Modeling for cloud part2 - Revision history

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Semantic Modeling

for Cloud Computing, Part 2
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Created page with '<table border="1" width="650" cellpadding="10"> <tr> <td width="650"> Semantic Modeling for Cloud Computing, Part 2<br…'

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Semantic Modeling for Cloud Computing, Part 2 
Amit Sheth and Ajith Ranabahu • Wright State University 

Part 1 of this two-part article discussed
challenges related to cloud computing,
cloud interoperability, and multidimen-
sional analysis of cloud-modeling requirements
(see the May/June issue). Here, we look more
specifically at areas in which semantic models
can support cloud computing. 

Opportunities for Semantic Models in Cloud Computing 
Semantic models are helpful in three aspects of
cloud computing.
The first is functional and nonfunctional
definitions. The ability to define application
functionality and quality-of-service details in
a platform-agnostic manner can immensely
benefit the cloud community. This is particu-
larly important for porting application code
horizontally—that is, across silos. Lightweight
semantics, which we describe in detail later, are
particularly applicable.
The second aspect is data modeling. A crucial
difficulty developers face is porting data hori-
zontally across clouds. For example, moving data
from a schema-less data store (such as Google
Bigtable1) to a schema-driven data store such
as a relational database presents a significant
challenge. For a good overview of this concern,
see the discussion of customer scenarios in the
Cloud Computing User Cases White Paper (www.scribd.com/doc/18172802/Cloud-Computing-Use-Cases-Whitepaper). The root of this dif-
ficulty is the lack of a platform-agnostic data
model. Semantic modeling of data to provide a
platform-independent data representation would
be a major advantage in the cloud space.
The third aspect is service description
enhancement. Clouds expose their operations
via Web services, but these service interfaces
differ between vendors. The operations’ seman-
tics, however, are similar. Metadata added
through annotations pointing to generic opera-
tional models would play a key role in consoli-
dating these APIs and enable interoperability
among the heterogeneous cloud environments. 
Functional Portability 
From a perspective of the cloud landscape
based on the language abstraction and type
of semantics (that is, viewing the cube in Fig-
ure 1 from the top), we see that opportunities
exist to use semantic models to define applica-
tions’ functional aspects in a platform-agnostic
manner. In most cases, however, converting a
high-level model directly to executable arti-
facts pollutes both representations. Intermedi-
ate representations are important to provide a
convenient conversion.
Applying high-level modeling to describe an
application’s functional aspects isn’t new. Many
software development companies have been
using UML to model application functionality
at a high level and use artifacts derived from
these models to drive development. This process
is commonly called model-driven development.
This is an example of using high-level models to
derive fine-grain artifacts. UML models usually
don’t include code, so you can use them only
to generate a skeletal application. That is, low-
level details are deliberately kept away from the
high-level models.
UML models, however, are inherently bound
with object-oriented languages, and UML-
driven development processes depend heavily
on advanced tools (for example, IBM’s Ratio-
nal Rose; www-01.ibm.com/software/awdtools/
developer/rose). This limits UML’s applicability.
A popular alternative to such tool-dependent
heavy upfront models is domain-specific lan-
guages (DSLs). Their popularity is due partly 
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to the availability of extensible
interpreted programming languages
such as Ruby and Python. Unlike
UML, a DSL is applicable only in a
given domain but enables a light-
weight model in that domain, often
without requiring proprietary tools.
For example, you can use IBM’s
Sharable Code DSL (http://services.alphaworks.ibm.com/isc), which is a
mashup generator, with a basic text
editor. (However, providing graphical
abstractions and specialized tooling
would be more convenient for users.)
“Lightweight” signifies that these
models don’t use rich knowledge rep-
resentation languages and so have
limited reasoning capabilities.
Our Cirrocumulus project for
cloud interoperability (http://knoesis.org/research/srl/projects/cirrocumulus) uses DSLs to bridge the
gap between executable artifacts
and high-level semantic models. A
DSL, although domain specific, can
provide a more programmer-oriented
representation of functional, non-
functional, or even data descriptions.
A best-of-both-worlds approach
is to use annotations to link mod-
els, which provides the convenience
of lightweight models while sup-
porting high-level operations when
required. Figure 2 shows an annota-
tion referring to an ontology from
a fictitious DSL script for configu-
ration. The script is more program-
mer-oriented (in fact, it’s derived
from Ruby) but lacks an ontology’s
richness. However, the annota-
tion links the relevant components
between the different levels, pro-
viding a way to facilitate high-level
operations while maintaining a
simpler representation.
From the perspective based on
the type of semantics and software
lifecycle stage—that is, looking at the
cube in Figure 1 from the front—you
can see the modeling coverage for
software deployment and manage-
ment. Elastra’s Elastic Computing
Modeling Language (ECML), Elas- 
[[File:Chart1.png|350px]] [[File:Chart2.png|350px]] 
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<td width="175">tic Deployment Modeling Language
(EDML), and Elastic Management
Modeling Language (EMML) ontolo-
gies cover many system aspects
and some nonfunctional aspects
in all stages. We’re pleased to see
such industry initiative in adopting
semantic technologies. 

Data Modeling 
Another opportunity for semantic </td>
<td width="175">models for clouds lies in Resource
Description Framework (RDF) data
modeling. As we discussed in part
1, a major concern plaguing cloud
computing’s adoption is data lock-
in—that is, the inability to port data
horizontally. Many vendors designed
schema-less, distributed data stores
with relaxed consistency models to
provide high availability and elas-
ticity to suit clouds’ needs. However, </td>
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[[File:Chart3.png|600px]]
exploiting these data stores requires
substantial redesign of many data-
driven applications and often makes
porting data to a traditional rela-
tional database extremely difficult.
The current practice is to address
such transitions case-by-case.
A better approach is to model the
data in RDF and generate the specific
target representations, and in some
cases even the code for the applica-
tion’s data access layer. This method
can formulate transformations from
one representation to another using
the lifting-lowering mechanism.
Semantic Annotations for WSDL
and XML Schema (SAWSDL; www.
w3.org/TR/sawsdl) demonstrated this
mechanism’s use for data mediation.
2
Lightweight modeling in terms of
DSLs also applies here. For example,
the Web services community has
long used XML Schema definitions
as platform-agnostic data definitions.
Schema definitions serve as inputs to
code generation tools that generate
platform-specific data definitions.
From the perspective of the type
of semantics and software lifecycle
stage, most of this data modeling
applies during application develop-
ment. Concrete artifacts generated
from these high-level models would
be used mostly during subsequent
lifecycle stages.
 


Service Enrichment 
One feature differentiating the cloud
from other distributed environments
is the availability of Web services
to manipulate resources. Avail-
ability of the service APIs lets you
programmatically manage the cloud
resources, even from within the
same cloud. These capabilities have
revolutionized application deploy-
ment and management. For example,
you can compose or mash up well-
defined services to facilitate elabo-
rate workflows.
Service definitions are usually
syntactic, and many researchers
have focused on embedding rich
metadata in formal service descrip-
tions. One result of this research
is SAWSDL. A growing trend is
to annotate HTML descriptions to
embed richer, machine-readable
semantic metadata. One reason for
this method’s popularity is search
engines’ use of metadata to dis-
play results in customized formats.
Yahoo’s SearchMonkey and Google
Rich Snippets are two such micro-
format-driven schemes. These anno-
tations, unlike the DSL annotations
in Figure 3, might not always point
to ontologies. Their structure can be
based on a vocabulary or taxonomy—
a lower-grade nonsemantic model.
For example, the popular hCalendar 
microformat is part of the “lowercase
semantic web” movement, which
emphasizes lightweight models.
Embedding rich semantic meta-
data in cloud service descriptions has
three main benefits that go beyond
customized search capabilities.
The first benefit deals with Rep-
resentational State Transfer (REST)
style services. Many cloud service
providers adopt REST-style Web ser-
vices that don’t advocate a formal
service description. These services
are described using HTML pages.
WSDL 2.0, the latest specification,
explicitly supports formal descrip-
tion of “RESTful” services but hasn’t
seen quick adoption. Alternative
approaches such as SA-REST3 (SA
stands for semantic annotation), a
generic annotation scheme that fol-
lows microformat design principles,
are becoming more applicable in
this space. These annotations enable
the seamless, flexible integration of
formalizations into RESTful service
descriptions. This opens the door
to many exciting avenues such as
faceted search to identify relevant
reusable services and semiautomated
service compositions.
The second benefit deals with
handling change. The cloud space is
still evolving. If the history of soft-
ware or component interoperability 
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is any guide, achieving consensus in
the cloud space will be difficult and
won’t likely happen soon. Attaching
formalizations via annotations, how-
ever, is flexible enough to accom-
modate an evolving model. This is
especially attractive to vendors who
aren’t willing to invest heavily in
interim standards.
The third benefit is that the for-
malizations apply not only to ser-
vice descriptions but also to many
other aspects such as service-level
agreements (SLAs) and software
licenses. You can use annotations
to embed formalizations even for
these documents, facilitating more
automation in the cloud space. For
example, Web Service- Level Agree-
ment (WSLA; www.research.ibm.
com/wsla) specification provides a
way to formalize SLAs, but creating
and maintaining these formaliza-
tions is time-consuming.
Figure 3 illustrates using SA-
REST annotations on the Amazon
Elastic Compute Cloud (EC2) SLA
document. It shows how a capable
processor could use these annota-
tions to extract a WSLA equivalent
of the human-readable SLA.
These benefits’ importance comes
into perspective when you consider
the enormous body of research on
standard-driven service compo-
sitions and agreement matching.
The informal, non-standard-driven
nature of many cloud services made
most of the previous research inap-
plicable. However, being able to
glean formalizations from existing
documents opens the doors to apply
many well-researched techniques. 
The cloud space presents many
opportunities for researchers,
and we see a plethora of applica-
tions that use semantic modeling.
Issues such as interoperability and
data portability, which the cloud
community is facing right now, are
the very issues for which semantic 
 
models excel in providing solutions.
However, learning from the past,
we advocate a multilevel modeling
strategy to provide smooth tran-
sitions into different granularity
levels. We also think that DSLs can
play an important role in the cloud
space to provide lightweight model-
ing in an appealing manner to the
software engineering community.
 References 

1. F. Chang et al., “Bigtable: A Distributed 
Storage System for Structured Data,” in 
Proc. Usenix Symp. Operating Systems 
Design and Implementation, Usenix 
Assoc., 2006, p. 15. 
2. M. Nagarajan et al., “Semantic Interoper- 
ability of Web Services—Challenges and 
Experiences,” Proc. 2006 IEEE Int’l Conf. 
Web Services (ICWS 06), IEEE CS Press, 
2006, p. 373–382. 
3. A.P. Sheth, K. Gomadam, and J. Lathem, 
“SA-REST: Semantically Interoperable 
and Easier-to-Use Services and Mash- 
ups,” IEEE Internet Computing, vol. 11, 
no. 6, 2007, pp. 91–94. > 
Amit Sheth is the director of the Ohio Cen- 
ter of Excellence on Knowledge-Enabled 
Computing (Kno.e.sis) at Wright State 
University. He’s also the university’s 
LexisNexis Ohio Eminent Scholar. He’s 
on the Web at http://knoesis.org/amit/. 
Ajith Ranabahu is pursuing a PhD in cloud- 
computing interoperability at Wright 
State University. He worked with IBM 
on Sharable Code and its Altocumulus 
project, and he coordinates the Cirrocu- 
mulus project (http://knoesis.wright.edu/research/srl/projects/cirrocumulus). Con-tact him at ajith@knoesis.org
Selected CS articles and columns
are also available for free at http://ComputingNow.computer.org/
</td>
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← Older revision		Revision as of 15:42, 14 July 2010
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		+	This is the Wiki version of the article published in IEEE Internet Computing July/August 2010 Issue.
		+	[http://www.computer.org/portal/web/csdl/doi/10.1109/MIC.2010.98 External Reference]
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@@ Line 159: / Line 159: @@
 and XML Schema ([http://www.w3.org/TR/sawsdl SAWSDL]) demonstrated this
 mechanism’s use for data mediation.
-−
 Lightweight modeling in terms of
 DSLs also applies here. For example,
@@ Line 221: / Line 221: @@
 explicitly supports formal description of “RESTful” services but hasn’t
 seen quick adoption. Alternative
-approaches such as SA-REST3 (SA
+approaches such as SA-REST(SA
 stands for semantic annotation), a
 generic annotation scheme that follows microformat design principles,
@@ Line 274: / Line 274: @@
 The cloud space presents many
 opportunities for researchers,
-and we see a plethora of applica-
+and we see a plethora of applications that use semantic modeling.
-tions that use semantic modeling.
 Issues such as interoperability and
 data portability, which the cloud

@@ Line 34: / Line 34: @@
 challenge. For a good overview of this concern,
 see the discussion of customer scenarios in the
-[www.scribd.com/doc/18172802/Cloud-Computing-Use-Cases-Whitepaper Cloud Computing User Cases White Paper]. The root of this difficulty is the lack of a platform-agnostic data
+[http://www.scribd.com/doc/18172802/Cloud-Computing-Use-Cases-Whitepaper Cloud Computing User Cases White Paper]. The root of this difficulty is the lack of a platform-agnostic data
 model. Semantic modeling of data to provide a
 platform-independent data representation would
@@ Line 41: / Line 41: @@
 enhancement. Clouds expose their operations
 via Web services, but these service interfaces
-differ between vendors. The operations’ seman-</p>
+differ between vendors. The operations’ </p>
-<p style="float:right;width:300px">tics, however, are similar. Metadata added
+<p style="float:right;width:300px">semantics, however, are similar. Metadata added
 through annotations pointing to generic operational models would play a key role in consolidating these APIs and enable interoperability
 among the heterogeneous cloud environments.<br /><br />
@@ Line 68: / Line 68: @@
 with object-oriented languages, and UML-
 driven development processes depend heavily
-on advanced tools (for example, IBM’s Rational Rose; www-01.ibm.com/software/awdtools/
+on advanced tools (for example, [http://www-01.ibm.com/software/awdtools/developer/rose IBM’s Rational Rose]). This limits UML’s applicability.
 developer/rose). This limits UML’s applicability.
 A popular alternative to such tool-dependent
 heavy upfront models is domain-specific languages (DSLs). Their popularity is due partly </p>
@@ Line 81: / Line 80: @@
 without requiring proprietary tools.
 For example, you can use IBM’s
-Sharable Code DSL (http://services.alphaworks.ibm.com/isc), which is a
+Sharable Code DSL ([http://services.alphaworks.ibm.com/isc ISC]), which is a
 mashup generator, with a basic text
 editor. (However, providing graphical
@@ Line 119: / Line 118: @@
 <td width="175"><p style="float:left">Elastic Deployment Modeling Language
 (EDML), and Elastic Management
-Modeling Language (EMML) ontolo-
+Modeling Language (EMML) ontologies cover many system aspects
-gies cover many system aspects
 and some nonfunctional aspects
 in all stages. We’re pleased to see
@@ Line 132: / Line 130: @@
 modeling. As we discussed in part
 , a major concern plaguing cloud
-computing’s adoption is data lock-
+computing’s adoption is data lock-in , that is, the inability to port data
-in—that is, the inability to port data
 horizontally. Many vendors designed
 schema-less, distributed data stores