintaining high levels of performance across heterogeneous systems

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HIGH-THROUGHPUT COMPUTING

Uses the grid to schedule large numbers of

loosely coupled or independent tasks, with the

goal of putting unused processor cycles to

work.

Schedule large numbers of independent tasks

Goal: exploit unused CPU cycles (e.g., from

idle workstations)

Unlike distributed computing, tasks loosely

coupled

Examples: parameter studies, cryptographic

problems

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On-Demand Computing

Uses grid capabilities to meet short-term

requirements for resources that are not

locally accessible.

Models real-time computing demands.

Use Grid capabilities to meet short-term requirements for resources that cannot conveniently be located locally

Unlike distributed computing, driven by cost-performance concerns rather than absolute performance

Dispatch expensive or specialized computations to remote servers

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COLLABORATIVE COMPUTING

Concerned primarily with enabling and

enhancing human-to-human interactions.

Enable shared use of data archives and

simulations

Applications are often structured in terms of a

virtual shared space.

Examples:

Collaborative exploration of large geophysical data sets

Challenges:

Real-time demands of interactive applications

Rich variety of interactions

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Data-Intensive Computing

The focus is on synthesizing new information

from data that is maintained in geographically

distributed repositories, digital libraries, and

databases.

Particularly useful for distributed data mining. Examples:

•High energy physics generate terabytes of distributed data, need complex queries to detect “interesting” events•Distributed analysis of Sloan Digital Sky Survey data

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LOGISTICAL NETWORKING

Logistical networks focus on exposing storage resources inside networks by optimizing the global scheduling of data transport, and data storage.

Contrasts with traditional networking, which does not explicitly model storage resources in the network.

high-level services for Grid applications

Called "logistical" because of the analogy it bears with the systems of warehouses, depots, and distribution channels.

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P2P COMPUTING VS GRID

COMPUTING

Differ in Target Communities

Grid system deals with more complex, more

powerful, more diverse and highly interconnected

set of resources than

P2P.

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A TYPICAL VIEW OF GRID

ENVIRONMENT

UserResource Broker

Grid Resources

Grid Information Service

A User sends computation or data intensive application to Global Grids in order to speed up the execution of the application.

A Resource Broker distribute the jobs in an application to the Grid resources based on user’s QoS requirements and details of available Grid resources for further executions.

Grid Resources (Cluster, PC, Supercomputer, database, instruments, etc.) in the Global Grid execute the user jobs.

Grid Information Servicesystem collects the details of the available Grid resources and passes the information to the resource broker.

Computation result

Grid application

Computational jobs

Details of Grid resources

Processed jobs

1

2

3

4

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GRID MIDDLEWARE

Grids are typically managed by grid ware -

a special type of middleware that enable sharing and manage grid components based on user requirements and resource attributes (e.g., capacity, performance)

Software that connects other software components or applications to provide the following functions:

Run applications on suitable available resources– Brokering, Scheduling

Provide uniform, high-level access to resources– Semantic interfaces– Web Services, Service Oriented Architectures

Address inter-domain issues of security, policy, etc.– Federated Identities

Provide application-level statusmonitoring and control

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MIDDLEWARES

Globus –chicago Univ

Condor – Wisconsin Univ – High throughput computing

Legion – Virginia Univ – virtual workspaces-collaborative computing

IBP – Internet back pane – Tennesse Univ –logistical networking

NetSolve – solving scientific problems in heterogeneous env – high throughput & data intensive

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TWO KEY GRID COMPUTING GROUPS

The Globus Alliance (www.globus.org) Composed of people from:

Argonne National Labs, University of Chicago, University of Southern California Information Sciences Institute, University of Edinburgh and others.

OGSA/I standards initially proposed by the Globus Group

The Global Grid Forum (www.ggf.org) Heavy involvement of Academic Groups and Industry

(e.g. IBM Grid Computing, HP, United Devices, Oracle, UK e-Science Programme, US DOE, US NSF, Indiana University, and many others)

Process Meets three times annually Solicits involvement from industry, research groups, and

academics

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GRID USERS

Many levels of users

Grid developers

Tool developers

Application developers

End users

System administrators

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SOME GRID CHALLENGES

Data movement

Data replication

Resource management

Job submission

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SOME OF THE MAJOR GRID PROJECTS

Name URL/Sponsor Focus

EuroGrid, Grid

Interoperability

(GRIP)

eurogrid.org

European Union

Create tech for remote access to super

comp resources & simulation codes; in

GRIP, integrate with Globus Toolkit™

Fusion Collaboratory fusiongrid.org

DOE Off. Science

Create a national computational

collaboratory for fusion research

Globus Project™ globus.org

DARPA, DOE,

NSF, NASA, Msoft

Research on Grid technologies;

development and support of Globus

Toolkit™; application and deployment

GridLab gridlab.org

European Union

Grid technologies and applications

GridPP gridpp.ac.uk

U.K. eScience

Create & apply an operational grid within the

U.K. for particle physics research

Grid Research

Integration Dev. &

Support Center

grids-center.org

NSF

Integration, deployment, support of the NSF

Middleware Infrastructure for research &

education

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Grid in India-GARUDA

•GARUDA is India's Grid Computinginitiative connecting 17 cities across thecountry.•The 45 participating institutes in thisnationwide project include all the IITs andC-DAC centers and other major institutesin India.

GLOBUS GRID TOOLKIT

Open source toolkit for building Grid systems and

applications

Enabling technology for the Grid

Share computing power, databases, and other tools securely

online

Facilities for:

Resource monitoring

Resource discovery

Resource management

Security

File management

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DATA MANAGEMENT IN GLOBUS

TOOLKIT

Data movement

GridFTP

Reliable File Transfer (RFT)

Data replication

Replica Location Service (RLS)

Data Replication Service (DRS)

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GRIDFTP High performance, secure, reliable data transfer protocol

Optimized for wide area networks

Superset of Internet FTP protocol

Features:

Multiple data channels for parallel transfers

Partial file transfers

Third party transfers

Reusable data channels

Command pipelining

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MORE GRIDFTP FEATURES

Auto tuning of parameters

Striping

Transfer data in parallel among multiple senders and

receivers instead of just one

Extended block mode

Send data in blocks

Know block size and offset

Data can arrive out of order

Allows multiple streams

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STRIPING ARCHITECTURE

Use ―Striped‖ servers

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LIMITATIONS OF GRIDFTP

Not a web service protocol (does not employ

SOAP, WSDL, etc.)

Requires client to maintain open socket

connection throughout transfer

Inconvenient for long transfers

Cannot recover from client failures

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GRIDFTP

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RELIABLE FILE TRANSFER (RFT)

Web service with ―job-scheduler‖ functionality for data

movement

User provides source and destination URLs

Service writes job description to a database and moves

files

Service methods for querying transfer status

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RFT

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REPLICA LOCATION SERVICE (RLS)

Registry to keep track of where replicas exist on physical

storage system

Users or services register files in RLS when files created

Distributed registry

May consist of multiple servers at different sites

Increase scale

Fault tolerance

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REPLICA LOCATION SERVICE (RLS)

Logical file name – unique identifier for contents of file

Physical file name – location of copy of file on storage system

User can provide logical name and ask for replicas

Or query to find logical name associated with physical file location

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DATA REPLICATION SERVICE (DRS) Pull-based replication capability

Implemented as a web service

Higher-level data management service built on top of RFT

and RLS

Goal: ensure that a specified set of files exists on a storage

site

First, query RLS to locate desired files

Next, creates transfer request using RFT

Finally, new replicas are registered with RLS

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CONDOR

Original goal: high-throughput computing

Harvest wasted CPU power from other machines

Can also be used on a dedicated cluster

Condor-G – Condor interface to Globus resources

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CONDOR

Provides many features of batch systems:

job queueing

scheduling policy

priority scheme

resource monitoring

resource management

Users submit their serial or parallel jobs

Condor places them into a queue

Scheduling and monitoring

Informs the user upon completion

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NIMROD-G Tool to manage execution of parametric studies across

distributed computers

Manages experiment

Distributing files to remote systems

Performing the remote computation

Gathering results

User submits declarative plan file

Parameters, default values, and commands necessary for

performing the work

Nimrod-G takes advantage of Globus toolkit features

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NIMROD-G ARCHITECTURE

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GRID CASE STUDIES

Earth System Grid

LIGO

TeraGrid

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EARTH SYSTEM GRID

Provide climate studies scientists with access to

large datasets

Data generated by computational models –

requires massive computational power

Most scientists work with subsets of the data

Requires access to local copies of data

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ESG INFRASTRUCTURE

Archival storage systems and disk storage systems at

several sites

Storage resource managers and GridFTP servers to

provide access to storage systems

Metadata catalog services

Replica location services

Web portal user interface

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EARTH SYSTEM GRID

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EARTH SYSTEM GRID INTERFACE

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LASER INTERFEROMETER

GRAVITATIONAL WAVE

OBSERVATORY (LIGO)

Instruments at two sites to detect gravitational waves

Each experiment run produces millions of files

Scientists at other sites want these datasets on local storage

LIGO deploys RLS servers at each site to register local

mappings and collect info about mappings at other sites

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LARGE SCALE DATA REPLICATION

FOR LIGO

Goal: detection of gravitational waves

Three interferometers at two sites

Generate 1 TB of data daily

Need to replicate this data across 9 sites to make

it available to scientists

Scientists need to learn where data items are,

and how to access them

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LIGO

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LIGO SOLUTION

Lightweight data replicator (LDR)

Uses parallel data streams, tunable TCP windows, and

tunable write/read buffers

Tracks where copies of specific files can be found

Stores descriptive information (metadata) in a

database

Can select files based on description rather than filename

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TERAGRID

NSF high-performance computing facility

Nine distributed sites, each with different

capability , e.g., computation power, archiving

facilities, visualization software

Applications may require more than one site

Data sizes on the order of gigabytes or terabytes

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TERAGRID

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TERAGRID

Solution: Use GridFTP and RFT with front end

command line tool (tgcp)

Benefits of system:

Simple user interface

High performance data transfer capability

Ability to recover from both client and server software

failures

Extensible configuration

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TGCP DETAILS

Idea: hide low level GridFTP commands from users

Copy file smallfile.dat in a working directory to another

system:

tgcp smallfile.dat tg-login.sdsc.teragrid.org:/users/ux454332

GridFTP command:

globus-url-copy -p 8 -tcp-bs 1198372 \

gsiftp://tg-gridftprr.uc.teragrid.org:2811/home/navarro/smallfile.dat

\

gsiftp://tg-login.sdsc.teragrid.org:2811/users/ux454332/smallfile.dat

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GRID ARCHITECTURE

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THE HOURGLASS MODEL

Focus on architecture issues

Propose set of core services as

basic infrastructure

Used to construct high-level,

domain-specific solutions

(diverse)

Design principles

Keep participation cost low

Enable local control

Support for adaptation

―IP hourglass‖ model

Diverse global services

Coreservices

Local OS

A p p l i c a t i o n s

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LAYERED GRID ARCHITECTURE

(BY ANALOGY TO INTERNET ARCHITECTURE)

Application

Fabric“Controlling things locally”: Access to, & control of, resources

Connectivity“Talking to things”: communication (Internet protocols) & security

Resource“Sharing single resources”: negotiating access, controlling use

Collective“Coordinating multiple resources”: ubiquitous infrastructure services, app-specific distributed services

Internet

Transport

Application

Link

Inte

rnet P

roto

col A

rchite

ctu

re

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EXAMPLE:

DATA GRID ARCHITECTURE

Discipline-Specific Data Grid Application

Coherency control, replica selection, task management, virtual data catalog, virtual data code catalog, …

Replica catalog, replica management, co-allocation, certificate authorities, metadata catalogs,

Access to data, access to computers, access to network performance data, …

Communication, service discovery (DNS), authentication, authorization, delegation

Storage systems, clusters, networks, network caches, …

Collective(App)

App

Collective(Generic)

Resource

Connect

Fabric

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SIMULATION TOOLS

GridSim – job scheduling

SimGrid – single client multiserver

scheduling

Bricks – scheduling

GangSim- Ganglia VO

OptoSim – Data Grid Simulations

G3S – Grid Security services Simulator –

security services

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SIMULATION TOOL

GridSim is a Java-based toolkit for modeling,and simulation of distributed resourcemanagement and scheduling for conventionalGrid environment.

GridSim is based on SimJava, a generalpurpose discrete-event simulation packageimplemented in Java.

All components in GridSim communicate witheach other through message passing operationsdefined by SimJava.

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SALIENT FEATURES OF THE GRIDSIM

It allows modeling of heterogeneous types of resources.

Resources can be modeled operating under space-or time-shared mode.

Resource capability can be defined (in the form of MIPS (Million Instructions Per Second) benchmark.

Resources can be located in any time zone.

Weekends and holidays can be mapped depending on resource‘s local time to model non-Grid (local) workload.

Resources can be booked for advance reservation.

Applications with different parallel applicationmodels can be simulated.

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SALIENT FEATURES OF THE GRIDSIM Application tasks can be heterogeneous and they can

be CPU or I/O intensive.

There is no limit on the number of application jobsthat can be submitted to a resource.

Multiple user entities can submit tasks for executionsimultaneously in the same resource, which may betime-shared or space-shared. This feature helps inbuilding schedulers that can use different market-driven economic models for selecting servicescompetitively.

Network speed between resources can be specified.

It supports simulation of both static and dynamicschedulers.

Statistics of all or selected operations can be recordedand they can be analyzed using GridSim statisticsanalysis methods.

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A MODULAR ARCHITECTURE FOR GRIDSIM

PLATFORM AND COMPONENTS.

Appn Conf Res Conf User Req Grid Sc Output

Application, User, Grid Scenario’s input and Results

Grid Resource Brokers or Schedulers

…

Appn

modeling

Res entity Info serv Job mgmt Res alloc Statis

GridSim Toolkit

Single

CPU

SMPs Clusters Load Netw Reservation

Resource Modeling and Simulation

SimJava Distributed SimJava

Basic Discrete Event Simulation Infrastructure

PCs Workstation ClustersSMPs Distributed

Resources

Virtual Machine

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Sandeep Kumar Poonia