Modeling for cloud - Revision history

AjithRanabahu: More changes

2010-08-03T15:34:31Z

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AjithRanabahu: More minor changes

2010-08-03T15:31:45Z

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AjithRanabahu: More changes

2010-08-03T15:27:29Z

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AjithRanabahu: saving version one - still need to read and find any descrepancies

2010-08-03T15:17:00Z

saving version one - still need to read and find any descrepancies

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AjithRanabahu at 14:55, 3 August 2010

2010-08-03T14:55:23Z

AjithRanabahu: First draft

2010-08-03T14:52:01Z

First draft

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AjithRanabahu: Created page with 'cc'

2010-08-03T14:50:48Z

Created page with 'cc'

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@@ Line 67: / Line 67: @@
 Semantic modeling can help with this.<br /><br />
-[[File:Chart4.png|380px]]
+[[File:Chart4.png]]
-−
 <span style="font-size:12pt;color:purple">Cloud Interoperability?</span><br />
@@ Line 147: / Line 146: @@
 must cover.
-[[File:Chart1.png|380px]]
+[[File:Chart1.png]]
 The language abstraction level
@@ Line 289: / Line 288: @@
 software deployment and management. Elastra’s Elastic Computing
 Modeling Language (ECML),
 [[File:Chart2.png]]
 Elastic Deployment Modeling Language

@@ Line 21: / Line 21: @@
 are still far from reality. In this two-part article,
 we discuss how a little bit of semantics can help
-address clouds’ key interoperability and porta-
+address clouds’ key interoperability and portability issues.
-bility issues.
 <span style="font-size:12pt;color:purple">Cloud-Related Challenges</span><br />
@@ Line 58: / Line 57: @@
 porting the code in PaaS and SaaS clouds will
 likely require more effort.
-The second consideration is that data col-
+The second consideration is that data collected for the application might need transformation. Data is the most important asset the
-lected for the application might need transfor-
-mation. Data is the most important asset the
 application generates over time and is essential
 for continued functioning. The transformation
@@ Line 99: / Line 96: @@
 flexible, and more reliable, prompting a transition.
 The following discussion on
-semantic models applies to both ver-
+semantic models applies to both vertical and horizontal interoperability.
-tical and horizontal interoperability.
 The key in addressing both types is
 that many of the core data and services causing them follow the same
@@ Line 141: / Line 137: @@
 We suggest a 3D slicing of the modeling requirements along the following dimensions (see Figure 2).
 The types of semantics that are
-useful for porting or interoperabil-
+useful for porting or interoperability in cloud computing are similar to
-ity in cloud computing are similar to
 those we introduced in 2003 for Web
 services.
@@ Line 322: / Line 317: @@
 schema-less, distributed data stores
 with relaxed consistency models to
-provide high availability and elas-
+provide high availability and elasticity to suit clouds’ needs. However,
-ticity to suit clouds’ needs. However,
 [[File:Chart3.png]]
@@ Line 378: / Line 372: @@
 engines’ use of metadata to display results in customized formats.
 Yahoo’s SearchMonkey and Google
-Rich Snippets are two such microformat-driven schemes. These anno-
+Rich Snippets are two such microformat-driven schemes. These annotations, unlike the DSL annotations
-tations, unlike the DSL annotations
 in Figure 3, might not always point
 to ontologies. Their structure can be
@@ Line 387: / Line 380: @@
 semantic web” movement, which
 emphasizes lightweight models.
-Embedding rich semantic meta-
+Embedding rich semantic meta-data in cloud service descriptions has
 data in cloud service descriptions has
 three main benefits that go beyond
 customized search capabilities.
@@ Line 421: / Line 413: @@
 interim standards.
 The third benefit is that the formalizations apply not only to service descriptions but also to many
-other aspects such as service-level
+other aspects such as service level
 agreements (SLAs) and software
 licenses. You can use annotations
@@ Line 427: / Line 419: @@
 these documents, facilitating more
 automation in the cloud space. For
-example, Web Service-  Level Agreement ([http://www.research.ibm.com/wsla WSLA]) specification provides a
+example, Web Service Level Agreement ([http://www.research.ibm.com/wsla WSLA]) specification provides a
 way to formalize SLAs, but creating
-and maintaining these formalizations is time-consuming.
+and maintaining these formalizations is timeconsuming.
 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 annotations to extract a WSLA equivalent
-of the human-readable SLA.
+of the human readable SLA.
 These benefits’ importance comes
 into perspective when you consider

@@ Line 24: / Line 24: @@
 bility issues.
-<span style="font-size:12pt;color:purple">
+<span style="font-size:12pt;color:purple">Cloud-Related Challenges</span><br />
 Cloud-Related Challenges</span><br />
 Figure 1 shows the three main flavors of clouds
 as outlined by NIST (http://csrc.nist.gov/groups/SNS/cloud-computing).  Infrastructure-as-a-
@@ Line 70: / Line 70: @@
 Semantic modeling can help with this.<br /><br />
-<span style="font-size:12pt;color:purple">
+[[File:Chart4.png|380px]]
 Cloud Interoperability?</span><br />
 First, a word about cloud interoperability is in
 order. Interoperability requirements in the cloud landscape in Figure 1 arise owing to
-two types of heterogeneities.
+two types of heterogeneities.The first is vertical heterogeneity — that is, within a single silo. We
 The first is vertical heterogeneity — that is, within a single silo. We
 can address this by using middleware to homogenize the API and
-sometimes by enforcing standardization. For example, the Open Virtualization Format (OVF; www.dmtf.org/
+sometimes by enforcing standardization. For example, the Open Virtualization Format ([http://www.dmtf.org/vman OVF] ) is an emerging standard that allows migration of virtual-machine
 vman) is an emerging standard that
 allows migration of virtual-machine
 snapshots across IaaS clouds.
 The second type is horizontal
@@ Line 113: / Line 112: @@
 interoperability in the cloud space.
 Some parts of the scientific and
-engineering community weren’t
+engineering community weren’t impressed by early semantic-modeling approaches, especially ones that
-−
-[[File:Chart4.png|380px]]
-−
 impressed by early semantic-modeling approaches, especially ones that
 required large up-front investment.
 This perception, however, is changing rapidly with the influx of new
@@ Line 123: / Line 118: @@
 exploit detailed semantic models to
 provide improved functionality (for
-example, biomedical ontologies cataloged at the US National Center for
+example, biomedical ontologies cataloged at the [http://www.bioontology.org US National Center for Biomedical Ontology] ). Semantic models such
-Biomedical Ontology; www.bioon-
-tology.org). Semantic models such
 as ontologies can formalize more
 details than other traditional modeling techniques. They also enable
@@ Line 131: / Line 124: @@
 rich models can improve traditional
 functions far beyond what was once
-thought possible. For example, Powerset (now part of Microsoft; www.
+thought possible. For example, [http://www.powerset.com Powerset] (now part of Microsoft), a search service
-powerset.com), a search service
 provider, added considerable value
 to search results by incorporating
@@ Line 140: / Line 132: @@
 to more rapidly create domain models, often by mining crowd knowledge represented, for example, in
 Wikipedia1 or shared data, exemplified by the linked-object data cloud.
-The technologies showcased at the
+The technologies showcased at the [http://www.semantic-conference.com Semantic Technology Conference]
-Semantic Technology Conference (www.semant ic-conference.com)
 also provide plenty of evidence of
 semantic-empowered commercial
 and scientific applications.<br /><br />
 <span style="font-size:12pt;color:purple">
-Multidimensional Analysis
+<span style="font-size:12pt;color:purple">Multidimensional Analysis of Cloud-Modeling Requirements</span><br />
 of Cloud-Modeling
 Requirements</span><br />
 We suggest a 3D slicing of the modeling requirements along the following dimensions (see Figure 2).
 The types of semantics that are
@@ Line 155: / Line 145: @@
 those we introduced in 2003 for Web
 services.
 This is natural because the
 primary means of interacting with a
 cloud environment is through Web
@@ Line 161: / Line 151: @@
 different semantic aspects a model
 must cover.
 The language abstraction level
 indicates the modeling’s granularity and specificity. Although ontological modeling is preferable at
@@ Line 169: / Line 162: @@
 explicit annotations. For example,
 although service developers happily
 use Web Services Description Language (WSDL) descriptions in their
 Web services, these descriptions
@@ Line 474: / Line 464: @@
 <span style="font-size:12pt;color:purple">References</span><br />
 <span style="font-size:9pt">
-.  F. Chang et al., “Bigtable: A Distributed
+.  C. Thomas et al., “Growing Fields of
 Storage System for Structured Data,” in Proc. Usenix Symp. Operating Systems
 Design and Implementation, Usenix
 Assoc., 2006, p. 15.
 . M. Nagarajan et al., “Semantic Interoper-
 ability of Web Services—Challenges and
 Experiences,” Proc. 2006 IEEE Int’l Conf.
@@ Line 485: / Line 485: @@
 , p. 373–382.
 .  A.P. Sheth, K. Gomadam, and J. Lathem,
 “SA-REST: Semantically Interoperable
 and Easier-to-Use Services and Mashups,”  IEEE Internet Computing, vol. 11,

@@ Line 2: / Line 2: @@
 <tr>
 <td width="650">
-<span style="font-size:28pt;color:purple">Semantic Modeling<br /><br />for Cloud Computing, Part 1</span><br /><br />
+<span style="font-size:28pt;color:purple">Semantic Modeling<br /><br />for Cloud Computing</span><br /><br />
 Amit Sheth and Ajith Ranabahu • <i>Wright State University</i><br /><br />
 <p style="float:left;width:300px">
@@ Line 282: / Line 282: @@
 particularly applicable.
 The second aspect is data modeling. A crucial
-difficulty developers face is porting data hori-
+difficulty developers face is porting data horizontally across clouds. For example, moving data
-zontally across clouds. For example, moving data
 from a schema-less data store (such as Google
 Bigtable1) to a schema-driven data store such