Difference between revisions of "Modeling for cloud part1"
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− | <span style="font-size: | + | <span style="font-size:28pt;color:purple">Semantic Modeling<br /><br />for Cloud Computing, Part 2</span><br /><br /> |
− | Amit Sheth and Ajith Ranabahu • <i>Wright State University</i><br /> | + | Amit Sheth and Ajith Ranabahu • <i>Wright State University</i><br /><br /> |
<p style="float:left;width:300px"> | <p style="float:left;width:300px"> | ||
Part 1 of this two-part article discussed | Part 1 of this two-part article discussed |
Revision as of 16:40, 13 July 2010
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.
scr ibd.com/doc/18172802/Cloud-Comput ing
-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