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.
Highlights
Deep learning affords new prospects for mobile platform to attain superior performance than earlier
deep convolutional neural network (DCNN) provides feature extraction and feature selection strategy which generates the data sinking event and front events required the event for a file system on local device
Viola et al [8] present a approach for helping drivers This on High Performance Computing (HPC) as well as GPU's approach basically proposed face detection using various respectively.Quick-IK achieve higher energy implementation feature selection, it is an real time application which captures on HPC on environment
Summary
Deep learning affords new prospects for mobile platform to attain superior performance than earlier. The present deep learning reasoning is applied directly while not accuracy loss and no user-related information uploading is required. This work basically proposed dynamic resource allocation as well as resource selection for storage devices to mobile platforms. This work carried out proposed deep convolutional neural network (DCNN) with tensorflow libraries, which provides highly resource utilization and execute the entire task with minimum time complexity. DCNN provides feature extraction and feature selection strategy which generates the data sinking event and front events required the event for a file system on local device
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