An Energy-Aware Algorithm for Optimizing Resource Allocation in Software Defined Network

2016 ◽  
Author(s):  
Bing Yu ◽  
Yanni Han ◽  
Xuemin Wen ◽  
Xin Chen ◽  
Zhen Xu
Keyword(s):  
Resource Allocation ◽  
Software Defined Network ◽  
Energy Aware

Constructing Energy-Aware Software-Defined Network Virtualization

10.7125/40.3 ◽  
2015 ◽  
Vol 40 (0) ◽  
pp. 14
Author(s):  
Nam Manh Tran ◽  
Thanh Huu Nguyen ◽  
Van Hong Nguyen ◽  
Long Bao Kim ◽  
Lam Duc Nguyen ◽  
...  
Keyword(s):  
Network Virtualization ◽  
Software Defined Network ◽  
Energy Aware

Energy and Quality Aware Multi-Objective Resource Allocation Algorithm in Cloud

2021 ◽  
pp. 2150052
Author(s):  
Koné Kigninman Désiré ◽  
Eya Dhib ◽  
Nabil Tabbane ◽  
Olivier Asseu
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Search Algorithm ◽  
Fitness Function ◽  
Video Quality ◽  
Minimum Energy ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Energy Aware ◽  
Resource Allocation Algorithm

Cloud gaming has become the new service provisioning prototype that hosts the video games in the cloud and broadcasts the interactive game streaming to the players through the Internet. Here, the cloud must use massive resources for video representation and its streaming when several simultaneous players reach a particular point. Alternatively, various players may have separate necessities on Quality-of Experience, like low delay, high-video quality, etc. The challenging task is providing better service by the fixed cloud resource. Hence, there is a necessity for an energy-aware multi-resource allocation in the cloud. This paper devises a Fractional Rider-Harmony search algorithm (Fractional Rider-HSA) for resource allocation in the cloud. The Fractional Rider-HSA combines fractional calculus, Rider Optimization algorithm (ROA), and HSA. Moreover, the fitness function, like mean opinion score (MOS), gaming experience loss, fairness, energy consumption, and network parameters, is considered to determine the optimal resource allocation. The proposed model produces the maximal MOS of 0.8961, maximal gaming experience loss (QE) of 0.998, maximal fairness of 0.9991, the minimum energy consumption of 0.3109, and minimal delay 0.2266, respectively.

scholarly journals Energy Aware Resource Allocation in Multi-Hop Multimedia Routing via the Smart Edge Device

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 151203-151214 ◽  
Author(s):  
Fadi Al-Turjman ◽  
B. D. Deebak ◽  
Leonardo Mostarda
Keyword(s):  
Resource Allocation ◽  
Energy Aware

Stackelberg Game Approach for Energy-Aware Resource Allocation in Data Centers

2016 ◽  
Vol 27 (12) ◽  
pp. 3646-3658 ◽  
Author(s):  
Bo Yang ◽  
Zhiyong Li ◽  
Shaomiao Chen ◽  
Tao Wang ◽  
Keqin Li
Keyword(s):  
Resource Allocation ◽  
Data Centers ◽  
Stackelberg Game ◽  
Energy Aware
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