Energy-Efficient Task Scheduling Using Quantum-Inspired Genetic Algorithm for Cloud Data Center

2021 ◽  
pp. 467-477
Author(s):  
Santanu Kumar Misra ◽  
Pratyay Kuila
2013 ◽  
Vol 846-847 ◽  
pp. 1468-1471
Author(s):  
De Wen Wang ◽  
Yang Liu

A multi-QoS evaluation model for electric power users is defined, combined with the characteristics of data center in electric power corporation, based on the research of cloud computing platform of data center in electric power corporation and task scheduling strategies of cloud data center. And a genetic algorithm based on multi-QoS, which fitness functions are QoS utility value and completion time, is put forward. Tests in Cloudsim platform and the result shows that the genetic algorithm based on multi-QoS can satisfy the requirements of multi-QoS of electric power users and improve the operating efficiency of data center in electric power corporation.


Author(s):  
Avinab Marahatta ◽  
Sandeep Pirbhulal ◽  
Fa Zhang ◽  
Reza M. Parizi ◽  
Kim-Kwang Raymond Choo ◽  
...  

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Chunxia Yin ◽  
Jian Liu ◽  
Shunfu Jin

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.


2020 ◽  
Vol 14 (12) ◽  
pp. 1942-1948
Author(s):  
Banavath Balaji Naik ◽  
Dhananjay Singh ◽  
Arun B. Samaddar

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