Cost Evaluation of Migrating a Computation Intensive Problem from Clusters to Cloud

Hernández, Sergio; Fabra, Javier; Álvarez, Pedro; Ezpeleta, J.

doi:10.1007/978-3-319-02414-1_7

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…A detailed economic analysis of the annotation process can be found in [51]. All the pending terms were correctly annotated using cloud computing resources.…”

Section: Dealing With Faultsmentioning

confidence: 99%

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Integration of grid, cluster and cloud resources to semantically annotate a large‐sized repository of learning objects

Fabra

Hernández

Otero

et al. 2014

Concurrency and Computation

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The Universia repository is composed of more than 15 million of educational resources. The lack of metadata describing these resources complicates their classification, search and recovery. To overcome this drawback, it was decided to semantically annotate the available educational resources using the ADEGA algorithm. For this objective, we selected the DBpedia, a cross-domain linked data composed of more than 3.77 million 'things' with 400 million 'facts', in order to make sure that the wide range of Universia topics are covered by the ontology. However, this kind of process is extremely expensive from a computational point of view: more than 1600 years of CPU time was estimated to achieve it. In this paper, parallel programming techniques and distributed computing paradigms are combined in order to achieve this semantic annotation in a reasonable time. The cornerstone of this proposal is a resource management and execution framework able to integrate heterogeneous computing resources at our disposal (grid, cluster and cloud resources). As a result, the problem was solved in less than 180 days, demonstrating that it is perfectly feasible to exploit the advantages of these computing models in the field of linked data. ontology. From a semantic point of view, the use of the ADEGA algorithm was very promising in terms of precision and recall. Nevertheless, a preliminary estimation concluded that more than 1640 years of CPU time would be needed to create graphs of depth 3 and 25,000 years for graphs of depth 4 (the semantic richness of a graph is proportional to its depth). Obviously, with these computation requirements, a specific computing resource (a personal computer, for instance) or a limited set of resources cannot solve the semantic annotation process.Distributed computing paradigms (grid or cloud computing, for instance) can help us to make the semantic annotation process viable from a computational point of view. This approach is not totally novel, and, therefore, some interesting practical experiences can be found in the scientific literature [5][6][7]. These experiences present two common features that must be emphasized. On the one hand, they show software applications or tools to annotate semantically Web documents or pages. These applications were programmed to be executed on a set of concrete computing resources, and, therefore, they are highly coupled to their corresponding execution environments and technologies. On the other hand, the described annotation processes are fast from time's perspective. This is due to that small-sized and medium-sized collections of data were semantically annotated and, additionally, each data was annotated by means of a single instance of the selected ontology. Now, we try to take advantage of our knowledge in the field of scientific computing and, unlike previous proposals, provide a solution independent of the underlying computing environment and able to solve large-scale annotation processes. In the Universia problem, the complexity of the annotation process is cau...

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“…A detailed economic analysis of the annotation process can be found in [51]. All the pending terms were correctly annotated using cloud computing resources.…”

Section: Dealing With Faultsmentioning

confidence: 99%

“…Therefore, the suitability of the Amazon cloud to compute consecutive faulty jobs is shown because it allows us to annotate all terms successfully with a small portion of the total cost. A detailed economic analysis of the annotation process can be found in [51].…”

Section: Dealing With Faultsmentioning

confidence: 99%

Integration of grid, cluster and cloud resources to semantically annotate a large‐sized repository of learning objects

Fabra

Hernández

Otero

et al. 2014

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

show abstract

“…The adoption decision depends on decision factors such as the trust into performance, security, data protection and an appropriate pricing schema by providers (Kett et al 2012;Hernández et al 2015). The cloud adoption must also be supported by open markets, enabling negotiation processes for handling ambiguous selection criteria (Pittl et al 2017;Slawik et al 2016;Filiopoulou et al 2017).…”

Section: Sustainability and Feasibility Of Cloud Infrastructuresmentioning

confidence: 99%

“…For an infrastructure or service to be successful, it is necessary to evaluate its feasibility and sustainability (Baig et al 2018;Hernández et al 2015;Kim et al 2015). Beyond technical aspects, it requires governance tools, ways to keep customers, and support for creating innovative business models and value chains (Pittl et al 2017;Mohammed et al 2009).…”

Section: Sustainability and Feasibility Of Cloud Infrastructuresmentioning

confidence: 99%