scholarly journals Energy Efficient Multiresource Allocation of Virtual Machine Based on PSO in Cloud Data Center

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
An-ping Xiong ◽  
Chun-xiang Xu
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Data Center ◽  
Energy Efficient ◽  
Heuristic Algorithms ◽  
Fitness Function ◽  
Cloud Data Center ◽  
Allocation Model ◽  
Cloud Data ◽  
Machine Allocation

Presently, massive energy consumption in cloud data center tends to be an escalating threat to the environment. To reduce energy consumption in cloud data center, an energy efficient virtual machine allocation algorithm is proposed in this paper based on a proposed energy efficient multiresource allocation model and the particle swarm optimization (PSO) method. In this algorithm, the fitness function of PSO is defined as the total Euclidean distance to determine the optimal point between resource utilization and energy consumption. This algorithm can avoid falling into local optima which is common in traditional heuristic algorithms. Compared to traditional heuristic algorithms MBFD and MBFH, our algorithm shows significantly energy savings in cloud data center and also makes the utilization of system resources reasonable at the same time.

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.

scholarly journals An Energy-Efficient Task Scheduling Mechanism with Switching On/Sleep Mode of Servers in Virtualized Cloud Data Centers

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Chunxia Yin ◽  
Jian Liu ◽  
Shunfu Jin
Keyword(s):  
Energy Consumption ◽  
Task Scheduling ◽  
Data Center ◽  
Energy Efficient ◽  
Data Centers ◽  
Sleep Mode ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Response Performance

In recent years, the energy consumption of cloud data centers has continued to increase. A large number of servers run at a low utilization rate, which results in a great waste of power. To save more energy in a cloud data center, we propose an energy-efficient task-scheduling mechanism with switching on/sleep mode of servers in the virtualized cloud data center. The key idea is that when the number of idle VMs reaches a specified threshold, the server with the most idle VMs will be switched to sleep mode after migrating all the running tasks to other servers. From the perspective of the total number of tasks and the number of servers in sleep mode in the system, we establish a two-dimensional Markov chain to analyse the proposed energy-efficient mechanism. By using the method of the matrix-geometric solution, we mathematically estimate the energy consumption and the response performance. Both numerical and simulated experiments show that our proposed energy-efficient mechanism can effectively reduce the energy consumption and guarantee the response performance. Finally, by constructing a cost function, the number of VMs hosted on each server is optimized.

Virtual Machine Migrating Algorithm Based on Genetic Algorithm in Cloud Data Center

2014 ◽  
Vol 513-517 ◽  
pp. 2031-2034
Author(s):  
Hui Zhang ◽  
Yong Liu
Keyword(s):  
Genetic Algorithm ◽  
Resource Utilization ◽  
Virtual Machine ◽  
Data Center ◽  
Virtual Machines ◽  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Virtual Machine Migration ◽  
Cloud Data Center ◽  
Cloud Data

Virtual machine migration is an effective method to improve the resource utilization of cloud data center. The common migration methods use heuristic algorithms to allocation virtual machines, the solution results is easy to fall into local optimal solution. Therefore, an algorithm called Migrating algorithm based on Genetic Algorithm (MGA) is introduced in this paper, which roots from genetic evolution theory to achieve global optimal search in the map of virtual machines to target nodes, and improves the objective function of Genetic Algorithm by setting the resource utilization of virtual machine and target node as an input factor into the calculation process. There is a contrast between MGA, Single Threshold (ST) and Double Threshold (DT) through simulation experiments, the results show that the MGA can effectively reduce migrations times and the number of host machine used.

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