Hopfield Neural Network - Based Approach for Joint Dynamic Resource Allocation in Heterogeneous Wireless Networks

2006 ◽  
Author(s):  
David Gomez-Barquero ◽  
Daniel Calabuig ◽  
Jose Monserrat ◽  
Nuria Garcia ◽  
Jordi Perez-Romero
Keyword(s):  
Neural Network ◽  
Resource Allocation ◽  
Wireless Networks ◽  
Heterogeneous Wireless Networks ◽  
Hopfield Neural Network ◽  
Dynamic Resource Allocation ◽  
Dynamic Resource

scholarly journals Load-Balancing and QoS Based Dynamic Resource Allocation Method for Smart Gird Fiber-Wireless Networks

2019 ◽  
Vol 28 (6) ◽  
pp. 1234-1243 ◽  
Author(s):  
Siya Xu ◽  
Peng Li ◽  
Feng Qi ◽  
Shaoyong Guo ◽  
Guiping Zhou ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Wireless Networks ◽  
Load Balancing ◽  
Dynamic Resource Allocation ◽  
Allocation Method ◽  
Dynamic Resource ◽  
Smart Gird

scholarly journals Dynamic Resource Allocation and Memory Management using Deep Convolutional Neural Network

2019 ◽  
Vol 9 (2) ◽  
pp. 608-612
Keyword(s):  
Neural Network ◽  
Resource Allocation ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Data Storage ◽  
Memory Management ◽  
Research Work ◽  
Dynamic Resource Allocation ◽  
Mobile Platform ◽  
Dynamic Resource

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.

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