B. Balamurugan ¹, D. Kamalraj ¹, S. Jegadeeswari ¹, M. Sugumaran ²

Affiliations
1 Bharathiar University, Coimbatore – 641046, Tamil Nadu, IN
2 Pondicherry Engineering College, Pillaichavadi – 605014, Puducherry, IN

Abstract

Cloud computing implements virtualization processing of data service in the internet, where it delivers the conceptual, scalable platforms and applications as on data services. The important problem arises in Cloud infrastructure in storing a very large amount of data and processing on the computational load on the cloud. It is a big challenge to overcome computation complexity on cloud. An effectively predict the data streams process with load factors of ensemble model and data stream are implemented to overcome in cloud. Data stream processes on the cloud infrastructure runs with continuously varying load factors. In this work, we propose an architecture with a load balancing framework for cloud infrastructure by using the Ensemble Tree Metric Space Indexing (E-tree MSI) technique. We developed three techniques to construct our E-tree MSI technique: Fast Predictive Look-ahead Scheduling approach (FPLS) where the scheduling of Spatio-temporal data stream files takes place; Parallel Ensemble Tree Classification (PETC) which performs the process of classification operations on cloud data stream; and a Bilinear quadrilateral Mapping process which adds efficient implementation of cloud infrastructure. We have done an experimental evolution using Cloud Sim, from which it is achieved that the performance of load balancing factor is increased, the accuracy rate of classification is better and it reduced the execution time for mapping.

Keywords

Cloud Security, Data Stream, E-Tree, Load Balancing, Load Factor, Mapping and Metric Space

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S. Jegadeeswari ¹, P. Dinadayalan ², N. Gnanambigai ³

Affiliations
1 Bharathiar University, Coimbatore - 641046, Tamil Nadu, IN
2 Department of Computer Science, Mahatma Gandhi Government Arts College, Mahe - 673311, Kerala, IN
3 Department of Computer Science, Indira Gandhi College of Arts and Science, Puducherry - 605009, Tamil Nadu, IN

Abstract

Neural Network is an efficient implementing technique for cryptographic algorithms to provide security in cloud environment. Cloud Computing is an outsourced on-demand computing service, where Privacy preserving is very difficult to provide. Secured Data Sharing is important in cloud storage aspect. The proposed Neural Data Security Model ensures high data confidentiality and security in cloud database environment. This Model is a combination of , Sensitive Data Component (SDC) and Counter Propagation Neural Data Security Component (CPNDSC). The Sensitive data Component is implemented for storing the fragmented sensitive data. In Neural Data Security Component the Neural cryptographic algorithm is used to encrypt the sensitive data to enhance the confidentiality level by using Counter Propagation Neural Network.This reasearch is carried on cloud databases and artificial Neural Network that achieves high data security in cloud environment.

Keywords

Keywords- Cloud Data Security, Fragmentation, Neural Network, Counter Propagation, Grossberg layer, Data Confidentiality, Kohonen layer.

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