Grid computing by vaishali sahare [katkar]

By,
Mrs. Vaishali Sahare (Katkar)
HoD Computer Science & Engineering
G H Raisoni Institute of Engineering & Technology, Nagpur.
G H RAISONI INSTITUTE OF ENGINEERING
AND TECHNOLOGY, NAGPUR.
Affiliated to RTM Nagpur University, Nagpur and NAAC Accredited.
GRID COMPUTING

OUTLINE
• Introduction to Grid Computing
• How Grid works?
• Grid Applications
• Grid Topologies
• Methods of Grid computing
• Grid Middleware
• Grid Architectures and Protocols
• GridSim Simulation
• Grid ToolKits
Mrs. Vaishali N. Sahare

Grid computing is a computer network in
which each computer's resources are shared
with every other computer in the system.
Processing power, memory and data storage are
all community resources that authorized users
can tap into and leverage for specific tasks.
Introduction to Grid Computing

Introduction to Grid Computing
 Grid 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

Source: “Evolving Computing Model: Grid Computing” Michael Teyssedre
How Grid Computing works?

How Grid Computing works?
Source: “Evolving Computing Model: Grid Computing” Michael Teyssedre

Grid computing by vaishali sahare [katkar]

How it evolves?

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.
Benefits
 Exploit Underutilized resources
 Resource load Balancing
 Virtualize resources across an enterprise
 Data Grids, Compute Grids
 Enable collaboration for virtual organizations

Grid Applications
Data and computationally intensive applications:
•This technology has been applied to computationally-intensive scientific,
mathematical, and academic problems like drug discovery, economic
forecasting, seismic analysis back office data processing in support of e-
commerce
•A chemist may utilize hundreds of processors to screen thousands 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, …

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

Grid Topologies: IntraGrid

Grid Topologies: ExtraGrid

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)

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).

Methods of Grid Computing
•Distributed Supercomputing
•High-Throughput Computing
•On-Demand Computing
•Data-Intensive Computing
•Collaborative Computing
•Logistical Networking

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.

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.
 Uses grid capabilities to meet short-term
requirements for resources that are not
locally accessible.
 Models real-time computing demands.
On-Demand Computing

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Collaborative Computing
• Concerned primarily with enabling and enhancing
human-to-human interactions.
• Applications are often structured in terms of a
virtual shared space.
 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.
Data-Intensive Computing

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.

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.

A typical view of Grid environment
User Resource 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 Service
system 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

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 status
monitoring and control

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

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

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

Grid in India- GARUDA
• GARUDA is India’s Grid Computing
initiative connecting 17 cities across the
country.
• The 45 participating institutes in this
nationwide project include all the IITs
and C-DAC centers and other major
institutes in India.

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
Core
services
Local OS
A p p l i c a t i o n s

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
InternetProtocolArchitecture

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

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

Simulation tool
 GridSim is a Java-based toolkit for modeling, and
simulation of distributed resource management and
scheduling for conventional Grid environment.
 GridSim is based on SimJava, a general purpose
discrete-event simulation package implemented in
Java.
 All components in GridSim communicate with each
other through message passing operations defined by
SimJava.

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 application models can
be simulated.

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 jobs that can
be submitted to a resource.
• Multiple user entities can submit tasks for execution
simultaneously in the same resource, which may be time-
shared or space-shared. This feature helps in building
schedulers that can use different market-driven economic
models for selecting services competitively.
• Network speed between resources can be specified.
• It supports simulation of both static and dynamic schedulers.
• Statistics of all or selected operations can be recorded and
they can be analyzed using GridSim statistics analysis
methods.

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

GLOBUS Grid ToolKit

Data Management in GLOBUS TOOLKIT

GRID FTP

More GRID FTP Features

Stripping Architecture

Limitations of GRID FTP

Reliable File Transfer (RFT)

Replica Location Service (RLS)

Data Replication Service (DRS)

CONDOR

NIMROD-G

Earth System GRID

Globus Grid ToolKit

LIGO Solution

TERA GRID

References
•Images: www.google.com
•Images: “Evolving Computing Model: Grid
Computing” Michael Teyssedre
•Howstuffworks.com
•IBM Grid computing sample video-
youtube.com

Grid computing by vaishali sahare [katkar]

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