Smart-DRS: A Strategy of Dynamic Resource Scheduling in Cloud Data Center

2012 ◽  
Author(s):  
Lei Xu ◽  
Wenzhi Chen ◽  
Zonghui Wang ◽  
Shuangquan Yang
Keyword(s):  
Data Center ◽  
Resource Scheduling ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Dynamic Resource

scholarly journals Efficient Resource Utilization Based on Energy Management in Cloud Data Center

2019 ◽  
Vol 8 (6) ◽  
pp. 340-344
Keyword(s):  
Power Consumption ◽  
Resource Utilization ◽  
Data Center ◽  
Vital Role ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Network Bandwidth ◽  
Efficient Resource ◽  
Novel Method ◽  
Dynamic Resource

Efficient resource utilization plays a vital role in cloud computing since the shared computational power of the resources is offered on demand. During dynamic resource allocation sometimes a server may be over utilized or underutilized thus leading to excess of energy consumption in the data centers. So the proposed system calculates the over utilization and underutilization of a CPU and RAM usage and also considers the network bandwidth usage to reduce power consumption in the cloud data center. Hence, a novel method is used for minimizing power consumption in the data center

An Energy-Efficient Resource Scheduling Algorithm for Cloud Computing based on Resource Equivalence Optimization

2016 ◽  
Vol 8 (2) ◽  
pp. 43-57 ◽  
Author(s):  
Li Mao ◽  
De Yu Qi ◽  
Wei Wei Lin ◽  
Bo Liu ◽  
Ye Da Li
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Data Center ◽  
Energy Efficient ◽  
Resource Scheduling ◽  
Constraint Satisfaction Problems ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Efficient Resource

With the rapid growth of energy consumption in global data centers and IT systems, energy optimization has become an important issue to be solved in cloud data center. By introducing heterogeneous energy constraints of heterogeneous physical servers in cloud computing, an energy-efficient resource scheduling model for heterogeneous physical servers based on constraint satisfaction problems is presented. The method of model solving based on resource equivalence optimization is proposed, in which the resources in the same class are pruning treatment when allocating resource so as to reduce the solution space of the resource allocation model and speed up the model solution. Experimental results show that, compared with DynamicPower and MinPM, the proposed algorithm (EqPower) not only improves the performance of resource allocation, but also reduces energy consumption of cloud data center.

P-Aware: a proportional multi-resource scheduling strategy in cloud data center

2016 ◽  
Vol 19 (3) ◽  
pp. 1089-1103 ◽  
Author(s):  
Hang Zhou ◽  
Qing Li ◽  
Weiqin Tong ◽  
Samina Kausar ◽  
Hai Zhu
Keyword(s):  
Data Center ◽  
Resource Scheduling ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Scheduling Strategy

scholarly journals Research on Resource Scheduling of Cloud Computing Based on Improved Genetic Algorithm

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Juanzhi Zhang ◽  
Fuli Xiong ◽  
Zhongxing Duan
Keyword(s):  
Genetic Algorithm ◽  
Data Center ◽  
Completion Time ◽  
Resource Scheduling ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Cloud Data Center ◽  
Two Stage ◽  
Cloud Data ◽  
Conventional Genetic Algorithm

In order to solve the problem that the resource scheduling time of cloud data center is too long, this paper analyzes the two-stage resource scheduling mechanism of cloud data center. Aiming at the minimum task completion time, a mathematical model of resource scheduling in cloud data center is established. The two-stage resource scheduling optimization simulation is realized by using the conventional genetic algorithm. On the technology of the conventional genetic algorithm, an adaptive transformation operator is designed to improve the crossover and mutation of the genetic algorithm. The experimental results show that the improved genetic algorithm can significantly reduce the total completion time of the task, and has good convergence and global optimization ability.

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