Difference between revisions of "Modeling for cloud part1"
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− | ===Semantic Modeling for Cloud Computing=== | + | This is the Wiki version of the article published in IEEE Internet Computing May/June 2010 Issue. |
− | Amit Sheth and Ajith Ranabahu | + | [http://www.computer.org/portal/web/csdl/doi/10.1109/MIC.2010.77 External Reference] |
+ | |||
+ | <table border="1" width="650" cellpadding="10"> | ||
+ | <tr> | ||
+ | <td width="650"> | ||
+ | <span style="font-size:28pt;color:purple">Semantic Modeling<br /><br />for Cloud Computing, Part 1</span><br /><br /> | ||
+ | Amit Sheth and Ajith Ranabahu • <i>Wright State University</i><br /><br /> | ||
+ | <p style="float:left;width:300px"> | ||
+ | <span style="font-size:16pt;color:purple"> | ||
+ | C</span>loud computing has lately become the | ||
+ | attention grabber in both academia and | ||
+ | industry. The promise of seemingly unlimited, readily available utility-type computing | ||
+ | has opened many doors previously considered | ||
+ | difficult, if not impossible, to open. The cloud- | ||
+ | computing landscape, however, is still evolving, | ||
+ | and we must overcome many challenges to foster widespread adoption of clouds. | ||
+ | The main challenge is interoperability. | ||
+ | Numerous vendors have introduced paradigms | ||
+ | and services, making the cloud landscape | ||
+ | diverse and heterogeneous. Just as in the computer hardware industry’s early days, when each | ||
+ | vendor made and marketed its own version of | ||
+ | (incompatible) computer equipment, clouds are | ||
+ | diverse and vendor-locked. Although many | ||
+ | efforts are under way to standardize clouds’ | ||
+ | important technical aspects, notably from the | ||
+ | US National Institute of Standards and Technology (NIST), consolidation and standardization | ||
+ | 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- | ||
+ | bility issues.<br /><br /> | ||
+ | <span style="font-size:12pt;color:purple"> | ||
+ | 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- | ||
+ | service (IaaS) clouds have the largest gap (a | ||
+ | high workload but little automation) in terms of | ||
+ | deploying and managing an application. Platform-as-a-service (PaaS) or software-as-a-service | ||
+ | (SaaS) clouds have substantially lower workloads, | ||
+ | but at the expense of flexibility and portability. | ||
+ | Given this diverse environment, a cloud service consumer faces four challenges. | ||
+ | First, depending on the application’s requirements, legal issues, and other possible consid</p> | ||
+ | <p style="float:right;width:300px">erations, the consumer must select a cloud to | ||
+ | use. Each cloud vendor exposes these details in | ||
+ | different formats and at different granularity | ||
+ | levels. | ||
+ | Second, the consumer must learn about the | ||
+ | vendor’s technical aspects (service interface, | ||
+ | scaling configuration, and so on) and workflow. | ||
+ | Third, the consumer must then develop an | ||
+ | application or customize the vendor-provided | ||
+ | multitenant application to fulfill his or her | ||
+ | requirements. When doing this, the consumer | ||
+ | must take into account various technical details | ||
+ | such as the choice of programming language | ||
+ | and limitations in the application runtime, | ||
+ | which will all be vendor-specific. | ||
+ | Finally, after deploying the application, if | ||
+ | the consumer must change the service provider | ||
+ | (which happens surprisingly often), at least two | ||
+ | major considerations arise. First, the consumer | ||
+ | might need to rewrite or modify the application | ||
+ | code to suit the new provider’s environment. For | ||
+ | some clouds (such as IaaS), this is minimal, but | ||
+ | porting the code in PaaS and SaaS clouds will | ||
+ | likely require more effort. | ||
+ | The second consideration is that data col- | ||
+ | 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 | ||
+ | might even need to carry across different data | ||
+ | models. The industry practice is to address such | ||
+ | transformations case-by-case. | ||
+ | To overcome these challenges and provide | ||
+ | better insight into the aspects requiring attention, proper modeling in this space is essential. | ||
+ | Semantic modeling can help with this.<br /><br /> | ||
+ | <span style="font-size:12pt;color:purple"> | ||
+ | Cloud Interoperability?</span><br /> | ||
+ | First, a word about cloud interoperability is in | ||
+ | order. Interoperability requirements in the cloud </p> | ||
+ | </td></tr> | ||
+ | <tr><td> | ||
+ | <p style="float:left;width:200px">landscape in Figure 1 arise owing to | ||
+ | two types of heterogeneities. | ||
+ | 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/ | ||
+ | vman) is an emerging standard that | ||
+ | allows migration of virtual-machine | ||
+ | snapshots across IaaS clouds. | ||
+ | The second type is horizontal | ||
+ | heterogeneity—that is, across silos. | ||
+ | Overcoming this is fundamentally | ||
+ | more difficult. Each silo provides | ||
+ | different abstraction levels and services. High-level modeling pays off, | ||
+ | especially when you must move an | ||
+ | application and code horizontally | ||
+ | across these silos. | ||
+ | Surprisingly, many small and | ||
+ | medium businesses make horizontal | ||
+ | transitions. PaaS clouds offer faster | ||
+ | setup for applications, and many | ||
+ | exploit the free hosting opportunities of some platform cloud providers | ||
+ | (for example, Google’s App Engine). | ||
+ | When the application grows in scope | ||
+ | and criticality, however, an IaaS | ||
+ | cloud might prove cheaper, more | ||
+ | flexible, and more reliable, prompting a transition. | ||
+ | The following discussion on | ||
+ | semantic models applies to both ver- | ||
+ | tical and horizontal interoperability. | ||
+ | The key in addressing both types is | ||
+ | that many of the core data and services causing them follow the same | ||
+ | semantic concepts. For example, | ||
+ | almost all IaaS clouds follow conceptually similar workflows when allocating resources, although the actual | ||
+ | service implementations and tools differ significantly. Similarly, the PaaS | ||
+ | modeling space is a subset of that for | ||
+ | IaaS, from a semantic perspective. | ||
+ | These observations prompt us to | ||
+ | argue for semantic models’ applicability, especially to supplement | ||
+ | interoperability in the cloud space. | ||
+ | Some parts of the scientific and | ||
+ | engineering community weren’t </p> | ||
+ | <p style="float:right;width:400px">[[File:Chart4.png|380px]]<br /><br /> | ||
+ | <table width="400" border="0" cellpadding="5"> | ||
+ | <tr> | ||
+ | <td width="200"><p style="float:left">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 | ||
+ | applications and technologies that | ||
+ | exploit detailed semantic models to | ||
+ | provide improved functionality (for | ||
+ | example, biomedical ontologies cataloged at the US National Center for | ||
+ | Biomedical Ontology; www.bioon- | ||
+ | tology.org). Semantic models such | ||
+ | as ontologies can formalize more | ||
+ | details than other traditional modeling techniques. They also enable | ||
+ | reasoning, a way to make inferences and gain new knowledge. Such | ||
+ | rich models can improve traditional | ||
+ | functions far beyond what was once | ||
+ | thought possible. For example, Powerset (now part of Microsoft; www. | ||
+ | powerset.com), a search service | ||
+ | provider, added considerable value | ||
+ | to search results by incorporating | ||
+ | semantic models and thus enabling | ||
+ | fact discovery. These capabilities are | ||
+ | being complemented by the ability | ||
+ | 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 | ||
+ | Semantic Technology Conference </p></td> | ||
+ | <td width="200"><p style="float:left">(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 | ||
+ | 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 | ||
+ | useful for porting or interoperabil- | ||
+ | ity in cloud computing are similar to | ||
+ | those we introduced in 2003 for Web | ||
+ | services. | ||
+ | 2 This is natural because the | ||
+ | primary means of interacting with a | ||
+ | cloud environment is through Web | ||
+ | services. The four types of semantics—data, functional, nonfunctional, and system—are based on the | ||
+ | different semantic aspects a model | ||
+ | must cover. | ||
+ | The language abstraction level | ||
+ | indicates the modeling’s granularity and specificity. Although ontological modeling is preferable at | ||
+ | a higher level, developers prefer | ||
+ | detailed, concrete syntactic representations. These representations | ||
+ | of different granularities might | ||
+ | need to be related, often through | ||
+ | explicit annotations. For example, | ||
+ | although service developers happily </p></td> | ||
+ | </tr> | ||
+ | </table> | ||
+ | </p> | ||
+ | </td> | ||
+ | </tr> | ||
+ | <tr> | ||
+ | <td> | ||
+ | [[File:Chart1.png|380px]] | ||
+ | <p style="float:left;width:200px">use Web Services Description Lan- | ||
+ | guage (WSDL) descriptions in their | ||
+ | Web services, these descriptions | ||
+ | are syntactic and can’t provide useful semantic details. To overcome | ||
+ | this deficiency, SAWSDL (Semantic | ||
+ | Annotations for WSDL and XML | ||
+ | Schema; www.w3.org/TR/sawsdl) | ||
+ | attaches semantic-model details to | ||
+ | WSDL documents. | ||
+ | The software lifecycle stage is | ||
+ | important in determining the modeling requirements. For example, some | ||
+ | nonfunctional and system require- | ||
+ | ments might not be modeled during | ||
+ | development but will be taken into | ||
+ | account only during deployment. A | ||
+ | different team handles each of these | ||
+ | lifecycle stages; this separation is | ||
+ | important so that one team doesn’t | ||
+ | step on another’s toes. This separation aims to focus the modeling | ||
+ | effort on the correct time and people. | ||
+ | Some cloud models fall under | ||
+ | the nonfunctional/system/ ontology | ||
+ | space in Figure 2. Such models include | ||
+ | the Elastic Computing Modeling | ||
+ | Language (ECML), Elastic Deployment Modeling Language (EDML), </p> | ||
+ | <p style="float:left;width:10px"> </p> | ||
+ | <p style="float:left;width:200px"> | ||
+ | and Elastic Management Modeling | ||
+ | Language (EMML), all based on OWL | ||
+ | and published by Elastra ([http://www.elastra.com/technology/languages Elastra Languages]). | ||
+ | However, some aspects of cloud | ||
+ | modeling have received little or no | ||
+ | attention. For example, there’s no | ||
+ | comprehensive higher-level modeling in the data and functional spaces. | ||
+ | Lessons learned during large-scale | ||
+ | ontological modeling in the Semantic Web and Semantic Web services, | ||
+ | biology, and many other domains are | ||
+ | readily applicable here and would | ||
+ | help address some of the challenges | ||
+ | in the cloud space.<br /> | ||
+ | <span style="font-size:16pt;color:purple"> | ||
+ | I</span>n part 2, we’ll look at opportunities | ||
+ | for semantic models in cloud computing. Particular areas in which | ||
+ | these models can help are functional | ||
+ | portability, data modeling, and ser- | ||
+ | vice enrichment. <br /><br /> | ||
+ | <span style="font-size:12pt;color:purple"> | ||
+ | References</span><br /> | ||
+ | 1. C. Thomas et al., “Growing Fields of | ||
+ | Interest—Using an Expand and Reduce | ||
+ | Strategy for Domain Model Extraction,”</p> | ||
+ | <p style="width:200px;float:right">Proc. 2008 Int’l Conf. Web Intelligence | ||
+ | and Intelligent Agent Technology (WI- | ||
+ | IAT 08), vol. 1, IEEE CS Press, 2008, pp. | ||
+ | 496–502. | ||
+ | 2. K. Sivashanmugam et al., “Adding | ||
+ | Semantics to Web Services Standards,” | ||
+ | Proc.Int’lConf.WebServices (ICWS 03), | ||
+ | CSREA Press, 2003, pp. 395–401. | ||
+ | Amit Sheth is the director of Kno.e.sis—the | ||
+ | Center of Excellence on Knowledge- | ||
+ | Enabled Human-Centered Computing at | ||
+ | Wright State University. He’s also the | ||
+ | university’s LexisNexis Ohio Eminent | ||
+ | Scholar and an IEEE Fellow. He’s on the | ||
+ | Web at [http://knoesis.org/amit 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 [http://knoesis.wright.edu/research/srl/projects/cirrocumulus Cirrocumulus project]. Contact him at ajith.ranabahu@gmail.com. | ||
+ | </p> | ||
+ | </td> | ||
+ | </tr> | ||
+ | </table> |
Latest revision as of 15:43, 30 July 2010
This is the Wiki version of the article published in IEEE Internet Computing May/June 2010 Issue. External Reference
Semantic Modeling
Cloud computing has lately become the
attention grabber in both academia and
industry. The promise of seemingly unlimited, readily available utility-type computing
has opened many doors previously considered
difficult, if not impossible, to open. The cloud-
computing landscape, however, is still evolving,
and we must overcome many challenges to foster widespread adoption of clouds.
The main challenge is interoperability.
Numerous vendors have introduced paradigms
and services, making the cloud landscape
diverse and heterogeneous. Just as in the computer hardware industry’s early days, when each
vendor made and marketed its own version of
(incompatible) computer equipment, clouds are
diverse and vendor-locked. Although many
efforts are under way to standardize clouds’
important technical aspects, notably from the
US National Institute of Standards and Technology (NIST), consolidation and standardization
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-
bility issues. erations, the consumer must select a cloud to
use. Each cloud vendor exposes these details in
different formats and at different granularity
levels.
Second, the consumer must learn about the
vendor’s technical aspects (service interface,
scaling configuration, and so on) and workflow.
Third, the consumer must then develop an
application or customize the vendor-provided
multitenant application to fulfill his or her
requirements. When doing this, the consumer
must take into account various technical details
such as the choice of programming language
and limitations in the application runtime,
which will all be vendor-specific.
Finally, after deploying the application, if
the consumer must change the service provider
(which happens surprisingly often), at least two
major considerations arise. First, the consumer
might need to rewrite or modify the application
code to suit the new provider’s environment. For
some clouds (such as IaaS), this is minimal, but
porting the code in PaaS and SaaS clouds will
likely require more effort.
The second consideration is that data col-
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
might even need to carry across different data
models. The industry practice is to address such
transformations case-by-case.
To overcome these challenges and provide
better insight into the aspects requiring attention, proper modeling in this space is essential.
Semantic modeling can help with this. | ||
landscape in Figure 1 arise owing to two types of heterogeneities. 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/ vman) is an emerging standard that allows migration of virtual-machine snapshots across IaaS clouds. The second type is horizontal heterogeneity—that is, across silos. Overcoming this is fundamentally more difficult. Each silo provides different abstraction levels and services. High-level modeling pays off, especially when you must move an application and code horizontally across these silos. Surprisingly, many small and medium businesses make horizontal transitions. PaaS clouds offer faster setup for applications, and many exploit the free hosting opportunities of some platform cloud providers (for example, Google’s App Engine). When the application grows in scope and criticality, however, an IaaS cloud might prove cheaper, more flexible, and more reliable, prompting a transition. The following discussion on semantic models applies to both ver- tical and horizontal interoperability. The key in addressing both types is that many of the core data and services causing them follow the same semantic concepts. For example, almost all IaaS clouds follow conceptually similar workflows when allocating resources, although the actual service implementations and tools differ significantly. Similarly, the PaaS modeling space is a subset of that for IaaS, from a semantic perspective. These observations prompt us to argue for semantic models’ applicability, especially to supplement interoperability in the cloud space. Some parts of the scientific and engineering community weren’t
|
||
use Web Services Description Lan- guage (WSDL) descriptions in their Web services, these descriptions are syntactic and can’t provide useful semantic details. To overcome this deficiency, SAWSDL (Semantic Annotations for WSDL and XML Schema; www.w3.org/TR/sawsdl) attaches semantic-model details to WSDL documents. The software lifecycle stage is important in determining the modeling requirements. For example, some nonfunctional and system require- ments might not be modeled during development but will be taken into account only during deployment. A different team handles each of these lifecycle stages; this separation is important so that one team doesn’t step on another’s toes. This separation aims to focus the modeling effort on the correct time and people. Some cloud models fall under the nonfunctional/system/ ontology space in Figure 2. Such models include the Elastic Computing Modeling Language (ECML), Elastic Deployment Modeling Language (EDML),
and Elastic Management Modeling
Language (EMML), all based on OWL
and published by Elastra (Elastra Languages).
However, some aspects of cloud
modeling have received little or no
attention. For example, there’s no
comprehensive higher-level modeling in the data and functional spaces.
Lessons learned during large-scale
ontological modeling in the Semantic Web and Semantic Web services,
biology, and many other domains are
readily applicable here and would
help address some of the challenges
in the cloud space. Proc. 2008 Int’l Conf. Web Intelligence and Intelligent Agent Technology (WI- IAT 08), vol. 1, IEEE CS Press, 2008, pp. 496–502. 2. K. Sivashanmugam et al., “Adding Semantics to Web Services Standards,” Proc.Int’lConf.WebServices (ICWS 03), CSREA Press, 2003, pp. 395–401. Amit Sheth is the director of Kno.e.sis—the Center of Excellence on Knowledge- Enabled Human-Centered Computing at Wright State University. He’s also the university’s LexisNexis Ohio Eminent Scholar and an IEEE Fellow. 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 Cirrocumulus project. Contact him at ajith.ranabahu@gmail.com. |