Guided container selection for data streaming through neural learning in cloud

Autor: Kokila R. Vaishali, D. Usha, L. Natrayan, S. Radha Rammohan, V. R. Niveditha
Rok vydání: 2021
Předmět:
Zdroj: International Journal of System Assurance Engineering and Management.
ISSN: 0976-4348
0975-6809
Popis: In Big data computing domains with a huge network of connected devices involved in various internet and social network concerns mainly for security, integrity, authentication and data privacy. Allocation and efficient usage of containers provided by cloud service providers has huge impact over efficient data processing and data handling. Batch processing method emphasizes huge databases by letting it into the programmable domains and segregate in accordance with their size, reliability, processing speed and required memory space. Whereas, Stream processing involves scrutinizing data promptly before entering into the stream and scrutinizing will be done accordingly. Container selection plays a major role in such processing methodologies and promptly makes the effective resource scheduling possible and efficient in cloud service providing. In our proposed method, the Guided Container Selection (GCS) process eradicates the bottle neck problem by selecting an efficient and optimal container which satisfies all requirements like required size, reliability, processing speed etc. Implementing either batch processing or stream processing to analyze solutions for multiple domain container selection which will be analyzed and resolved through Deep Neural Learning (DNL). The novel DNL method successfully ranks and handles optimal container selection according to the dynamic data involvement and provides efficient solutions for data processing.In future it also helps selecting appropriate containers for similar requirements for cloud service providers and also for its consumers.
Databáze: OpenAIRE
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