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Data Refining and Transforming via Cloud Technology through Local Desktop

Affiliations

  • Saksoft Pvt Ltd., Chennai – 600096, Tamil Nadu, India
  • Department of Computer Science and Engineering, Vignan University, Guntur – 522213, Andhra Pradesh, India

Abstract


Objectives: To fulfil the FeRAM and Cloud infrastructure for the avoidance of external Storage and for IAAS methods. Methods/Statistical Analysis: In this method, we approach with the Eucalyptus Technique and we use node controller to manipulate the exact performance. For interacting with the Device like FeRAM (Ferro Electric RAM) we used Euca2ools for performance statistics. This can be done from the analysis of Cloud Services with the reliable Cloud Controller. Findings: This model deals about the features that can be adopted in the Cloud computing environment along with the Usage of FeRAM's advantages. Our proposal is to adopt all values inside the FeRAM without the usage of Hard Disk. As the storage controller would be having all the patterns to conclude load balancer which simulates our workload along with the scaling listener, it maps with the additionally stored FeRAM. At this point, we would be integration the mechanism involved in the cloud controller and with the FeRAM as this would add more efficient with our works. In future, there will be a need for avoidance of external storage for storing large data. Applications: In this perspective, a novel optimization model has been proposed in which the cloud architecture is redefined with new RAM (FeRAM) and other external resources. Through Internet Connectivity, we make proper authentication and services with secured password protection manner (OTP) or email verification for every login transitions and thus we propose a model OUT_OF_HARDDRIVE method. The resources are provided to the user in the form of connecting bridge as virtual servers and are possibly distributed, running in cloud environment via Internet.

Keywords

Data Security Model, Encryption and Decryption in Cloud Computing, FeRAM, Heterogeneous Workloads.

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