TARNN: Task‐aware autonomic resource management using neural networks in cloud environment

2019 ◽  
Author(s):  
N. Sujaudeen ◽  
T.T. Mirnalinee
Keyword(s):  
Neural Networks ◽  
Resource Management ◽  
Cloud Environment ◽  
Autonomic Resource Management

Resource Management Framework Using Deep Neural Networks in Multi-Cloud Environment

2021 ◽  
pp. 89-104
Author(s):  
S. Brilly Sangeetha ◽  
R. Sabitha ◽  
B. Dhiyanesh ◽  
G. Kiruthiga ◽  
N. Yuvaraj ◽  
...  
Keyword(s):  
Neural Networks ◽  
Resource Management ◽  
Deep Neural Networks ◽  
Cloud Environment ◽  
Management Framework ◽  
Multi Cloud

Human-Computer Cloud and Its Applications in E-Tourism

2021 ◽  
pp. 1143-1176
Author(s):  
Andrew Ponomarev ◽  
Nikolay Shilov
Keyword(s):  
Information Processing ◽  
Decision Support ◽  
Resource Management ◽  
Ad Hoc ◽  
Dynamic Environments ◽  
Resource Provisioning ◽  
Cloud Environment ◽  
On Demand ◽  
Building Information ◽  
Developing Area

The chapter addresses two problems that typically arise during the creation of decision support systems that include humans in the information processing workflow, namely, resource management and complexity of decision support in dynamic environments, where it is impossible (or impractical) to implement all possible information processing workflows that can be useful for a decision-maker. The chapter proposes the concept of human-computer cloud, providing typical cloud features (elasticity, on demand resource provisioning) to the applications that require human input (so-called human-based applications) and, on top of resource management functionality, a facility for building information processing workflows for ad hoc tasks in an automated way. The chapter discusses main concepts lying behind the proposed cloud environment, as well as its architecture and some implementation details. It is also shown how the proposed human-computer cloud environment solves information and decision support demands in the dynamic and actively developing area of e-tourism.

scholarly journals Framework for Slice-Aware Radio Resource Management Utilizing Artificial Neural Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 174972-174987
Author(s):  
Behnam Khodapanah ◽  
Ahmad Awada ◽  
Ingo Viering ◽  
Andre Noll Barreto ◽  
Meryem Simsek ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Resource Management ◽  
Radio Resource Management ◽  
Radio Resource ◽  
Artificial Neural
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