Artificial Fish Swarm Load Balancing and Job Migration Task with Overloading Detection in Cloud Computing Environments


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Abstract


Ontologies play an important role in knowledge Management like annotating web resources, web mining and other internet related applications. Since the manual construction of a high quality ontologies are more expensive and take more time to complete the process. So, more number of automatic and semi-automatic ontologies is created in the system and also existing ontology learning provides the best results, but sometimes makes the failure due to process of noise in the text. Noise text is one of the major problems in the ontology learning. Because noise text are could not extracted. It makes the problem in completion of the extraction. For avoiding the noise in the data and providing the quick process, paper introduce the novel concept extraction method. This concept extraction presents an ontology building through the automatic and semi-automatic process. Most of the ontology learning technique developed using the Classifiers, NLP, probabilistic and statistical learning. For the concept extraction it uses the process of statistical learning with the combination of text. To increases the richness and avoid the issue of noise, this paper proposes the method of PROCEOL (Probabilistic Relational of Concept Extraction in Ontology Learning). An experimental result provides the best concept extractions compared to the state of the art method.
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Keywords


Load Balancing, Job migration task, and overloading detection, Cloud computing, Hidden Semi Markov Model (HSMM), Artificial Fish Swarm Algorithm (AFSA).

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References


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