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

Efficient dynamic resource allocation method for cloud computing environment

2020 ◽  
Vol 23 (4) ◽  
pp. 2871-2889 ◽  
Author(s):  
Ali Belgacem ◽  
Kadda Beghdad-Bey ◽  
Hassina Nacer ◽  
Sofiane Bouznad
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Dynamic Resource Allocation ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Allocation Method ◽  
Dynamic Resource

scholarly journals Dynamic Resource Allocation for Load Balancing in Fog Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaolong Xu ◽  
Shucun Fu ◽  
Qing Cai ◽  
Wei Tian ◽  
Wenjie Liu ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Load Balancing ◽  
Load Balance ◽  
Fog Computing ◽  
Dynamic Resource Allocation ◽  
Iot Applications ◽  
Computing Paradigm ◽  
Allocation Method ◽  
Dynamic Resource

Fog computing is emerging as a powerful and popular computing paradigm to perform IoT (Internet of Things) applications, which is an extension to the cloud computing paradigm to make it possible to execute the IoT applications in the network of edge. The IoT applications could choose fog or cloud computing nodes for responding to the resource requirements, and load balancing is one of the key factors to achieve resource efficiency and avoid bottlenecks, overload, and low load. However, it is still a challenge to realize the load balance for the computing nodes in the fog environment during the execution of IoT applications. In view of this challenge, a dynamic resource allocation method, named DRAM, for load balancing in fog environment is proposed in this paper. Technically, a system framework for fog computing and the load-balance analysis for various types of computing nodes are presented first. Then, a corresponding resource allocation method in the fog environment is designed through static resource allocation and dynamic service migration to achieve the load balance for the fog computing systems. Experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of DRAM.

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