Difference between revisions of "Modeling for cloud part1"
(Added the details and external reference) |
|||
Line 1: | Line 1: | ||
+ | This is the Wiki version of the article published in IEEE Internet Computing May/June 2010 Issue. | ||
+ | [http://www.computer.org/portal/web/csdl/doi/10.1109/MIC.2010.77 External Reference] | ||
+ | |||
<table border="1" width="650" cellpadding="10"> | <table border="1" width="650" cellpadding="10"> | ||
<tr> | <tr> |
Revision as of 15:41, 14 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 unlim-
ited, 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 fos-
ter 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 com-
puter 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 Technol-
ogy (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 atten-
tion, 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 heterogene- ity—that is, within a single silo. We can address this by using middle- ware to homogenize the API and sometimes by enforcing standardiza- tion. For example, the Open Virtual- ization 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 ser- vices. 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 opportuni- ties 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, prompt- ing 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 ser- vices causing them follow the same semantic concepts. For example, almost all IaaS clouds follow concep- tually similar workflows when allo- cating resources, although the actual service implementations and tools dif- fer 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’ appli- cability, 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 use- ful 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 model- ing 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 sepa- ration 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 Deploy- ment Modeling Language (EDML),
and Elastic Management Modeling
Language (EMML), all based on OWL
and published by Elastra (www.elas-
tra.com/technology/languages).
However, some aspects of cloud
modeling have received little or no
attention. For example, there’s no
comprehensive higher-level model-
ing in the data and functional spaces.
Lessons learned during large-scale
ontological modeling in the Seman-
tic 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 Cirrocu- mulus project (http://knoesis.wright.edu/ research/srl/projects/cirrocumulus). Con- tact him at ajith.ranabhu@gmail.com. Selected CS articles and columns arealsoavailableforfreeathttp:// ComputingNow.computer.org. |