9. the semantic grid and autonomic grid

GRID COMPUTING

The Semantic Grid and Autonomic Computing

Sandeep Kumar Poonia
Head of Dept. CS/IT, Jagan Nath University, Jaipur
B.E., M. Tech., UGC-NET
LM-IAENG, LM-IACSIT,LM-CSTA, LM-AIRCC, LM-SCIEI, AM-UACEE
10/27/2013


1

Introduction
Metadata

and

Ontology

in

the

Semantic Web
Semantic Web Services

A Layered Structure of the Semantic
Grid
Semantic Grid Activities
Autonomic Computing
10/27/2013


2

Introduction
Relationship between the Semantic Web, Grid
and Semantic Grid

10/27/2013


3

Introduction
The

Semantic

Web:

“an

extension

of

the

current Web in which information is given

well-defined

meaning,

better

enabling

computers and people to work in cooperation”
The aim of the Semantic Web: To augment
unstructured Web content so that it may be
machine-interpretable information to improve

the potential capabilities of Web applications.

10/27/2013


4

Introduction
The aim of the Semantic Grid: To explore the
use of Semantic Web technologies to enrich the

Grid with semantics.
The Semantic Grid is layered on top of the
Semantic Web and the Grid. It is the application
of Semantic Web technologies to the Grid.
The Semantic Grid is layered on top of the
Semantic Web and the Grid. It is the application
of Semantic Web technologies to the Grid.
10/27/2013


5

Introduction
Metadata and ontologies play a critical role in
the

development

of

the

Semantic

Web.

Metadata can be viewed as data that is used to
describe data.
Data can be annotated with metadata to specify
its origin or its history. In the Semantic Grid,
for example, Grid services can be annotated
with metadata associated with an ontology for
automatic service discovery.
10/27/2013


6

Introduction
The Grid is complex in nature because it tries to couple
distributed and heterogeneous resources such as data,
computers,

operating

systems,

database

systems,

applications and special devices, which may run across
multiple

virtual

organizations

to

provide

a

uniform

platform for technical computing.

10/27/2013


7

Introduction
The

techniques

deal

with

complexity,

heterogeneity and uncertainty is referred to
autonomic computing.
An autonomic computing system is one that
has the capabilities of being self-healing,
selfconfiguring,

self-optimizing

and

self-

protecting.

10/27/2013


8

The Semantic Grid
“The Semantic Grid is an extension of the current Grid in
which information and services are given well-defined
and explicitly represented meaning, so that it can be
shared and used by humans and machines, better
enabling computers and people to work in cooperation”
D. De Roure, et. al

Semantics in and on the Grid
• Web Sites
– www.semanticgrid.org
– Setting up the www.semanticgridcafe.org

• GGF Semantic Grid Research Group
(SEM-RG)
– Mailing List: sem-grd@gridforum.org
10/27/2013
9


Semantic Grid trajectory
Demonstration
Phase

Efforts

Systematic Investigation
Phase
Specific experiments
Part of the Architecture
Dagstuhl Seminar

Combe
Chem

Pioneering Phase
Ad-hoc experiments, early
pioneers

SRB

Implicit Semantics
OGSA generation

Grid Resource Ontology
Semantic Grid workshops

GGF Semantic Grid
Research Group
Many workshops

Implicit Semantics
1st generation
10/27/2013
10


Time

From the pioneering phase to
the systematic investigation phase


In the pioneering phase...
– Ontologies and their associated technologies are
not
completely
integrated
in
the
Grid
applications
>They are used as in Semantic Web applications

– But there are
applications

distinctive

features

of

Grid

>Distribution of resources
>Scale
>Resource management and state


In the systematic investigation phase
– We have to take these features into account
– And incorporate semantics as another Grid
resource
– Our proposal is: S-OGSA

10/27/2013
11


Decision making
Knowledge Discovery
Lots
Ontology
building
Workflow
discovery
and design

VO mgt
Resource
discovery &
brokering

Not
much

10/27/2013
12

Flexible &
extensible
metadata
schemas

Not
much

Configuration

Information linking

Provenance

General
language annotation

Grid

Lots

METADATA AND ONTOLOGY IN
THE SEMANTIC WEB
Semantic Web is to convert the current structure of the
Web as a distributed data storage, which is interpretable

only by human beings, into a structure of information
storage

that

can

be

understood

by

computer-based

entities.

In order to convert data into information, metadata has to
be

added

into

context.

The

metadata

contains

the

semantics, the explanation of the data to which it refers.
Metadata and ontology are critical to the development of
the Semantic Web.

10/27/2013


13

THE SEMANTIC WEB

Metadata and ontology in semantic service matching
10/27/2013


14

THE SEMANTIC WEB
Metadata and ontologies play a critical role in the
development of the Semantic Web. An ontology is a

specification of a conceptualization.
In

this

context,

specification

refers

to

an

explicit

representation by some syntactic means.
In contrast to schema languages such as XML Schema,
ontologies try to capture the semantics of a domain by
using knowledge representation primitives, allowing a
computer to fully or partially understand the relationships
between concepts in a domain.
10/27/2013


15

THE SEMANTIC WEB

The layered structure of the Semantic Web

10/27/2013


16

THE SEMANTIC WEB

In summary
Expressive models

SWRL
Inference

Model fusion
OWL

10/27/2013
17

RDF
XML


Annotation

Extensible metadata
schemas that you
don’t have to nail
down

RDF(S)

Integration
Integration

Controlled
vocabularies
Data fusion

THE SEMANTIC WEB
Ontology provide a common vocabulary for a domain
and

define

the

meaning

of

the

terms

and

the

relationships between them.
Ontology is referred to as the shared understanding of

some domain of interest, which is often conceived as a
set of classes (concepts), relations, functions, axioms
and instances. Concepts in the ontology are usually

organized in taxonomies

10/27/2013


18

THE SEMANTIC WEB

 Resource Description Framework (RDF)
 RDF Schema (RDFS)
 DAML+OIL

Language’s

(DARPA

+

Agent

Ontology

Markup

Inference

Layer)
 Web Ontology Language (OWL)

10/27/2013


19

Resource Description Framework
The RDF, developed under the auspices of the W3C,
is an infrastructure that facilitates the encoding,

exchange and reuse of structured metadata.
The

RDF

infrastructure

enables

metadata

interoperability through the design of mechanisms
that support common conventions of semantics,
syntax and structure.

10/27/2013


20

RDF does not stipulate semantics for each resource
description community, but rather provides the ability for

these communities to define metadata elements as
needed.
RDF uses XML as a common syntax for the exchange and
processing of metadata. The XML syntax provides vendor
independence,
readability

user

and

the

extensibility,
ability

to

validation,
represent

human
complex

structures.

10/27/2013


21


RDF development efforts
RDF is the result of a number of metadata communities
bringing together their needs to provide a robust and
flexible architecture for supporting metadata for the
Web.
RDF is a collaborative design effort.
RDF drew upon the XML design as well as proposals
related to XML data submitted by Microsoft’s XML Data

and Netscape’s Meta Content Framework.

10/27/2013


22

The RDF data model
An RDF data model contains resources, properties and
the values of properties.

10/27/2013


23



In RDF, a resource is uniquely identifiable by a
Uniform Resource Identifier (URI).



The properties associated with resources are identified
by property types which have corresponding values.



In RDF, values may be atomic in nature (text strings,
numbers, etc.) or other resources, which in turn may

have their own properties.
RDF is represented as a directed graph in which resources
are identified as nodes, property types are defined as
directed label arcs, and string values are quoted

10/27/2013


24

how to apply the RDF model for representing RDF statements
RDF

Statement

1:

The

author

of

this

paper

(someURI/thispaper) is Sandeep. Figure shows the graph
representation of the RDF statement 1. In this example,
the RDF resource is someURI/thispaper whose property is
author.

 The value of the property is sandeep.

someURI/thispaper

10/27/2013

Author


sandeep

25

RDF Statement 2: The author of this paper (someURI/thispaper)
is another URI whose name is Sandeep.
Figure shows the graph representation of the RDF statement 2. In this
example, the RDF resource is someURI/thispaper whose property is
author. The value of the property is another URI (resource) whose
property is name and the value of the property is Sandeep.

someURI/thispaper

Author

Another URI

Name
Sandeep

10/27/2013


26

The XML description of the second RDF statement 2
<rdf:RDF>
xmlns = “...”
xmlns:rdf = “...”
<rdf:Description about = “someURI/thispaper”>
<authored-by>
<rdf:Description Resource = “anotherURI”>
<name>Sandeep</name>
</rdfDescription>
</authored-by>
</rdf:Description>
</rdf:RDF>

10/27/2013


27

Ontology languages
RDFS


RDF itself is a compassable and extensible standard
for building RDF data models.



However, the modeling primitives offered by RDF
are very limited in supporting the definition of a
specific vocabulary for a data model.



RDF does not provide a way to specify resource and
property types, i.e. it cannot express the classes to
which a resource and its associated properties
belong.

10/27/2013


28

RDFS
The RDFS specification, which is built on top of RDF,

defines further modeling primitives such as:


class (rdfs:Class),



subclass relationship (subClassOf, subPropertyOf ),



domain and range restrictions for property, and



sub-property

(rdfs:ConstraintProperty

and

rdfs:ContainerMembershipProperty).
A

resource

(rdfs:Resource)

is

the

base

class

for

modeling primitives defined in RDFS. RDFS define the
valid properties in a given RDF description, as well as
any characteristics or restrictions of the property-type

values
10/27/2013 themselves


29

DAML+OIL
DARPA Agent Markup Language’s+ Ontology Inference Layer (OIL)

DAML+OIL is an ontology language designed for the
Web, which is built upon XML and RDF, and adds the
familiar ontological primitives of object-oriented and
frame-based systems.

DAML+OIL evolved from a merger of DARPA Agent
Markup Language’s (DAML) initial ontology language
(DAML−ONT), an earlier DAML ontology language, and
the Ontology Inference Layer (OIL), an ontology
language that couples modeling primitives commonly
used in frame-based ontologies, with a simple and
well-defined semantics of an expressive DL.

10/27/2013


30

DAML+OIL
DAML+OIL features:



DAML+OIL has well-defined semantics and clear

properties

via

an

underlying

mapping

to

an

expressive DL. The DL gives DAML+OIL the ability
and flexibility to compose classes and slots to form
new

expressions.

ontology

With

expressed

the
in

support

DAML+OIL

of

DL,
can

an
be

automatically reasoned by a DL reasoning system
such as the FaCT system

10/27/2013


31

DAML+OIL
DAML+OIL features:



DAML+OIL supports the full range of XML Schema

data types. It is tightly integrated with RDFS, e.g.
RDFS is used to express DAML+OIL’s machinereadable specification, and provides a serialization
for DAML+OIL.


A layered architecture for easy manipulation of
the language.



The DAML+OIL axioms are significantly more
extensive than the axioms for either RDF or RDFS.

10/27/2013


32

AUTONOMIC COMPUTING
What is autonomic computing?
Autonomic computing is a self-managing computing
model named after, and patterned on, a human body’s
autonomic nervous system.
An autonomic computing system is one that is resilient,
and able to take both preemptive and post facto

measures to ensure a high quality of service with a
minimum of human intervention, in the same way that
the autonomic nervous system regulates body systems

without conscious input from the individual.

10/27/2013


33

AUTONOMIC COMPUTING
The goal of autonomic computing is to reduce the
complexity in the management of large computing
systems such as the Grid. The Grid needs autonomic
computing for following reasons.

 Complexity:

The Grid is complex in nature
because it tries to couple large-scale disparate,
distributed and heterogeneous resources – such as
data,
computers,
operating
systems,
database
systems, applications and special devices – which may
run across multiple virtual organizations to provide a
uniform computing platform.

 Dynamic nature:

The Grid is a dynamic
computing environment in that resources and services
can join and leave at any time.

10/27/2013


34

AUTONOMIC COMPUTING
Features of autonomic computing systems

10/27/2013


35

AUTONOMIC COMPUTING
1. Self-protection
A

self-protecting

identify

system

hostile

can

behaviour

detect
and

and
take

autonomous actions to protect itself against
intrusive behaviour.
Self-protecting systems, as envisioned, could
safeguard themselves against two types of

behaviour:

accidental

human

errors

and

malicious intentional actions.
10/27/2013


36

AUTONOMIC COMPUTING

2. Self-optimizing
Self-optimizing components in an autonomic system are
able to dynamically tune themselves to meet end-user
or business needs with minimal human intervention.

The tuning actions involve the reallocation of resources
based on load balancing functions and system run-time
state information to improve overall resource utilization
and system performance.

10/27/2013


37

AUTONOMIC COMPUTING

3. Self-healing
Self-healing is the ability of a system to recover from
faults that might cause some parts of it to malfunction.
For a system to be self-healing, it must be able to
recover from a failed component by first detecting and

isolating the failed component, taking it off line, fixing
and reintroducing the fixed or replacement component
into service without any apparent overall disruption.

A self-healing system will also need to predict problems
and take actions to prevent the failure from having an
impact on applications.
10/27/2013


38

AUTONOMIC COMPUTING

4. Self-configuring
Installing, configuring and integrating large, complex
systems is challenging, time consuming and errorprone even for experts.
A self-configuring system can adapt automatically to
dynamically changing environments in that system
components

including

software

components

and

hardware components can be dynamically added to the
system with no disruption of system services and with
minimum human intervention.

10/27/2013


39

AUTONOMIC COMPUTING

5. Open standards
An autonomic system should be based on open
standards and provide a standard way to interoperate

with other systems.

6. Self-learning
An autonomic system should be integrated with a

machine learning component that can build knowledge
rules based on a certain time of the system running to
improve

system

performance,

robustness

and

resilience and anticipating foreseeable failures.

10/27/2013


40

AUTONOMIC COMPUTING

Autonomic computing projects
IBM eLiza
eLiza is IBM’s initiative to add autonomic capabilities

into existing products such as their servers.
An autonomic server will be enhanced with capabilities
such as


the detection and isolation of bad memory chips,
protection against hacker attacks,



automatically configuring itself when new features
are added, and



optimizing CPU,



storage and resources in order to handle different
levels of internal traffic.Kumar Poonia
Sandeep

10/27/2013

41

AUTONOMIC COMPUTING

IBM OptimalGrid
OptimalGrid is middleware that aims to simplify the
creation and management of large scale, connected,
parallel Grid applications.
It incorporates the core tenants of the features of

autonomic computing such as self-configuring, selfhealing and self-optimizing to create an environment
in which it is possible for application developers to

exploit these features, without the need either to build
them or to code external APIs.
10/27/2013


42

AUTONOMIC COMPUTING

Intel Itanium 2
Intel

built

autonomic

into

its

Itanium

computing

2

called

processor
the

features

Machine

of

Check

Architecture (MCA).
The MCA is an infrastructure that allows systems to
continue executing transactions as it recovers from error

conditions.
It has the ability to detect and correct errors and to report
these errors to the operating system. It also has the

capability to analyse data and respond in a way that
provides higher overall system reliability and availability.
10/27/2013


43

AUTONOMIC COMPUTING

Sun N1
The aim of the Sun N1 is to manage N
computers as 1 entity.
The autonomic capability in N1 has been

focused on the automation of software and
hardware installation and configuration for
new business service deployment.

10/27/2013


44

AUTONOMIC COMPUTING

The concept of autonomic Grid services

10/27/2013


45

AUTONOMIC COMPUTING

Core:

This is the core component of the service to

provide

core

functionality

such

as

performing

a

computation.

Advertising:

The advertising component registers

the service name, domain-related problems it could
solve with a registry server in a Grid system. Domain
problems can be annotated with metadata.

10/27/2013


46

AUTONOMIC COMPUTING
Self-learning:

Based on a certain time of running of

the service, this component can gain some knowledge on

service component, e.g. how to tune the component,
what kinds of fault it could have, how to fix faults.

Self-configuring:

This

component

automatically

configures the environment for the service to execute,
based on the configuration metadata. It may also install

or remove additional software, if necessary.

10/27/2013


47

AUTONOMIC COMPUTING

Logging:

This component records events during

the execution of the service for self-healing. The events
will be annotated with log metadata.

Self-healing:

Once an error occurred to a service,

this component uses the log metadata and healing
strategy metadata to decide how to heal the service.

10/27/2013


48

AUTONOMIC COMPUTING
Monitoring:

This component monitors the execution

of the service and periodically invokes the logging to
record events. It also detects the correct execution of the
service. If something incorrect happens, it will invoke the
self-healing component to heal the service.

Self-protecting:
metadata,

this

Based on the service protection

component

can

authenticate

and

authorize who can use the service.

10/27/2013


49

AUTONOMIC COMPUTING

Self-optimizing:
checks

the

state

This component periodically

information

of

the

service.

If

necessary, it will optimize the service with the
optimization strategy metadata.

10/27/2013


50

9. the semantic grid and autonomic grid

More Related Content

What's hot (20)

Viewers also liked (12)

Similar to 9. the semantic grid and autonomic grid (20)

More from Dr Sandeep Kumar Poonia (20)

Recently uploaded (20)

9. the semantic grid and autonomic grid