Fine-grained Task Scheduling in Cloud Data Centers Using Simulated-annealing-based Bees Algorithm

2020 ◽  
Author(s):  
Haitao Yuan ◽  
Jing Bi ◽  
MengChu Zhou ◽  
Jia Zhang ◽  
Wei Zhang
Keyword(s):  
Simulated Annealing ◽  
Task Scheduling ◽  
Data Centers ◽  
Bees Algorithm ◽  
Cloud Data ◽  
Fine Grained ◽  
Cloud Data Centers

Energy-Aware Fault-Tolerant Dynamic Task Scheduling Scheme for Virtualized Cloud Data Centers

2018 ◽  
Vol 24 (3) ◽  
pp. 1063-1077 ◽  
Author(s):  
Avinab Marahatta ◽  
Youshi Wang ◽  
Fa Zhang ◽  
Arun Kumar Sangaiah ◽  
Sumarga Kumar Sah Tyagi ◽  
...  
Keyword(s):  
Task Scheduling ◽  
Data Centers ◽  
Fault Tolerant ◽  
Energy Aware ◽  
Cloud Data ◽  
Dynamic Task ◽  
Scheduling Scheme ◽  
Cloud Data Centers ◽  
Dynamic Task Scheduling

Hybrid ant genetic algorithm for efficient task scheduling in cloud data centers

2021 ◽  
Vol 95 ◽  
pp. 107419
Author(s):  
Muhammad Sohaib Ajmal ◽  
Zeshan Iqbal ◽  
Farrukh Zeeshan Khan ◽  
Muneer Ahmad ◽  
Iftikhar Ahmad ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Data Centers ◽  
Cloud Data ◽  
Cloud Data Centers

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.

A fine-grained rule partition algorithm in cloud data centers

2018 ◽  
Vol 113 ◽  
pp. 14-25 ◽  
Author(s):  
Wei Jiang ◽  
Wanchun Jiang ◽  
Weiping Wang ◽  
Haodong Wang ◽  
Yi Pan ◽  
...  
Keyword(s):  
Data Centers ◽  
Cloud Data ◽  
Fine Grained ◽  
Cloud Data Centers
Sign in / Sign up

Export Citation Format

Share Document