Ontology and search engine for cloud computing system

Kang, Jaeyong; Sim, Kwang Mong

doi:10.1109/icsse.2011.5961913

Cited by 35 publications

(17 citation statements)

References 6 publications

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“…Firstly, we build our CSOnt manually by following the NIST ‡ Cloud computing reference architecture 32 and a number of ontology-based works that have been published in the Cloud computing field. 14,17,24,33,34 Secondly, we generate more concepts by automatically analyzing the real-world Cloud services to increase the number of concepts in the Cloud service ontology.…”

Section: Cloud Service Ontologymentioning

confidence: 99%

Enhanced semantic similarity measure based on two‐level retrieval model

Djemaa

Nabli

Amous

2019

Concurrency and Computation

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Summary With the rapid spread of services in the Cloud computing environment, it is difficult for users to find the right service. Therefore, the necessity of a search engine with semantic focused crawler becomes a fundamental requirement. However, the huge size and varied functionalities of Cloud services on the Web, together with the lack of standardized and coherent description of services available, have a great effect for crawlers in order to provide effective Cloud services. To solve these issues, we propose a Cloud service discovery crawler that employs a two‐level semantic similarity measure based on both TF‐IDF and LDA models. Moreover, in order to automatically discover and categorize Cloud services, we present a Cloud Service Ontology (CSOnt) that contains a set of concepts defining Cloud service categories. Experimental results show that the proposed method enhances the performance of the focused crawlers and presents an efficient way to parse the Web and collect Web pages relevant to Cloud services.

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Section: Cloud Service Ontologymentioning

confidence: 99%

Enhanced semantic similarity measure based on two‐level retrieval model

Djemaa

Nabli

Amous

2019

Concurrency and Computation

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“…On the other hand, Kang et al [29] and Zhang et al [30] uses ontology for semantic similarity based cloud service search and reasoning. While these approaches aids in understanding of the configuration of cloud services, they do not offer any support for automating the process of migrating web application services to public clouds.…”

Section: Related Workmentioning

confidence: 99%

CloudGenius: A Hybrid Decision Support Method for Automating the Migration of Web Application Clusters to Public Clouds

Menzel

Ranjan

Wang

et al. 2015

IEEE Trans. Comput.

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Abstract-With the increase in cloud service providers, and the increasing number of compute services offered, a migration of information systems to the cloud demands selecting the best mix of compute services and VM (Virtual Machine) images from an abundance of possibilities. Therefore, a migration process for web applications has to automate evaluation and, in doing so, ensure that Quality of Service (QoS) requirements are met, while satisfying conflicting selection criteria like throughput and cost. When selecting compute services for multiple connected software components, web application engineers must consider heterogeneous sets of criteria and complex dependencies across multiple layers, which is impossible to resolve manually. The previously proposed CloudGenius framework has proven its capability to support migrations of single-component web applications. In this paper, we expand on the additional complexity of facilitating migration support for multi-component web applications. In particular, we present an evolutionary migration process for web application clusters distributed over multiple locations, and clearly identify the most important criteria relevant to the selection problem. Moreover, we present a multicriteria-based selection algorithm based on Analytic Hierarchy Process (AHP). Because the solution space grows exponentially, we developed a Genetic Algorithm (GA)-based approach to cope with computational complexities in a growing cloud market. Furthermore, a use case example proofs CloudGenius' applicability. To conduct experiments, we implemented CumulusGenius, a prototype of the selection algorithm and the GA deployable on hadoop clusters. Experiments with CumulusGenius give insights on time complexities and the quality of the GA.

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“…A notable example is Cloudle [7], [8], a multi-criteria Cloud service search engine based on matchmaking. In Cloudle users can identify the Cloud services queried by means of three kinds of requirements: functional requirement (like programming language for PaaS service type), technical requirement (like CPU clock or RAM for IaaS service type) and cost requirement (like max price) as input parameters.…”

Section: Related Workmentioning

confidence: 99%

Using the mOSAIC's semantic engine to design and develop civil engineering cloud applications

Cretella

Martino

Stankovski

2012

Proceedings of the 14th International Conference on Information Integration and Web-Based Applications &Amp; Services

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The development of applications for the Cloud requires programming skills and knowledge about the several programming models, APIs and underlying infrastructures, which are provided by Cloud vendors. The European Project mO-SAIC aims at developing an API, Platform and a set of tools to facilitate language and platform agnostic application development and deployment on a variety of Infrastructures as a Service offers. Within the mOSAIC project, appropriate ontologies, a knowledge base and an associated Semantic Engine [10] have been developed to support the Cloud application developer in the tasks of discovering the needed functionalities and resources for application development through vendor independent representations of such application components, and representation of generic programming concepts and patterns, including application domain related ones. In this paper the use of the Semantic Engine, its ontologies and knowledge base is illustrated by following the design and implementation of an application for analysis of structures under static loading that is based on the Finite Element Method.

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Ontology and search engine for cloud computing system

Cited by 35 publications

References 6 publications

Enhanced semantic similarity measure based on two‐level retrieval model

Enhanced semantic similarity measure based on two‐level retrieval model

CloudGenius: A Hybrid Decision Support Method for Automating the Migration of Web Application Clusters to Public Clouds

Using the mOSAIC's semantic engine to design and develop civil engineering cloud applications

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