Dynamic Resource Allocation and Memory Management using Deep Convolutional Neural Network
Dipak Raghunath Patil1, Rajesh Purohit2
1Mr. Dipak Raghunath Patil, Department of Computer Science & Engineering & Technology, Suresh Gyan Vihar University, Jagatpura (Jaipur) India.
2Dr. Rajesh Purohit, Department of Computer Science & Engineering & Technology, Suresh Gyan Vihar University, Jagatpura (Jaipur) India.
Manuscript received on November 20, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 608-612 | Volume-9 Issue-2, December, 2019. | Retrieval Number: A9961109119/2020©BEIESP | DOI: 10.35940/ijeat.A9961.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Memory management is very essential task for large-scale storage systems; in mobile platform generate storage errors due to insufficient memory as well as additional task overhead. Many existing systems have illustrated different solution for such issues, like load balancing and load rebalancing. Different unusable applications which are already installed in mobile platform user never access frequently but it allocates some memory space on hard device storage. In the proposed research work we describe dynamic resource allocation for mobile platforms using deep learning approach. In Real world mobile systems users may install different kind of applications which required ad-hoc basis. Such applications may be affect to execution performance of system as well space complexity, sometime they also affect another runnable applications performance. To eliminate of such issues, we carried out an approach to allocate runtime resources for data storage for mobile platform. When system connected with cloud data server it store complete file system on remote Virtual Machine (VM) and whenever a single application required which immediately install beginning as remote server to local device. For developed of proposed system we implemented deep learning base Convolutional Neural Network (CNN), algorithm has used with tensorflow environment which reduces the time complexity for data storage as well as extraction respectively.
Keywords: Deep Learning, Transfer Learning.