Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
February 17, 2026
Neural Networks and Rules-based Systems used to Find Rational and
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Cloud computing play an important role in data intensive application since it provide a consistent performance over time and it provide scalability and good fault tolerant mechanism. Hadoop provide a scalable data intensive map reduce architecture. Hadoop map task are executed on large cluster and consumes lot of energy and resources. Executing these tasks requires lot of resource and energy which are expensive so minimizing the cost and resource is critical for a map reduce application. So here in this paper we propose a new novel efficient cloud structure algorithm for data processing or computation on azure cloud. Here we propose an efficient BSP based dynamic scheduling algorithm for iterative MapReduce for data intensive application on Microsoft azure cloud platform. Our framework can be used on different domain application such as data analysis, medical research, dataminining etc… Here we analyze the performance of our system by using a co-located cashing on the worker role and how it is improving the performance of data intensive application over Hadoop map reduce data intrinsic application. The experimental result shows that our proposed framework properly utilizes cloud infrastructure service (management overheads, bandwith bottleneck) and it is high scalable, fault tolerant and efficient.
Rama Satish K V. 2015. \u201cA New Efficient Cloud Model for Data Intensive Application\u201d. Global Journal of Computer Science and Technology - B: Cloud & Distributed GJCST-B Volume 15 (GJCST Volume 15 Issue B1): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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Neural Networks and Rules-based Systems used to Find Rational and
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Cloud computing play an important role in data intensive application since it provide a consistent performance over time and it provide scalability and good fault tolerant mechanism. Hadoop provide a scalable data intensive map reduce architecture. Hadoop map task are executed on large cluster and consumes lot of energy and resources. Executing these tasks requires lot of resource and energy which are expensive so minimizing the cost and resource is critical for a map reduce application. So here in this paper we propose a new novel efficient cloud structure algorithm for data processing or computation on azure cloud. Here we propose an efficient BSP based dynamic scheduling algorithm for iterative MapReduce for data intensive application on Microsoft azure cloud platform. Our framework can be used on different domain application such as data analysis, medical research, dataminining etc… Here we analyze the performance of our system by using a co-located cashing on the worker role and how it is improving the performance of data intensive application over Hadoop map reduce data intrinsic application. The experimental result shows that our proposed framework properly utilizes cloud infrastructure service (management overheads, bandwith bottleneck) and it is high scalable, fault tolerant and efficient.
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