Network Aware VM Migration in Cloud Data Centers

2014 ◽  
Author(s):  
Hellen Maziku ◽  
Sachin Shetty
Keyword(s):  
Data Centers ◽  
Vm Migration ◽  
Cloud Data ◽  
Cloud Data Centers

scholarly journals On demand clock synchronization for live VM migration in distributed cloud data centers

2020 ◽  
Vol 138 ◽  
pp. 15-31 ◽  
Author(s):  
Yashwant Singh Patel ◽  
Aditi Page ◽  
Manvi Nagdev ◽  
Anurag Choubey ◽  
Rajiv Misra ◽  
...  
Keyword(s):  
Data Centers ◽  
Clock Synchronization ◽  
Vm Migration ◽  
Cloud Data ◽  
On Demand ◽  
Cloud Data Centers ◽  
Distributed Cloud

Live VM Migration Across Cloud Data Centers

2017 ◽  
Author(s):  
Suhib Bani Melhem ◽  
Anjali Agarwal ◽  
Mustafa Daraghmeh ◽  
Nishith Goel ◽  
Marzia Zaman
Keyword(s):  
Data Centers ◽  
Vm Migration ◽  
Cloud Data ◽  
Cloud Data Centers

Towards Green Cloud Computing an Algorithmic Approach for Energy Minimization in Cloud Data Centers

2019 ◽  
Vol 9 (1) ◽  
pp. 59-81 ◽  
Author(s):  
Jenia Afrin Jeba ◽  
Shanto Roy ◽  
Mahbub Or Rashid ◽  
Syeda Tanjila Atik ◽  
Md Whaiduzzaman
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Dynamic Scheduling ◽  
Random Search ◽  
Power Reduction ◽  
Vm Migration ◽  
Green Cloud Computing ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Green Cloud

The article presents an efficient energy optimization framework based on dynamic resource scheduling for VM migration in cloud data centers. This increasing number of cloud data centers all over the world are consuming a vast amount of power and thus, exhaling a huge amount of CO2 that has a strong negative impact on the environment. Therefore, implementing Green cloud computing by efficient power reduction is a momentous research area. Live Virtual Machine (VM) migration, and server consolidation technology along with appropriate resource allocation of users' tasks, is particularly useful for reducing power consumption in cloud data centers. In this article, the authors propose algorithms which mainly consider live VM migration techniques for power reduction named “Power_reduction” and “VM_migration.” Moreover, the authors implement dynamic scheduling of servers based on sequential search, random search, and a maximum fairness search for convenient allocation and higher utilization of resources. The authors perform simulation work using CloudSim and the Cloudera simulator to evaluate the performance of the proposed algorithms. Results show that the proposed approaches achieve around 30% energy savings than the existing algorithms.

scholarly journals A Novel Physical Machine Overload Detection Algorithm Combined with Quiescing for Dynamic Virtual Machine Consolidation in Cloud Data Centers

2019 ◽  
Vol 17 (3) ◽  
pp. 358-366
Author(s):  
Loiy Alsbatin ◽  
Gürcü Öz ◽  
Ali Ulusoy
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machines ◽  
Energy Performance ◽  
Detection Algorithm ◽  
Vm Migration ◽  
Cloud Data ◽  
Detection Algorithms ◽  
Physical Machine ◽  
Cloud Data Centers

Further growth of computing performance has been started to be limited due to increasing energy consumption of cloud data centers. Therefore, it is important to pay attention to the resource management. Dynamic virtual machines consolidation is a successful approach to improve the utilization of resources and energy efficiency in cloud environments. Consequently, optimizing the online energy-performance trade off directly influences Quality of Service (QoS). In this paper, a novel approach known as Percentage of Overload Time Fraction Threshold (POTFT) is proposed that decides to migrate a Virtual Machine (VM) if the current Overload Time Fraction (OTF) value of Physical Machine (PM) exceeds the defined percentage of maximum allowed OTF value to avoid exceeding the maximum allowed resulting OTF value after a decision of VM migration or during VM migration. The proposed POTFT algorithm is also combined with VM quiescing to maximize the time until migration, while meeting QoS goal. A number of benchmark PM overload detection algorithms is implemented using different parameters to compare with POTFT with and without VM quiescing. We evaluate the algorithms through simulations with real world workload traces and results show that the proposed approaches outperform the benchmark PM overload detection algorithms. The results also show that proposed approaches lead to better time until migration by keeping average resulting OTF values less than allowed values. Moreover, POTFT algorithm with VM quiescing is able to minimize number of migrations according to QoS requirements and meet OTF constraint with a few quiescings.

Towards Green Cloud Computing an Algorithmic Approach for Energy Minimization in Cloud Data Centers

2021 ◽  
pp. 846-872
Author(s):  
Jenia Afrin Jeba ◽  
Shanto Roy ◽  
Mahbub Or Rashid ◽  
Syeda Tanjila Atik ◽  
Md Whaiduzzaman
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Dynamic Scheduling ◽  
Random Search ◽  
Power Reduction ◽  
Vm Migration ◽  
Green Cloud Computing ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
Green Cloud

The article presents an efficient energy optimization framework based on dynamic resource scheduling for VM migration in cloud data centers. This increasing number of cloud data centers all over the world are consuming a vast amount of power and thus, exhaling a huge amount of CO2 that has a strong negative impact on the environment. Therefore, implementing Green cloud computing by efficient power reduction is a momentous research area. Live Virtual Machine (VM) migration, and server consolidation technology along with appropriate resource allocation of users' tasks, is particularly useful for reducing power consumption in cloud data centers. In this article, the authors propose algorithms which mainly consider live VM migration techniques for power reduction named “Power_reduction” and “VM_migration.” Moreover, the authors implement dynamic scheduling of servers based on sequential search, random search, and a maximum fairness search for convenient allocation and higher utilization of resources. The authors perform simulation work using CloudSim and the Cloudera simulator to evaluate the performance of the proposed algorithms. Results show that the proposed approaches achieve around 30% energy savings than the existing algorithms.

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