Penalty-aware and cost-efficient resource management in cloud data centers

2016 ◽  
Vol 30 (8) ◽  
pp. e3179 ◽  
Author(s):  
AA Rahmanian ◽  
GH Dastghaibyfard ◽  
H Tahayori
Keyword(s):  
Resource Management ◽  
Data Centers ◽  
Cloud Data ◽  
Efficient Resource ◽  
Cloud Data Centers ◽  
Cost Efficient

Service level agreement based energy-efficient resource management in cloud data centers

2014 ◽  
Vol 40 (5) ◽  
pp. 1621-1633 ◽  
Author(s):  
Yongqiang Gao ◽  
Haibing Guan ◽  
Zhengwei Qi ◽  
Tao Song ◽  
Fei Huan ◽  
...  
Keyword(s):  
Resource Management ◽  
Energy Efficient ◽  
Data Centers ◽  
Service Level Agreement ◽  
Service Level ◽  
Cloud Data ◽  
Efficient Resource ◽  
Cloud Data Centers

scholarly journals An Energy and SLA-Aware Resource Management Strategy in Cloud Data Centers

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Chi Zhang ◽  
Yuxin Wang ◽  
Yuanchen Lv ◽  
Hao Wu ◽  
He Guo
Keyword(s):  
Energy Consumption ◽  
Resource Management ◽  
Management Strategy ◽  
Data Centers ◽  
Minimum Energy ◽  
Service Level Agreement ◽  
Cloud Data ◽  
Cpu Utilization ◽  
Cloud Data Centers ◽  
Detecting Method

Reducing energy consumption of data centers is an important way for cloud providers to improve their investment yield, but they must also ensure that the services delivered meet the various requirements of consumers. In this paper, we propose a resource management strategy to reduce both energy consumption and Service Level Agreement (SLA) violations in cloud data centers. It contains three improved methods for subproblems in dynamic virtual machine (VM) consolidation. For making hosts detection more effective and improving the VM selection results, first, the overloaded hosts detecting method sets a dynamic independent saturation threshold for each host, respectively, which takes the CPU utilization trend into consideration; second, the underutilized hosts detecting method uses multiple factors besides CPU utilization and the Naive Bayesian classifier to calculate the combined weights of hosts in prioritization step; and third, the VM selection method considers both current CPU usage and future growth space of CPU demand of VMs. To evaluate the performance of the proposed strategy, it is simulated in CloudSim and compared with five existing energy–saving strategies using real-world workload traces. The experimental results show that our strategy outperforms others with minimum energy consumption and SLA violation.

Sign in / Sign up

Export Citation Format

Share Document