Difference between revisions of "Modeling for cloud part1"
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Amit Sheth and Ajith Ranabahu • <i>Wright State University</i><br /> | Amit Sheth and Ajith Ranabahu • <i>Wright State University</i><br /> | ||
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Revision as of 16:20, 13 July 2010
Semantic Modeling for Cloud Computing
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-